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 Arguments
In view of the appeal brief filed on 03/02/2026, PROSECUTION IS HEREBY REOPENED. New grounds of rejection are set forth below.
To avoid abandonment of the application, appellant must exercise one of the following two options:
(1) file a reply under 37 CFR 1.111 (if this Office action is non-final) or a reply under 37 CFR 1.113 (if this Office action is final); or,
(2) initiate a new appeal by filing a notice of appeal under 37 CFR 41.31 followed by an appeal brief under 37 CFR 41.37. The previously paid notice of appeal fee and appeal brief fee can be applied to the new appeal. If, however, the appeal fees set forth in 37 CFR 41.20 have been increased since they were previously paid, then appellant must pay the difference between the increased fees and the amount previously paid.
A Supervisory Patent Examiner (SPE) has approved of reopening prosecution by signing below:
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
Regarding the rejection of claims under 35 U.S.C. 103:
Regarding claim 1 and analogous claims 16-17, Applicant argues that Bhattacharyya does not teach the limitations for which it is cited in the previous rejection. In particular, Applicant argues that the method disclosed in the present specification works differently than the method of Bhattacharyya:
“Thus, actually, unlike the references, according to the claim, no special sensor
is required to detect any condition of the vial by the tag. Furthermore, the condition at
the tag can only be determined after it transmits a plurality of frequency words, e.g.,
collected over a period of time, unlike what happens with an actual sensor.
Bhat appears to require a specific special sensor.” (pg. 8)
“Again, note that the transmission frequency calibration at the tag that is called for in the claim is a procedure performed at the tag in order that it properly transmit and this calibration is performed at the tag to compensate for the effects of the environment in the vicinity of the tag, e.g., as caused by conditions at the vial where the tag is located. Each adjustment required by the calibration process for a transmission is digitized into a frequency word.” (pg. 9)
“It should also be appreciated that the tag of Bhat is a single-use tag, meaning that the transmitted frequency will always be the same until the threshold temperature is exceeded, at which point the frequency of transmission will change permanently […] This is a very different approach from that of the invention as claimed, where the frequency words can change and also change back to indicate different conditions which can be detected by the classifying. The device as claimed need not be a single use device.” (pg. 13)
Examiner respectfully disagrees. At issue is not whether the method disclosed in the present specification and the reference differ, but whether the reference teaches the broadest reasonable interpretation of the claim language. Claim 1 reads in relevant part:
“wherein each tag is configured to transmit a plurality of frequency words, wherein each frequency word is derived from a calibration of a transmission frequency of the tag;
extracting at least one data feature from the plurality of frequency words, wherein each data feature changes in response to a change in a state of a vaccine vial”
As explained in previous rejections, Bhattacharyya teaches these limitations. In Bhattacharyya, the transmission frequency on which the tag is responsive changes from a standard value in the presence of a certain temperature fluctuation (Bhattacharyya, Section III, paragraph 2, “For a given reader tag separation, we determine the threshold transmitted power at which the reader just starts detecting the tag sensor and observe the frequency channels on which the tag is responding. If the tag is in State 1, we would expect responses on the 920-925 MHz channels. However, if the tag is in State 2, responses would be in the 907-912 MHz range. A change in channel responses thus enable us to detect which state the sensor is currently in.”). The range of frequencies used for transmission in state 1 can be read as “a calibration of a transmission frequency of the tag”; the determination of a difference between the transmission frequencies in state 2 and state 1 can therefore be read as “frequency word […] derived from a calibration of a transmission frequency of the tag”; and because this change is induced by a temperature change, observing this change in frequencies reads as “extracting at least one data feature from the plurality of frequency words, wherein each data feature changes in response to a change in a state of a vaccine vial.” The Bhattacharyya reference therefore teaches the method recited in these limitations.
Applicant further argues that “frequency word” is a “coined term” explicitly defined in the specification and therefore the use of that term in the claim requires the claim to be read only as describing the methods of the specification; Applicant quotes specification paragraphs 0034-0035 in support.
Examiner respectfully disagrees. Examiner finds that the specification provides no explicit definition of “frequency word,” but merely describes the term in the context of an embodiment (“In an embodiment, a data packet transmitted by a gateway 1 20-I (i=1 , ... , 5) includes a digital frequency word and an Identifier (ID) of an loT tag 110. The frequency word is measured by an loT tag 110 depending on a frequency calibration of an loT tag 110,” paragraph 0034). In the absence of an explicit definition for “frequency word,” Examiner must apply the plain language of the claims (“Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim. For example, a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment,” MPEP 2111.01 (II)).
The arguments regarding the Bhattacharyya reference as applied to claim 1 are therefore found unpersuasive.
Regarding the arguments directed towards the limitations in claim 1 for which the Thangavelu reference was previously relied upon, the arguments are persuasive and new grounds of rejection are given below.
Regarding the rejection of claims 4–7 and 20–23, new grounds of rejection are given below. However, for clarity of record, Examiner notes Applicant’s arguments regarding the meaning of “trace temperature” in claims 4–7 and 20–23:
“Second, Bhat cannot trace temperature of the vaccine vials because it is limited to a one-time use as to whether a certain temperature was exceeded. Tracing of the vials involves considerably more, as explained in at least paragraphs 42 and 64 of the instant specification, but certainly to keep track of the temperature as it changes over time and such cannot be done by the single-use tags of Bhat.” (pg. 18)
Examiner notes that the specification provides no explicit definition of the term “trace,” and absent such a definition, Examiner must rely upon the plain language and not import limitations from the specification. The plain meaning of “trace” in the context of the claims (e.g., “the machine learning model is trained to trace temperature of the vaccine vials at deep freeze temperature range” in claim 4) could encompass any temperature processing by the model, including, but not limited to, processing a temperature of each of multiple vials, or processing the temperature of a vial, as in Bhattacharyya, twice, before and after a detected temperature shift. The wording of claims 4–7 and 20–23 does not require that the temperature determination mechanism be capable of determining an arbitrary series of readings of different temperatures of the same tag over a period of time.
Regarding the rejection of claims 8 and 24 by the combination of Bhattacharyya and Thangavelu, Applicant’s arguments are persuasive and new grounds of rejection are given below.
Regarding the rejection of claims 12 and 28 by the combination of Bhattacharyya and Thangavelu, Applicant’s arguments are persuasive and new grounds of rejection are given below.
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.
Claims 1–3, 7, 9–10, 14–19, 23, 25–26, and 30–31 rejected under 35 U.S.C. 103 as unpatentable over Bhattacharyya et al., “RFID Tag Antenna Based Temperature Sensing in the Frequency Domain,” 2011 IEEE Conference on RFID, 2011 IEEE International Conference on RFID, doi: 10.1109/RFID.2011.5764639 (hereafter Bhattacharyya) in view of Kiemele et al., US Pre-Grant Publication No. 2019/0220697 (hereafter Kiemele).
Regarding claim 1:
Bhattacharyya teaches:
“A method for tracing vaccine vials, comprising”: Bhattacharyya, Section I, paragraph 4, “We also present a design and design methodology [A method] for an UHF RFID tag antenna that shifts its optimum operating frequency within the 902-928 MHz band when a temperature threshold is exceeded.”
(bold only) “receiving, from a gateway of a plurality of gateways, frequency words from tags attached to vaccine vials“: Bhattacharyya, Section III, paragraph 2, “Both Za and Zc are functions of the operating frequency of the reader antenna [receiving, from a gateway]. By causing Za to change with violation of a temperature threshold, we seek to manipulate the frequencies at which τ is close to 1. We therefore intend to design a narrow band RFID tag antenna where there is an appreciable shift in the optimal matching frequencies when a critical temperature threshold is violated. Furthermore, we constrain this frequency shift completely in the 902-928 MHz band of American RFID operations. Specifically, we design our sensor to have the following states: State 1: τ is designed to be near 1 for 920-925 MHz and sharply decreases for all other frequencies when the temperature is below a given threshold. State 2: If the temperature exceeds the threshold, the optimal frequencies over which τ is near 1 shifts to the 907-912 MHz range and sharply decreases for all other frequencies. If we design a tag antenna that exhibits these properties then we are presented with a frequency domain based state detection mechanism that can be implemented on commercially available RFID readers in the Americas: For a given reader tag separation, we determine the threshold transmitted power at which the reader just starts detecting the tag sensor and observe the frequency channels on which the tag is responding [frequency words from tags]. If the tag is in State 1, we would expect responses on the 920-925 MHz channels. However, if the tag is in State 2, responses would be in the 907-912 MHz range. A change in channel responses thus enable us to detect which state the sensor is currently in”; Bhattacharyya, Section IV. A, paragraph 2, “The sensor is initialized by freezing a metal plate in aqueous solution behind the tag A in Fig. 3 and placing it on the item passing through the cold supply chain [tags attached to vaccine vials, vaccine vial interpreted as an item capable of storing a vaccine].”
“wherein each tag is configured to transmit a plurality of frequency words, wherein each frequency word is derived from a calibration of a transmission frequency of the tag”: Bhattacharyya, Section III, paragraph 2, “Both Za and Zc are functions of the operating frequency of the reader antenna. By causing Za to change with violation of a temperature threshold, we seek to manipulate the frequencies at which τ is close to 1. We therefore intend to design a narrow band RFID tag antenna where there is an appreciable shift in the optimal matching frequencies when a critical temperature threshold is violated. Furthermore, we constrain this frequency shift completely in the 902-928 MHz band of American RFID operations. Specifically, we design our sensor to have the following states: State 1: τ is designed to be near 1 for 920-925 MHz and sharply decreases for all other frequencies when the temperature is below a given threshold. State 2: If the temperature exceeds the threshold, the optimal frequencies over which τ is near 1 shifts to the 907-912 MHz range and sharply decreases for all other frequencies [the information content of the transmission (frequency word) is indicated by a divergence from a base transmission frequency, hence, derived from a calibration of a transmission frequency of the tag]. If we design a tag antenna that exhibits these properties then we are presented with a frequency domain based state detection mechanism that can be implemented on commercially available RFID readers in the Americas: For a given reader tag separation, we determine the threshold transmitted power at which the reader just starts detecting the tag sensor and observe the frequency channels on which the tag is responding. If the tag is in State 1, we would expect responses on the 920-925 MHz channels. However, if the tag is in State 2, responses would be in the 907-912 MHz range. A change in channel responses thus enable us to detect which state the sensor is currently in.”
“extracting at least one data feature from the plurality of frequency words, wherein each data feature changes in response to a change in a state of a vaccine vial”: Bhattacharyya, Section III, paragraph 2, “Both Za and Zc are functions of the operating frequency of the reader antenna. By causing Za to change with violation of a temperature threshold, we seek to manipulate the frequencies at which τ is close to 1. We therefore intend to design a narrow band RFID tag antenna where there is an appreciable shift in the optimal matching frequencies when a critical temperature threshold is violated [extracting at least one data feature from the plurality of frequency words, wherein each data feature changes in response to a change in a state of a vaccine vial]. Furthermore, we constrain this frequency shift completely in the 902-928 MHz band of American RFID operations. Specifically, we design our sensor to have the following states: State 1: τ is designed to be near 1 for 920-925 MHz and sharply decreases for all other frequencies when the temperature is below a given threshold. State 2: If the temperature exceeds the threshold, the optimal frequencies over which τ is near 1 shifts to the 907-912 MHz range and sharply decreases for all other frequencies. If we design a tag antenna that exhibits these properties then we are presented with a frequency domain based state detection mechanism that can be implemented on commercially available RFID readers in the Americas: For a given reader tag separation, we determine the threshold transmitted power at which the reader just starts detecting the tag sensor and observe the frequency channels on which the tag is responding. If the tag is in State 1, we would expect responses on the 920-925 MHz channels. However, if the tag is in State 2, responses would be in the 907-912 MHz range. A change in channel responses thus enable us to detect which state the sensor is currently in.”
(bold only) “classifying the extracted data feature based on a machine learning model trained with respect to a location of the gateway, wherein the classifier is trained to label a trace parameter indicative of a state of a vaccine vial”: Bhattacharyya, Section III, paragraph 2, “Both Za and Zc are functions of the operating frequency of the reader antenna. By causing Za to change with violation of a temperature threshold, we seek to manipulate the frequencies at which τ is close to 1. We therefore intend to design a narrow band RFID tag antenna where there is an appreciable shift in the optimal matching frequencies when a critical temperature threshold is violated. Furthermore, we constrain this frequency shift completely in the 902-928 MHz band of American RFID operations. Specifically, we design our sensor to have the following states: State 1: τ is designed to be near 1 for 920-925 MHz and sharply decreases for all other frequencies when the temperature is below a given threshold. State 2: If the temperature exceeds the threshold, the optimal frequencies over which τ is near 1 shifts to the 907-912 MHz range and sharply decreases for all other frequencies. If we design a tag antenna that exhibits these properties then we are presented with a frequency domain based state detection mechanism that can be implemented on commercially available RFID readers in the Americas: For a given reader tag separation, we determine the threshold transmitted power at which the reader just starts detecting the tag sensor and observe the frequency channels on which the tag is responding. If the tag is in State 1, we would expect responses on the 920-925 MHz channels. However, if the tag is in State 2, responses would be in the 907-912 MHz range. A change in channel responses thus enable us to detect which state the sensor is currently in [classifying the extracted data feature …, wherein the classifier … label a trace parameter indicative of a state of a vaccine vial].”
“sending a semantic event indicating a value of the trace parameter”: Bhattacharyya, Section III, paragraph 2, “Both Za and Zc are functions of the operating frequency of the reader antenna. By causing Za to change with violation of a temperature threshold, we seek to manipulate the frequencies at which τ is close to 1. We therefore intend to design a narrow band RFID tag antenna where there is an appreciable shift in the optimal matching frequencies when a critical temperature threshold is violated. Furthermore, we constrain this frequency shift completely in the 902-928 MHz band of American RFID operations. Specifically, we design our sensor to have the following states: State 1: τ is designed to be near 1 for 920-925 MHz and sharply decreases for all other frequencies when the temperature is below a given threshold. State 2: If the temperature exceeds the threshold, the optimal frequencies over which τ is near 1 shifts to the 907-912 MHz range and sharply decreases for all other frequencies. If we design a tag antenna that exhibits these properties then we are presented with a frequency domain based state detection mechanism that can be implemented on commercially available RFID readers in the Americas: For a given reader tag separation, we determine the threshold transmitted power at which the reader just starts detecting the tag sensor and observe the frequency channels on which the tag is responding. If the tag is in State 1, we would expect responses on the 920-925 MHz channels. However, if the tag is in State 2, responses would be in the 907-912 MHz range. A change in channel responses thus enable us to detect which state the sensor is currently in [sending a semantic event indicating a value of the trace parameter, interpreted as signaling the occurrence of an event based on the trace parameter].”
Bhattacharyya does not explicitly teach:
(bold only) “receiving, from a gateway of a plurality of gateways, frequency words from tags attached to vaccine vials”
(bold only) “classifying the extracted data feature based on a machine learning model trained with respect to a location of the gateway, wherein the classifier is trained to label a trace parameter indicative of a state of a vaccine vial”
Kiemele teaches (bold only) “receiving, from a gateway of a plurality of gateways, frequency words from tags attached to vaccine vials” and (bold only) “classifying the extracted data feature based on a machine learning model trained with respect to a location of the gateway, wherein the classifier is trained to label a trace parameter indicative of a state of a vaccine vial”: Kiemele, paragraph 0061, “Training data obtained from the training data repository, including location-specific training data 372, is used by an ML model training component 380 (which may be referred to as an ‘ML model training module’) to produce a location-specific model 382, much as described in FIG. 1 [based on a machine learning model trained with respect to a location of the gateway]. The newly obtained location-specific training data 370, as well as other location-specific training data included in the training data repository 372 from previous training, may be preferentially weighted. In some examples, multiple different location-specific training models may be generated [a plurality of gateways]. In some implementations, the ML model training component 380 is configured to generate a location-specific training model 382 that, in combination with other computing activities performed by the sensor device 310, can achieve real-time performance. This may affect a selection of an ML algorithm or an ML model size. Accordingly, where sensor device 310 provides fairly low computational resources, such as an Internet of Things (IoT) device [gateway], a less computationally intensive ML algorithm, such as a decision tree or random decision forest, may be selected over a deep learning algorithm such as a convolution neural network.”
Kiemele and Bhattacharyya are analogous arts as they are both related to processing sensor data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the location-specific models of Kiemele with the teachings of Bhattacharyya to arrive at the present invention, in order to provide better sensor processing performance in varied locations and environments, as stated in Kiemele, paragraph 0002, “However, such devices can be placed in environments, or experience changes in their environment, where an ML model effectiveness is reduced. Furthermore, it can be difficult both to identify the occurrence of such situations (particularly in situations where resulting from intermittent changes in the environment) and to obtain a new ML model that is more effective. The development of an improved ML model training process is necessary to establish more reliable ML models in these and other types of situations.”
Regarding claim 2:
Bhattacharyya as modified by Kiemele teaches “The method of claim 1.”
Bhattacharyya further teaches “wherein each data feature is any of: a change between two consecutive frequency words and a rate of reception of frequency words”: Bhattacharyya, Section III, paragraph 2, “Both Za and Zc are functions of the operating frequency of the reader antenna. By causing Za to change with violation of a temperature threshold, we seek to manipulate the frequencies at which τ is close to 1. We therefore intend to design a narrow band RFID tag antenna where there is an appreciable shift in the optimal matching frequencies when a critical temperature threshold is violated. Furthermore, we constrain this frequency shift completely in the 902-928 MHz band of American RFID operations. Specifically, we design our sensor to have the following states: State 1: τ is designed to be near 1 for 920-925 MHz and sharply decreases for all other frequencies when the temperature is below a given threshold. State 2: If the temperature exceeds the threshold, the optimal frequencies over which τ is near 1 shifts to the 907-912 MHz range and sharply decreases for all other frequencies. If we design a tag antenna that exhibits these properties then we are presented with a frequency domain based state detection mechanism that can be implemented on commercially available RFID readers in the Americas: For a given reader tag separation, we determine the threshold transmitted power at which the reader just starts detecting the tag sensor and observe the frequency channels on which the tag is responding. If the tag is in State 1, we would expect responses on the 920-925 MHz channels. However, if the tag is in State 2, responses would be in the 907-912 MHz range. A change in channel responses thus enable us to detect which state the sensor is currently in [a change between two consecutive frequency words].”
Regarding claim 3:
Bhattacharyya as modified by Kiemele teaches “The method of claim 1.”
Bhattacharyya further teaches “wherein the state of the vaccine vial is any of: a temperature of a vaccine vial and a number of doses in the vaccine vial”: Bhattacharyya, Section III, paragraph 2, “Both Za and Zc are functions of the operating frequency of the reader antenna. By causing Za to change with violation of a temperature threshold, we seek to manipulate the frequencies at which τ is close to 1. We therefore intend to design a narrow band RFID tag antenna where there is an appreciable shift in the optimal matching frequencies when a critical temperature threshold is violated. Furthermore, we constrain this frequency shift completely in the 902-928 MHz band of American RFID operations. Specifically, we design our sensor to have the following states: State 1: τ is designed to be near 1 for 920-925 MHz and sharply decreases for all other frequencies when the temperature is below a given threshold. State 2: If the temperature exceeds the threshold, the optimal frequencies over which τ is near 1 shifts to the 907-912 MHz range and sharply decreases for all other frequencies [a temperature of a vaccine vial]. If we design a tag antenna that exhibits these properties then we are presented with a frequency domain based state detection mechanism that can be implemented on commercially available RFID readers in the Americas: For a given reader tag separation, we determine the threshold transmitted power at which the reader just starts detecting the tag sensor and observe the frequency channels on which the tag is responding. If the tag is in State 1, we would expect responses on the 920-925 MHz channels. However, if the tag is in State 2, responses would be in the 907-912 MHz range. A change in channel responses thus enable us to detect which state the sensor is currently in.”
Regarding claim 7:
Bhattacharyya as modified by Kiemele teaches “The method of claim 1.”
Kiemele further teaches (bold only) “wherein the gateway is located in an administration station, and the machine learning model is trained to trace temperature of a number of doses in each vaccine vial”: Kiemele, paragraph 0061, “Training data obtained from the training data repository, including location-specific training data 372, is used by an ML model training component 380 (which may be referred to as an ‘ML model training module’) to produce a location-specific model 382 [wherein the gateway is located, interpreted as location-specific modelling, in an administration station, interpreted as a location in which a model traces temperatures], much as described in FIG. 1 [the machine learning model is trained]. The newly obtained location-specific training data 370, as well as other location-specific training data included in the training data repository 372 from previous training, may be preferentially weighted. In some examples, multiple different location-specific training models may be generated. In some implementations, the ML model training component 380 is configured to generate a location-specific training model 382 that, in combination with other computing activities performed by the sensor device 310, can achieve real-time performance. This may affect a selection of an ML algorithm or an ML model size. Accordingly, where sensor device 310 provides fairly low computational resources, such as an Internet of Things (IoT) device, a less computationally intensive ML algorithm, such as a decision tree or random decision forest, may be selected over a deep learning algorithm such as a convolution neural network”; Kiemele, paragraph 0021, “For purposes of this disclosure, a ‘sensor type’ refers to a particular modality that a physical sensor is designed to operate in and/or receive or detect information about. For example, some broad modalities may include, but are not limited to, audio, light, haptic, flow rate, distance, pressure, motion, chemical, barometric, humidity, and temperature [the machine learning model is trained to trace temperature of a number of doses in each vaccine vial]. Within each of these modalities there is a wide array of specific modalities, or sensor types, as well as a wide array of sensor data types reflecting sensor measurements.”
Kiemele and Bhattacharyya are combinable for the rationale given under claim 1.
Regarding claim 9:
Bhattacharyya as modified by Kiemele teaches “The method of claim 1.”
Bhattacharyya further teaches “wherein the transmission frequency is a frequency used for a channel of a low energy transmission protocol”: Bhattacharyya, Section III, paragraph 2, “Both Za and Zc are functions of the operating frequency of the reader. By causing Za to change with violation of a temperature threshold, we seek to manipulate the frequencies at which τ is close to 1. We therefore intend to design a narrow band RFID tag antenna where there is an appreciable shift in the optimal matching frequencies when a critical temperature threshold is violated. Furthermore, we constrain this frequency shift completely in the 902-928 MHz band of American RFID operations. Specifically, we design our sensor to have the following states: State 1: τ is designed to be near 1 for 920-925 MHz and sharply decreases for all other frequencies when the temperature is below a given threshold. State 2: If the temperature exceeds the threshold, the optimal frequencies over which τ is near 1 shifts to the 907-912 MHz range and sharply decreases for all other frequencies. If we design a tag antenna that exhibits these properties then we are presented with a frequency domain based state detection mechanism that can be implemented on commercially available RFID readers in the Americas: For a given reader tag separation, we determine the threshold transmitted power at which the reader just starts detecting the tag sensor and observe the frequency channels on which the tag is responding [the transmission frequency is a frequency used for a channel]. If the tag is in State 1, we would expect responses on the 920-925 MHz channels. However, if the tag is in State 2, responses would be in the 907-912 MHz range. A change in channel responses thus enable us to detect which state the sensor is currently in”; Bhattacharyya, Abstract, “This shift is detectable by commercial UHF RFID readers operating in the 902-928 MHz frequency band. We will illustrate how state change information is preserved using a nonelectric memory mechanism that works even in the absence of reader transmitted power [hence, using a low energy transmission protocol].”
Regarding claim 10:
Bhattacharyya as modified by Kiemele teaches “The method of claim 1.”
Bhattacharyya further teaches “wherein a frequency word changes when the tag is out of transmission frequency calibration”: Bhattacharyya, Section III, paragraph 2, “Both Za and Zc are functions of the operating frequency of the reader. By causing Za to change with violation of a temperature threshold, we seek to manipulate the frequencies at which τ is close to 1. We therefore intend to design a narrow band RFID tag antenna where there is an appreciable shift in the optimal matching frequencies when a critical temperature threshold is violated. Furthermore, we constrain this frequency shift completely in the 902-928 MHz band of American RFID operations. Specifically, we design our sensor to have the following states: State 1: τ is designed to be near 1 for 920-925 MHz and sharply decreases for all other frequencies when the temperature is below a given threshold. State 2: If the temperature exceeds the threshold, the optimal frequencies over which τ is near 1 shifts to the 907-912 MHz range and sharply decreases for all other frequencies [wherein a frequency word changes when the tag is out of transmission frequency calibration]. If we design a tag antenna that exhibits these properties then we are presented with a frequency domain based state detection mechanism that can be implemented on commercially available RFID readers in the Americas: For a given reader tag separation, we determine the threshold transmitted power at which the reader just starts detecting the tag sensor and observe the frequency channels on which the tag is responding. If the tag is in State 1, we would expect responses on the 920-925 MHz channels. However, if the tag is in State 2, responses would be in the 907-912 MHz range. A change in channel responses thus enable us to detect which state the sensor is currently in”; Bhattacharyya, Abstract, “This shift is detectable by commercial UHF RFID readers operating in the 902-928 MHz frequency band. We will illustrate how state change information is preserved using a nonelectric memory mechanism that works even in the absence of reader transmitted power.”
Regarding claim 14:
Bhattacharyya as modified by Kiemele teaches “The method of claim 1.”
Bhattacharyya further teaches (bold only) “wherein the frequency words are transmitted over a Bluetooth Low Energy (BLE) protocol”: Bhattacharyya, Section III, paragraph 2, “Both Za and Zc are functions of the operating frequency of the reader antenna. By causing Za to change with violation of a temperature threshold, we seek to manipulate the frequencies at which τ is close to 1. We therefore intend to design a narrow band RFID tag antenna where there is an appreciable shift in the optimal matching frequencies when a critical temperature threshold is violated. Furthermore, we constrain this frequency shift completely in the 902-928 MHz band of American RFID operations. Specifically, we design our sensor to have the following states: State 1: τ is designed to be near 1 for 920-925 MHz and sharply decreases for all other frequencies when the temperature is below a given threshold. State 2: If the temperature exceeds the threshold, the optimal frequencies over which τ is near 1 shifts to the 907-912 MHz range and sharply decreases for all other frequencies [frequency word]. If we design a tag antenna that exhibits these properties then we are presented with a frequency domain based state detection mechanism that can be implemented on commercially available RFID readers in the Americas: For a given reader tag separation, we determine the threshold transmitted power at which the reader just starts detecting the tag sensor and observe the frequency channels on which the tag is responding. If the tag is in State 1, we would expect responses on the 920-925 MHz channels. However, if the tag is in State 2, responses would be in the 907-912 MHz range. A change in channel responses thus enable us to detect which state the sensor is currently in.”
Kiemele further teaches (bold only) “wherein the frequency words are transmitted over a Bluetooth Low Energy (BLE) protocol”: Kiemele, paragraph 0020, “As noted above, the use of low-cost physical sensors in the identification of events of interest (and individual instances of those events) is associated with many challenges. […] For purposes of this description, a ‘low-cost sensor’ or ‘economical sensor’ refers to a physical sensor that can be employed with substantially lower energy consumption [low energy] or power, emitting substantially less heat, is substantially smaller in one or more dimensions and/or volume, requires substantially less maintenance, does not require specific operating conditions, produces sensor data requiring substantially less processing, and/or has a substantially lower monetary cost for manufacture and/or operation in comparison to another high-cost physical sensor”; Kiemele, paragraph 0106, “The communication components 1264 may include, for example, components adapted to provide wired communication, wireless communication, cellular communication, Near Field Communication (NFC), Bluetooth communication [transmitted over a Bluetooth Low Energy (BLE) protocol], Wi-Fi, and/or communication via other modalities. The device(s) 1280 may include other machines or various peripheral devices (for example, coupled via USB).”
Kiemele and Bhattacharyya are analogous arts as they are both related to processing sensor data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the low-energy wireless communications of Kiemele with the teachings of Bhattacharyya to arrive at the present invention, in order to reduce system cost and improve reliability, as stated in Kiemele, paragraph 0020, “For purposes of this description, a ‘low-cost sensor’ or ‘economical sensor’ refers to a physical sensor that can be employed with substantially lower energy consumption or power, emitting substantially less heat, is substantially smaller in one or more dimensions and/or volume, requires substantially less maintenance, does not require specific operating conditions, produces sensor data requiring substantially less processing, and/or has a substantially lower monetary cost for manufacture and/or operation in comparison to another high-cost physical sensor.”
Regarding claim 15:
Bhattacharyya as modified by Kiemele teaches “The method of claim 1.”
Bhattacharyya further teaches (bold only) “wherein tag is a battery-less internet of things (loT) tag in a form factor of a label attached to a vaccine vial”: Section 1, paragraphs 2-3, “Existing solutions typically rely on RFID tags which feature a battery and a dedicated temperature sensor [5] [6]. More recently, researchers have proposed sensors which use the RFID tag antenna as a temperature sensor [7] to reduce the cost of the sensor device by using conventional UHF RFID tags. Temperature violations are detected as changes in the received signal strength from the RFID tag [wherein tag is a battery-less internet of things (loT) tag in a form factor of a label attached to a vaccine vial].”
Regarding claim 16:
This claim recites “A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising” a series of steps analogous to the method of claim 1, which are taught by Bhattacharyya as modified by Kiemele.
Kiemele further teaches “A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising”: Kiemele, paragraph 0103, “As used herein, ‘machine-readable medium’ refers to a device able to temporarily or permanently store instructions and data that cause machine 1200 to operate in a specific fashion [computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process], and may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical storage media [non-transitory], magnetic storage media and devices, cache memory, network accessible or cloud storage, other types of storage and/or any suitable combination thereof.”
Kiemele and Bhattacharyya are analogous arts as they are both related to sensor devices. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the instruction storage of Kiemele with the teachings of Bhattacharyya to arrive at the present invention, in order to persistently store the method instructions.
Regarding claim 17:
This claim recites “A system for developing a treatment plan using multi-stage machine learning, comprising: a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to” perform a series of steps analogous to the method of claim 1, which are taught by Bhattacharyya as modified by Kiemele.
Kiemele teaches “system for developing a treatment plan using multi-stage machine learning, comprising: a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to”: Kiemele, paragraph 0101, “The machine [system] 1200 may include processors 1210 [processing circuitry], memory [memory] 1230, and I/O components 1250, which may be communicatively coupled via, for example, a bus 1202. The bus 1202 may include multiple buses coupling various elements of machine 1200 via various bus technologies and protocols. In an example, the processors 1210 (including, for example, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an ASIC, or a suitable combination thereof) may include one or more processors 1212a to 1212n that may execute the instructions 1216 [the memory containing instructions that, when executed by the processing circuitry, configure the system to] and process data.”
Kiemele and Bhattacharyya are analogous arts as they are both related to sensor devices. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the computing system of Kiemele with the teachings of Bhattacharyya to arrive at the present invention, in order to explicitly execute the steps via computing hardware.
Regarding claims 18–19, 23, 25–26, and 30–31:
These claims are analogous to claims 2–3, 7, 9–10, and 14–15, respectively, and are rejected by the same reasoning.
Claims 4–6 and 20–22 rejected under 35 U.S.C. 103 as unpatentable over Bhattacharyya as modified by Kiemele in view of Chakraborty et al., US Pre-Grant Publication No. 2020/0213354 (hereafter Chakraborty).
Regarding claim 4:
Bhattacharyya as modified by Kiemele teaches “The method of claim 1.”
Kiemele further teaches (bold only) “wherein the gateway is located in a deep freeze station, and the machine learning model is trained to trace temperature of the vaccine vials at deep freeze temperature range”: Kiemele, paragraph 0061, “Training data obtained from the training data repository, including location-specific training data 372, is used by an ML model training component 380 (which may be referred to as an ‘ML model training module’) to produce a location-specific model [wherein the gateway is located, interpreted as location-specific modelling] 382, much as described in FIG. 1 [the machine learning model is trained]. The newly obtained location-specific training data 370, as well as other location-specific training data included in the training data repository 372 from previous training, may be preferentially weighted. In some examples, multiple different location-specific training models may be generated. In some implementations, the ML model training component 380 is configured to generate a location-specific training model 382 that, in combination with other computing activities performed by the sensor device 310, can achieve real-time performance. This may affect a selection of an ML algorithm or an ML model size. Accordingly, where sensor device 310 provides fairly low computational resources, such as an Internet of Things (IoT) device, a less computationally intensive ML algorithm, such as a decision tree or random decision forest, may be selected over a deep learning algorithm such as a convolution neural network”; Kiemele, paragraph 0021, “For purposes of this disclosure, a ‘sensor type’ refers to a particular modality that a physical sensor is designed to operate in and/or receive or detect information about. For example, some broad modalities may include, but are not limited to, audio, light, haptic, flow rate, distance, pressure, motion, chemical, barometric, humidity, and temperature [trained to trace temperature of the vaccine vials]. Within each of these modalities there is a wide array of specific modalities, or sensor types, as well as a wide array of sensor data types reflecting sensor measurements.”
Kiemele and Bhattacharyya are combinable for the rationale given under claim 1.
Bhattacharyya as modified by Kiemele does not explicitly teach (bold only) “wherein the gateway is located in a deep freeze station, and the machine learning model is trained to trace temperature of the vaccine vials at deep freeze temperature range.”
Chakraborty teaches (bold only) “wherein the gateway is located in a deep freeze station, and the machine learning model is trained to trace temperature of the vaccine vials at deep freeze temperature range”: Chakraborty, paragraph 0084, “Temperature ranges might be used. For instance, a device 102 might perform training in block 1010 if the temperature is below freezing, within a range from freezing to 80 F, and also higher than 80 F. If a current temperature is below freezing, one discriminator model D 110 would be used [at deep freeze temperature range][in a deep freeze station, deep freeze station interpreted as a location containing a model tracing temperatures at deep freeze temperature range]; if a current temperature is above freezing but below 80 F, another discriminator model D 110 is used; and if the temperature is above 80 F, a third discriminator model D 110 might be used. Note also that very high temperatures could be used, such as above 1 50 F, in case an IoT device has direct sun exposure. These might also be automatic, such that if an IoT device is placed into service at 65 F, training would be performed at that temperature. If the temperature varies by a certain amount (e.g., 30 F) from there, training would be performed at the new temperature, and therefore two, three, or more temperature ranges could be used (e.g., below 30 F, between 30 F and 90 F, and above 90 F). Other implementations are also possible. After these processes are complete, the device 102 distributes (see block 1015) trained discriminator models D and also different temperature ranges applicable to the models.”
Chakraborty and Bhattacharyya as modified by Kiemele does are analogous arts as they are both related to models to process sensor data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the temperature-range specific models of Chakraborty with the localized models of Bhattacharyya as modified by Kiemele to arrive at the present invention, in order to optimize model performance in different environments, as stated in Chakraborty, paragraph 0083, “Transmitters 28 may also be affected by temperatures. For instance, IoT transmitters might be in an environment outside a conditioned space. Based on this, a device 102 could train its discriminator model D 110 for different temperatures.”
Regarding claim 5:
Bhattacharyya as modified by Kiemele teaches “The method of claim 1.”
Kiemele further teaches (bold only) “wherein the gateway is located in a refrigerate station, and the machine learning model is trained to trace temperature of the vaccine vials at a warming temperature range”: Kiemele, paragraph 0061, “Training data obtained from the training data repository, including location-specific training data 372, is used by an ML model training component 380 (which may be referred to as an ‘ML model training module’) to produce a location-specific model 382 [wherein the gateway is located, interpreted as location-specific modelling], much as described in FIG. 1 [the machine learning model is trained]. The newly obtained location-specific training data 370, as well as other location-specific training data included in the training data repository 372 from previous training, may be preferentially weighted. In some examples, multiple different location-specific training models may be generated. In some implementations, the ML model training component 380 is configured to generate a location-specific training model 382 that, in combination with other computing activities performed by the sensor device 310, can achieve real-time performance. This may affect a selection of an ML algorithm or an ML model size. Accordingly, where sensor device 310 provides fairly low computational resources, such as an Internet of Things (IoT) device, a less computationally intensive ML algorithm, such as a decision tree or random decision forest, may be selected over a deep learning algorithm such as a convolution neural network”; Kiemele, paragraph 0021, “For purposes of this disclosure, a ‘sensor type’ refers to a particular modality that a physical sensor is designed to operate in and/or receive or detect information about. For example, some broad modalities may include, but are not limited to, audio, light, haptic, flow rate, distance, pressure, motion, chemical, barometric, humidity, and temperature [trained to trace temperature of the vaccine vials]. Within each of these modalities there is a wide array of specific modalities, or sensor types, as well as a wide array of sensor data types reflecting sensor measurements.”
Kiemele and Bhattacharyya are combinable for the rationale given under claim 1.
Bhattacharyya as modified by Kiemele does not explicitly teach (bold only) “wherein the gateway is located in a refrigerate station, and the machine learning model is trained to trace temperature of the vaccine vials at a warming temperature range.”
Chakraborty teaches (bold only) “wherein the gateway is located in a refrigerate station, and the machine learning model is trained to trace temperature of the vaccine vials at a warming temperature range”: Chakraborty, paragraph 0084, “Temperature ranges might be used. For instance, a device 102 might perform training in block 1010 if the temperature is below freezing, within a range from freezing to 80 F, and also higher than 80 F. If a current temperature is below freezing, one discriminator model D 110 would be used; if a current temperature is above freezing but below 80 F, another discriminator model D 110 is used [at a warming temperature range][in a refrigerate station, refrigerate station interpreted as a location containing a model tracing temperatures at a warming temperature range]; and if the temperature is above 80 F, a third discriminator model D 110 might be used. Note also that very high temperatures could be used, such as above 150 F, in case an IoT device has direct sun exposure. These might also be automatic, such that if an IoT device is placed into service at 65 F, training would be performed at that temperature. If the temperature varies by a certain amount (e.g., 30 F) from there, training would be performed at the new temperature, and therefore two, three, or more temperature ranges could be used (e.g., below 30 F, between 30 F and 90 F, and above 90 F). Other implementations are also possible. After these processes are complete, the device 102 distributes (see block 1015) trained discriminator models D and also different temperature ranges applicable to the models.”
Chakraborty and Bhattacharyya as modified by Kiemele does are analogous arts as they are both related to models to process sensor data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the temperature-range specific models of Chakraborty with the localized models of Bhattacharyya as modified by Kiemele to arrive at the present invention, in order to optimize model performance in different environments, as stated in Chakraborty, paragraph 0083, “Transmitters 28 may also be affected by temperatures. For instance, IoT transmitters might be in an environment outside a conditioned space. Based on this, a device 102 could train its discriminator model D 110 for different temperatures.”
Regarding claim 6:
Bhattacharyya as modified by Kiemele teaches “The method of claim 1.”
Kiemele further teaches (bold only) “wherein the gateway is located in a preparation station, and the machine learning model is trained to trace temperature of the vaccine vials at a room temperature range”: Kiemele, paragraph 0061, “Training data obtained from the training data repository, including location-specific training data 372, is used by an ML model training component 380 (which may be referred to as an ‘ML model training module’) to produce a location-specific model 382 [wherein the gateway is located, interpreted as location-specific modelling], much as described in FIG. 1 [the machine learning model is trained]. The newly obtained location-specific training data 370, as well as other location-specific training data included in the training data repository 372 from previous training, may be preferentially weighted. In some examples, multiple different location-specific training models may be generated. In some implementations, the ML model training component 380 is configured to generate a location-specific training model 382 that, in combination with other computing activities performed by the sensor device 310, can achieve real-time performance. This may affect a selection of an ML algorithm or an ML model size. Accordingly, where sensor device 310 provides fairly low computational resources, such as an Internet of Things (IoT) device, a less computationally intensive ML algorithm, such as a decision tree or random decision forest, may be selected over a deep learning algorithm such as a convolution neural network”; Kiemele, paragraph 0021, “For purposes of this disclosure, a ‘sensor type’ refers to a particular modality that a physical sensor is designed to operate in and/or receive or detect information about. For example, some broad modalities may include, but are not limited to, audio, light, haptic, flow rate, distance, pressure, motion, chemical, barometric, humidity, and temperature [trained to trace temperature of the vaccine vials]. Within each of these modalities there is a wide array of specific modalities, or sensor types, as well as a wide array of sensor data types reflecting sensor measurements.”
Kiemele and Bhattacharyya are combinable for the rationale given under claim 1.
Bhattacharyya as modified by Kiemele does not explicitly teach (bold only) “wherein the gateway is located in a preparation station, and the machine learning model is trained to trace temperature of the vaccine vials at a room temperature range.”
Chakraborty teaches (bold only) “wherein the gateway is located in a preparation station, and the machine learning model is trained to trace temperature of the vaccine vials at a room temperature range”: Chakraborty, paragraph 0084, “Temperature ranges might be used. For instance, a device 102 might perform training in block 1010 if the temperature is below freezing, within a range from freezing to 80 F, and also higher than 80 F. If a current temperature is below freezing, one discriminator model D 110 would be used; if a current temperature is above freezing but below 80 F, another discriminator model D 110 is used [at a room temperature range][in a preparation station, preparation station interpreted as a location containing a model tracing temperatures at a room temperature range]; and if the temperature is above 80 F, a third discriminator model D 110 might be used. Note also that very high temperatures could be used, such as above 150 F, in case an IoT device has direct sun exposure. These might also be automatic, such that if an IoT device is placed into service at 65 F, training would be performed at that temperature. If the temperature varies by a certain amount (e.g., 30 F) from there, training would be performed at the new temperature, and therefore two, three, or more temperature ranges could be used (e.g., below 30 F, between 30 F and 90 F, and above 90 F). Other implementations are also possible. After these processes are complete, the device 102 distributes (see block 1015) trained discriminator models D and also different temperature ranges applicable to the models.”
Chakraborty and Bhattacharyya as modified by Kiemele does are analogous arts as they are both related to models to process sensor data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the temperature-range specific models of Chakraborty with the localized models of Bhattacharyya as modified by Kiemele to arrive at the present invention, in order to optimize model performance in different environments, as stated in Chakraborty, paragraph 0083, “Transmitters 28 may also be affected by temperatures. For instance, IoT transmitters might be in an environment outside a conditioned space. Based on this, a device 102 could train its discriminator model D 110 for different temperatures.”
Regarding claims 20–22:
These claims are analogous to claims 4–6, respectively, and are rejected by the same reasoning.
Claims 8 and 24 rejected under 35 U.S.C. 103 as unpatentable over Bhattacharyya as modified by Kiemele in view of Carson, US Pre-Grant Publication No. 2012/0224178 (hereafter Carson).
Regarding claim 8:
Bhattacharyya as modified by Kiemele teaches “The method of claim 1.”
Kiemele further teaches (bold only) “wherein the gateway is located in a discard station, and the machine learning model is trained to trace discard of vaccine vials”: Kiemele, paragraph 0061, “Training data obtained from the training data repository, including location-specific training data 372, is used by an ML model training component 380 (which may be referred to as an ‘ML model training module’) to produce a location-specific model 382 [wherein the gateway is located, interpreted as location-specific modelling], much as described in FIG. 1 [the machine learning model is trained]. The newly obtained location-specific training data 370, as well as other location-specific training data included in the training data repository 372 from previous training, may be preferentially weighted. In some examples, multiple different location-specific training models may be generated. In some implementations, the ML model training component 380 is configured to generate a location-specific training model 382 that, in combination with other computing activities performed by the sensor device 310, can achieve real-time performance. This may affect a selection of an ML algorithm or an ML model size. Accordingly, where sensor device 310 provides fairly low computational resources, such as an Internet of Things (IoT) device, a less computationally intensive ML algorithm, such as a decision tree or random decision forest, may be selected over a deep learning algorithm such as a convolution neural network”; Kiemele, paragraph 0021, “For purposes of this disclosure, a ‘sensor type’ refers to a particular modality that a physical sensor is designed to operate in and/or receive or detect information about. For example, some broad modalities may include, but are not limited to, audio, light, haptic, flow rate, distance, pressure, motion, chemical, barometric, humidity, and temperature [trained to trace … vaccine vials]. Within each of these modalities there is a wide array of specific modalities, or sensor types, as well as a wide array of sensor data types reflecting sensor measurements.”
Kiemele and Bhattacharyya are combinable for the rationale given under claim 1.
Bhattacharyya as modified by Kiemele do not explicitly teach (bold only) “wherein the gateway is located in a discard station, and the machine learning model is trained to trace discard of vaccine vials.”
Carson teaches (bold only) “wherein the gateway is located in a discard station, and the machine learning model is trained to trace discard of vaccine vials”: Carson, paragraphs 0009-0010, “Generally, in one embodiment, the present invention relates to a method and apparatus for determining whether a freeze-sensitive vaccine presentation has been damaged by freezing during storage or transportation, and thus whether it should still be used or discarded [to trace discard of vaccine vials][located in a discard station, discard station interpreted as a place where a determination of discarding is made]. One method of the present invention may be used in connection with vaccine formulations where agglomerates form from dispersed adjuvant particles in the event the vial has been frozen. These agglomerates have a faster settling rate, and thus tend to fall towards the bottom of the vial more quickly than the dispersed, non-agglomerated particles. As the agglomerated particles fall, the amount of light transmitted through the top of the dispersion increases with time. The rate of this increase can be compared against a known frozen, reference control vaccine to determine whether the vaccine specimen in question, or, suspect vaccine, is still safe to use.”
Carson and Bhattacharyya as modified by Kiemele are analogous arts as they are both related to temperature monitoring. Carson shows that the determination of discarding a vaccine vial can be made on the basis of a temperature reading. Bhattacharyya as modified by Kiemele is a method of reading temperature using localized models. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the teachings of Carson with the teachings of Bhattacharyya as modified by Kiemele to arrive at the present invention, in order to discard unsafe vials based on temperature, as stated in Carson, paragraph 0004, “However, if a vial of such a vaccine is frozen, the lattice of antigen may be removed from the adjuvant particles, and the antigen-adjuvant combination may be unable allow vaccinations at low dosages and, therefore, may be unusable.”
Regarding claim 24:
This claims is analogous to claim 8 and is rejected by the same reasoning.
Claims 11 and 27 rejected under 35 U.S.C. 103 as unpatentable over Bhattacharyya as modified by Kiemele in view of Owen, “Measurement Good Practice Guide No. 68: Good Practice Guide to Phase Noise Measurement,” 2004 (hereafter Owen).
Regarding claim 11:
Bhattacharyya as modified by Kiemele teaches “The method of claim 1.”
Bhattacharyya as modified by Kiemele does not explicitly teach wherein each received frequency word is measured as dBC/Hz, wherein the dBC is dBC unit is decibels relative to the carrier.”
Owen teaches “wherein each received frequency word is measured as dBC/Hz, wherein the dBC is dBC unit is decibels relative to the carrier”: Owen, section 1.1, “However, if a signal was disturbed by a phase modulation signal of (say) 0.01 radians at a variable offset between (say) 1 kHz and 100 kHz, the sideband signal level seen on a spectrum display would have a fixed relationship with the carrier level [the dBC is dBC unit is decibels relative to the carrier] (as it happens in this case it would be -46 dBc), irrespective of the frequency offset of the signal. Applying the reverse argument the signal level in dBc/Hz [each received frequency word is measured as dBC/Hz] is therefore directly related to the amount of phase disturbance the noise signal generates in that narrow band of modulation frequencies.”
Owen and Bhattacharyya are analogous arts are they are both related to signal processing. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine the use of the dBc/Hz unit of Owen for measuring frequency changes with using frequency changes in Bhattacharyya to track material changes, with the advantage of using a common and generic method, as stated in Owen, section 1, “Phase noise is the most generic method of expressing frequency instability. The carrier frequency instability is expressed by deriving the average carrier frequency and then measuring the power at various offsets from the carrier frequency in a defined bandwidth. The result is then expressed as a logarithmic ratio compared to the total carrier power.”
Regarding claim 27:
This claim is analogous to claim 11 and is rejected the same reasoning.
Claims 12–13 and 28–29 rejected under 35 U.S.C. 103 as unpatentable over Bhattacharyya as modified by Kiemele in view of Buchanan et al., US Pre-Grant Publication No. 2014/0121973 (hereafter Buchanan).
Regarding claim 12:
Bhattacharyya as modified by Kiemele teaches “The method of claim 1.”
Bhattacharyya further teaches (bold only) “training a classifier to classify the extracted data feature, wherein training the classifier includes: receiving a learning dataset of unseen frequency words; filtering frequency words demonstrating at least abnormal readings; extracting data features from the filtered frequency words; and training the machine learning model based on the extracted data features”: Bhattacharyya, Section III, paragraph 2, “Both Za and Zc are functions of the operating frequency of the reader antenna. By causing Za to change with violation of a temperature threshold, we seek to manipulate the frequencies at which τ is close to 1. We therefore intend to design a narrow band RFID tag antenna where there is an appreciable shift in the optimal matching frequencies when a critical temperature threshold is violated. Furthermore, we constrain this frequency shift completely in the 902-928 MHz band of American RFID operations. Specifically, we design our sensor to have the following states: State 1: τ is designed to be near 1 for 920-925 MHz and sharply decreases for all other frequencies when the temperature is below a given threshold. State 2: If the temperature exceeds the threshold, the optimal frequencies over which τ is near 1 shifts to the 907-912 MHz range and sharply decreases for all other frequencies [frequency word]. If we design a tag antenna that exhibits these properties then we are presented with a frequency domain based state detection mechanism that can be implemented on commercially available RFID readers in the Americas: For a given reader tag separation, we determine the threshold transmitted power at which the reader just starts detecting the tag sensor and observe the frequency channels on which the tag is responding. If the tag is in State 1, we would expect responses on the 920-925 MHz channels. However, if the tag is in State 2, responses would be in the 907-912 MHz range. A change in channel responses thus enable us to detect which state the sensor is currently in.”
Bhattacharyya as modified by Kiemele does not explicitly teach (bold only) “training a classifier to classify the extracted data feature, wherein training the classifier includes: receiving a learning dataset of unseen frequency words; filtering frequency words demonstrating at least abnormal readings; extracting data features from the filtered frequency words; and training the machine learning model based on the extracted data features.”
Buchanan teaches (bold only) “training a classifier to classify the extracted data feature, wherein training the classifier includes: receiving a learning dataset of unseen frequency words; filtering frequency words demonstrating at least abnormal readings; extracting data features from the filtered frequency words; and training the machine learning model based on the extracted data features”: Buchanan, paragraph 0035, “The well log data may be obtained using various sensors and indicators, such as pressure sensors, temperature sensors, and valve position indicators, among others, that may be included within the downhole tool [receiving a learning dataset of unseen … words]”; Buchanan, paragraph 0038, “After the time series data has been preprocessed by noise removal (block 255) to remove redundant data and outliers, the data may be filtered (block 256) and features may be selected [filtering … words demonstrating at least abnormal readings; extracting data features from the filtered … words]. Training the classification methods on features [training the machine learning model based on the extracted data features] derived from the raw data [training a classifier to classify the extracted data feature] can lead to improved classification results. For example, as shown in FIG. 5, a statistical technique such as principal component analysis may be employed to determine the data components that have the largest possible variance. FIG. 5 depicts a principal component plot that shows the top four principal components that account for the variance in the data. The x-axis shows the principal components, and the y-axis shows the percent variance for which each component is responsible.”
Buchanan and Bhattacharyya as modified by Kiemele are analogous arts as they are both related to models processing sensor data. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the data filtering of Buchanan with the teachings of Bhattacharyya as modified by Kiemele to arrive at the present invention, in order to improve classification results, as stated in Buchanan, paragraph 0038, “Training the classification methods on features derived from the raw data can lead to improved classification results.”
Regarding claim 13:
Bhattacharyya as modified by Kiemele and Buchanan teaches “The method of claim 12.”
Kiemele further teaches “wherein the machine learning model is any one of: a supervised model and an unsupervised model”: Kiemele, paragraph 0027, “A training event detection component 140 (which may be referred to as a ‘training event detection module’) receives the second sensor data 132, detects instances of events of interest, and produces training event instance data 142 corresponding to the detected event instances. In some examples, the training event detection component 140 further performs labeling of the detected event instances, and data corresponding to one or more resulting labels may be included in the training event instance data 142 [labelled data used for training, hence, a supervised model].”
Kiemele and Bhattacharyya are combinable for the rationale given under claim 1.
Regarding claims 28–29:
These claims are analogous to claims 12–13, respectively and are rejected by the same reasoning.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Roper et al., US Pre-Grant Publication No. 2022/0120488, discloses a method of tracking individual containers of medicines including vaccines within a storage system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT SPRAUL whose telephone number is (703) 756-1511. The examiner can normally be reached M-F 9:00 am - 5:00 pm.
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/VAS/ Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129