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
Claim 1 is objected to because of the following informalities:
Claim 1, line 1, “A cloud-based AI intelligent electrical stimulation system” should read “A cloud-based artificial intelligence (AI) intelligent electrical stimulation system”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-6, 8, and 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.
Claim 4 recites the limitation "receive the user’s daily tolerance data" in line 2. There is insufficient antecedent basis for this limitation in the claim. Examiner will interpret as "receive a user’s daily tolerance data" and suggests amending.
Claim 5 recites the limitation "determines the user’s physical condition" in line 7. There is insufficient antecedent basis for this limitation in the claim. Examiner will interpret as "determines a user’s physical condition" and suggests amending.
Claim 6 is rejected based on its dependency on claim 5.
Claim 8 recites the limitation "data of the user’s age" in line 4. There is insufficient antecedent basis for this limitation in the claim. Examiner will interpret as "data of a user’s age" and suggests amending.
Claim 13 is replete with insufficient antecedent basis limitations. Examiner will interpret as "A cloud-based AI intelligent electrical stimulation control method, comprising: collecting first data of a user through a wrist stimulator, wherein the first data comprises one or more of tremor data and physiological data; uploading the first data to a cloud server through a mobile terminal to generate a stimulation parameter set through an AI model module, wherein the first data is transmitted by the wrist stimulator to the cloud server through the mobile terminal, and the stimulation parameter set comprises one or more control parameters of an optimization processing module according to user tolerance data, transmitting the adjusted stimulation parameter set to the wrist stimulator, such that the wrist stimulator outputs electrical stimulation pulses based on the stimulation parameter set." and suggests amending.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 7-8, and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over CN 116917003 Ziv et al., hereinafter “Ziv”, in view of DE 202024105231 Khalil et al., hereinafter “Khalil”.
Regarding claim 1, Ziv discloses a cloud-based artificial intelligence (AI) intelligent electrical stimulation system (System of Figure 1A, cloud is shown as element 36 and Para 263 discloses AI), comprising:
a wrist stimulator (Figure 1A, element 10), comprising a first acquisition module (Figure 2A, elements, 11, 13, and/or 16; see also Para 152) and a stimulation module (Figure 2A, element 12a/12b; see also Para 152), wherein the first acquisition module is configured to collect first data (Para 170; “the wearable device is configured to record the subject’s ECG by deploying an ECG sensor 16”), and the stimulation module is configured to apply electrical stimulation pulses to one or more target nerves (Para 152; “wearable devices include one or more electrodes 12a, 12b, which are configured to provide neuromodulation therapy to the subject” and Para 164; “In some embodiments, one or more of the subject's median nerve, radial and ulnar nerve, tibial nerve, peroneal nerve, subcostal nerve, intravertebral nerves, and/or different nerves are stimulated”); the first data comprises one or more of tremor data and physiological data (Para 170 discloses ECG therefore disclosing physiological data);
a mobile terminal in communication connection with the wrist stimulator (Figure 1A, any one of elements 30, 32, or 34, Para 160: “Measurement data can be sent to control module 18 and/or external computing device 28, such as smartphone 30, tablet 32, computer 34, or remote cloud server 36 or the like, where the measurement data is processed.”; Para 287: “For example, the control module 18 may receive input from the subject via the user interface 14 and/or via a smartphone 30, tablet 32 and/or personal computer 34 (as shown in Figure 1A)”);
a cloud server in communication connection with the mobile terminal (Para 154 and 159) , comprising an AI model module (Para 263) and an optimization processing module (Consider Para 154 that discloses that the control module can be located on a cloud server and Para 160 discloses that data is processed on the control module, thereby disclosing a processing module);
the Al model module outputs a stimulation parameter set based on the first data and pre-stored user data (Para 263: “in order to determine which treatment parameters to apply to the subject, in addition to analyzing current data or recently acquired subject-related data, the subject's and/or other subjects' responses to previous treatments are also considered, for example, by using artificial intelligence and/or machine learning algorithms.”), and the first data is transmitted by the wrist stimulator to the cloud server through the mobile terminal (Para 160 and 159 “The communication unit 19 can be configured to communicate over long distances via cellular networks and/or Wi-Fi (e.g., with the user's cloud storage”); the stimulation parameter set includes one or more control parameters of the electrical stimulation pulses (Para 217-229); and
the optimization processing module (Para 157; “one or more external computing devices 28 (as shown in Figures 1A-1B), which may include a computer processor”) adjusts one or more control parameters of the stimulation parameter set (Para 157 and 263, see also Para 151 that discloses adjusting stimulus parameters based on updated monitoring data) and transmits the adjusted stimulation parameter set to the wrist stimulator (Para 151), such that the wrist stimulator outputs electrical stimulation pulses based on the stimulation parameter set (Para 151 “The stimulus parameters are adjusted accordingly (i.e., updated values are taken into account). Then, the device can switch back to stimulation mode and resume applying stimulation, and so on”).
Ziv does not disclose adjusts one or more control parameters of the stimulation parameter set according to user tolerance data.
However, Khalil discloses an artificial intelligence-based device for monitoring and treating neuropathic pain (Para 1) and teaches adjusts one or more control parameters of the stimulation parameter set according to user tolerance data (Para 36 and 50; the models are personalized to the user and the parameters keep adjusting until they reach the personalized sensitivity and therapeutic response to the individual; tolerance data under BRI simply means the allowable range to reach a therapeutic response, without surpassing or underperforming stimulation).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have disclosed user tolerance data as taught by Khalil, in the invention of Ziv, in order to provide a personalized therapeutic response tailored to the user's condition (Khalil; Para 36).
Regarding claim 2, Ziv discloses the mobile terminal further generates pre-stored user data and/or user tolerance data based on consultation results (Para 263; a user’s response to previous treatment is considered pre-stored data based on consultation results) and transmits the data to the cloud server (Para 160 and 159 “The communication unit 19 can be configured to communicate over long distances via cellular networks and/or Wi-Fi (e.g., with the user's cloud storage” and 263; the AI system loaded onto the control unit receives this data).
Regarding claim 3, Ziv discloses after receiving the stimulation parameter set sent by the cloud server (Para 160 and 263), the mobile terminal converts the stimulation parameter set into a waveform graph for display (Para 166, 217, and 224; the control unit/mobile terminal as disclosed in Para 160 determined stimulation parameters which includes determining a signal waveform; the term “for display” is intended use, the claim does not recite a limitation that positively recites displaying the waveform), and marks an original optimal stimulation identifier (Para 217; under BRI an optimal stimulation identifier is simply the most optimal set of treatment parameters that are determined, examiner interprets the word “marks” to mean sets).
Ziv does not disclose marks a user tolerance benchmark identifier.
However, Khalil teaches marks a user tolerance benchmark identifier (Para 36 and 50; the models are personalized to the user and the parameters keep adjusting until they reach the personalized sensitivity and therapeutic response to the individual; tolerance data under BRI simply means the allowable range to reach a therapeutic response, without surpassing or underperforming stimulation, examiner interprets the word “marks” to mean sets).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have disclosed user tolerance data as taught by Khalil, in the invention of Ziv, in order to provide a personalized therapeutic response tailored to the user's condition (Khalil; Para 36).
Regarding claim 4, Ziv discloses all the limitations of claim 1.
Ziv does not disclose the mobile terminal is also configured to receive a user's daily tolerance data and transmit the data to the cloud server, wherein the optimization processing module further adjusts the stimulation parameter set based on the daily tolerance data.
However, Khalil teaches the mobile terminal is also configured to receive a user's daily tolerance data and transmit the data to the cloud server (Para 32 and 42 disclose that the system/wearable device is used daily; Para 36 discloses that therapy is dynamically adjusted in real time based on feedback thus providing a personalized therapeutic response, therefore the feedback from the sensors is the tolerance data), wherein the optimization processing module further adjusts the stimulation parameter set based on the daily tolerance data (Para 36; based on the feedback from the sensors, i.e. the tolerance data, a personalized therapeutic response is tailored to the user).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have disclosed user tolerance data as taught by Khalil, in the invention of Ziv, in order to provide a personalized therapeutic response tailored to the user's condition (Khalil; Para 36).
Regarding claim 7, Ziv discloses the target nerve is one of a radial nerve, a median nerve, or an ulnar nerve (Para 42; “one or more of the subject's ulnar nerve, median nerve, radial nerve, […] are stimulated”).
Regarding claim 8, Ziv discloses collecting multimodal data to construct a training set (Consider Para 248-263 that lists all the data that are collected and used as “subject-related data” that is fed into the AI system or the machine learning algorithm; note that “to construct a training set” is intended use language, examiner recommends using “configured to” language to positively recite the limitation), wherein the multimodal data comprises data of a user's age (Para 261), medical history (Para 257), tremor type (Para 201; trembling heartbeat), real-time movement status (Para 256; activity data, the term “real-time” is subjective, everything happens in real-time, there is no form of time that is other than “real-time”, therefore any reported activity data is activity that happened in real-time), electrical stimulation response (Para 263) , and electrical stimulation parameter set (Para 257); and
training an initialized neural network based on the training set to obtain the Al model module (Para 263; note that “to obtain the AI model module” is intended use language, examiner recommends using “configured to” language to positively recite the limitation).
Ziv does not explicitly disclose a training method for the AI model module comprises: collecting multimodal data to construct a training set; preprocessing the training set to balance label distribution; and training an initialized neural network based on the training set to obtain the Al model module.
However, Khalil teaches a training method for the AI model module (Para 18, 31, and 49) comprises: collecting multimodal data to construct a training set (Para 18 and 49);
preprocessing the training set to balance label distribution (Para 18 and 49; note that “to balance label distribution” is intended use language, examiner recommends using “configured to” language to positively recite the limitation); and
training an initialized neural network based on the training set to obtain the Al model module Para 18 and 49; note that “to obtain the Al model module” is intended use language, examiner recommends using “configured to” language to positively recite the limitation).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have disclosed multimodal data training as taught by Khalil, in the invention of Ziv, in order to detect subtle patterns and correlations within this multidimensional dataset that indicate neuropathic episodes, so that the NPU can classify the intensity and type of episode with high accuracy (Khalil; Para 49).
Regarding claim 11, Ziv discloses the first data further comprises physiological data (Para 152); the physiological data comprises one or more of skin impedance data, therapeutic response data, electromyographic signal data, neural signal data, skin temperature data, and blood oxygen saturation data (Para 107, 110, and 287 detect therapeutic response data).
Regarding claim 12, Ziv discloses the stimulation module comprises a plurality of electrode wristbands tailored to different wrist sizes (Figure 2A shows a wristband that is adaptable to different sizes, however also consider Para 185 that tailors the band 15 to fit the user), and each of the electrode wristbands is provided with one or more electrode sets for applying electrical stimulation to the target nerve (Para 185); and the electrode set comprises one or more electrode pads (Para 185 and Figure 2D, elements 12a and 12b), and one electrode pad corresponds to one target nerve (Para 185 and Figure 2D, elements 12a and Para 42).
Regarding claim 13, Ziv discloses a cloud-based AI intelligent electrical stimulation control method (Para 7, method of using system of Figure 1A, cloud is shown as element 36 and Para 263 discloses AI), comprising:
collecting first data of a user (Para 170; “the wearable device is configured to record the subject’s ECG by deploying an ECG sensor 16”) through a wrist stimulator (Figure 1A, element 10, more specifically Figure 2A, elements, 11, 13, and/or 16), wherein the first data comprises one or more of tremor data and physiological data (Para 170 discloses ECG therefore disclosing physiological data);
uploading the first data to a cloud server through a mobile terminal (Para 154 and 159; Para 287: “For example, the control module 18 may receive input from the subject via the user interface 14 and/or via a smartphone 30, tablet 32 and/or personal computer 34 (as shown in Figure 1A)”) to generate a stimulation parameter set through an AI model module (Para 263: “in order to determine which treatment parameters to apply to the subject, in addition to analyzing current data or recently acquired subject-related data, the subject's and/or other subjects' responses to previous treatments are also considered, for example, by using artificial intelligence and/or machine learning algorithms.”; also note “to generate” is an intended use limitation, examiner suggests using “configured to” language to positively recite the limitation), wherein the first data is transmitted by the wrist stimulator to the cloud server through the mobile terminal (Para 160 and 159 “The communication unit 19 can be configured to communicate over long distances via cellular networks and/or Wi-Fi (e.g., with the user's cloud storage”), and the stimulation parameter set comprises one or more control parameters of electrical stimulation pulses (Para 217-229); and
adjusting the stimulation parameter set (Para 157 and 263, see also Para 151 that discloses adjusting stimulus parameters based on updated monitoring data) through an optimization processing module (Para 157; “one or more external computing devices 28 (as shown in Figures 1A-1B), which may include a computer processor”), transmitting the adjusted stimulation parameter set to the wrist stimulator (Para 151), such that the wrist stimulator outputs electrical stimulation pulses based on the stimulation parameter set (Para 151 “The stimulus parameters are adjusted accordingly (i.e., updated values are taken into account). Then, the device can switch back to stimulation mode and resume applying stimulation, and so on”).
Ziv does not disclose adjusts one or more control parameters of the stimulation parameter set according to user tolerance data.
However, Khalil discloses an artificial intelligence-based device for monitoring and treating neuropathic pain (Para 1) and teaches adjusts one or more control parameters of the stimulation parameter set according to user tolerance data (Para 36 and 50; the models are personalized to the user and the parameters keep adjusting until they reach the personalized sensitivity and therapeutic response to the individual; tolerance data under BRI simply means the allowable range to reach a therapeutic response, without surpassing or underperforming stimulation).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have disclosed user tolerance data as taught by Khalil, in the invention of Ziv, in order to provide a personalized therapeutic response tailored to the user's condition (Khalil; Para 36).
Claim(s) 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over CN 116917003 Ziv et al., hereinafter “Ziv”, in view of DE 202024105231 Khalil et al., hereinafter “Khalil”, further in view of US 2022/0035452 Lockhart et al., hereinafter “Lockhart”.
Regarding claim 5, Ziv discloses the wrist stimulator (Figure 1A, element 10); wherein the Al model module outputs a stimulation parameter set based on the first data (Para 192, ECG), the second data (Para 192; PPG) and the pre-stored user data (Para 263);
wherein the Al model module outputs a stimulation parameter set according to the first data, the second data, and the pre-stored user data (Para 263; all current data is analyzed and previous data to determine treatment parameters).
Ziv does not disclose a wearable accessory is further comprised and the wearable accessory is provided with a second acquisition module in communication connection with the mobile terminal or the wrist stimulator for detecting second data of the user; and the second data comprises one or more of tremor data and physiological data; when the wearable accessory is in communication connection with the mobile terminal, the mobile terminal, upon receiving the second data, determines the user's physical condition according to the second data and transmits the second data to the cloud server; when the wearable accessory is in communication connection with the wrist stimulator, the wrist stimulator transmits both the second data received and the first data collected to the cloud server through the mobile terminal.
However, Lockhart discloses a system to identify tremors and nerve stimulation therapy (Abstract) and teaches a wearable accessory (Figure 7C, any one of elements 202) is further comprised and the wearable accessory is provided with a second acquisition module (Para 34 and 35 the sensors 202 measure movement) in communication connection with the mobile terminal (Figure 7C shows the mobile terminal 201 or the HMD, Para 97 discloses “each sensor 202 communicates independently with the HMD accessory which then transmits its data to HMD 201”) or the stimulator (Para 97, the stimulator is shown in Figure 7C as element 202B; “In some embodiments, each sensor 202 communicates its position and orientation in real-time with WTM 202B, which is in wireless communication with HMD 201”) for detecting second data of the user (Para 34 and 35 also note that 202B is a sensor as well); and the second data comprises one or more of tremor data and physiological data (Para 35; tremor data is disclosed);
when the wearable accessory is in communication connection with the mobile terminal, the mobile terminal, upon receiving the second data, determines a user's physical condition according to the second data (Para 12, the VR system, i.e. element 201 receives the data and identifies a tremor, i.e. a user’s physical condition) and transmits the second data to the cloud server (Para 94; cloud servers are updated with information);
when the wearable accessory is in communication connection with the wrist stimulator, the wrist stimulator transmits both the second data received and the first data collected to the cloud server through the mobile terminal (Para 97).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have disclosed a wearable accessory as taught by Lockhart, in the invention of Ziv, in order to measure multiple forms of data and transmit them to the same system via one communication interface (Lockhart; Para 97).
Regarding claim 6, Ziv discloses all the limitations of claim 5.
Ziv does not disclose the wearable accessory comprises one or more of a bracelet, glasses, an earphone, a ring, a headband, a waistbelt, a patch, and jewelry.
However, Lockhart teaches the wearable accessory comprises one or more of a bracelet, glasses, an earphone, a ring, a headband, a waistbelt, a patch, and jewelry (Figure 7C shows a waistbelt, bracelet…).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have disclosed a wearable accessory as taught by Lockhart, in the invention of Ziv, in order to measure multiple forms of data and transmit them to the same system via one communication interface (Lockhart; Para 97).
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over CN 116917003 Ziv et al., hereinafter “Ziv”, in view of DE 202024105231 Khalil et al., hereinafter “Khalil”, further in view of KR 20240146184 Lee et al., hereinafter “Lee”.
Regarding claim 9, Ziv discloses the Al model module (Para 263).
Ziv does not disclose a transfer learning unit, being configured to adapt an initial stimulation strategy for a new user based on a cross-user data generalization model; and a reinforcement learning unit, being configured to optimize a long-term efficacy indicator through a reward function according to operational detection data collected by the wrist stimulator each time and a corresponding stimulation parameter set and configure a personalized stimulation strategy.
However, Lee discloses a nerve stimulate device (Para 1) and teaches a transfer learning unit (Para 9; “the stimulation control model is based on a transfer learning algorithm and a reinforcement learning algorithm”), being configured to adapt an initial stimulation strategy for a new user based on a cross-user data generalization model (Para 18; an initial stimulation strategy is adapted and altered under the reward state); and
a reinforcement learning unit (Para 9; “the stimulation control model is based on a transfer learning algorithm and a reinforcement learning algorithm”), being configured to optimize a long-term efficacy indicator through a reward function according to operational detection data collected by the stimulator each time and a corresponding stimulation parameter set and configure a personalized stimulation strategy (Para 18; “the reinforcement learning algorithm are Action, Reward, Environment, and State, wherein the Action corresponds to an element for vagus nerve stimulation through ASMR, TENS, and PEMF, the Reward is a response to stimulation extracted based on sympathetic or parasympathetic nerve changes, the Environment is generated by performing learning based on the user's body response to stimulation based on electrical stimulation or magnetic fields, and the State corresponds to a user state monitoring function based on Environment information”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have disclosed reinforcement learning as taught by Lee, in the invention of Ziv, in order to deliver a personalized stimulus to the user (Lee; Para 9 and 18).
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over CN 116917003 Ziv et al., hereinafter “Ziv”, in view of DE 202024105231 Khalil et al., hereinafter “Khalil”, further in view of US 2025/0010068 Carballo et al., hereinafter “Carballo”.
Regarding claim 10, Ziv discloses a waveform of the electrical stimulation pulse is one or a combination of more of a biphasic square wave, a sine wave, a pulse wave, a triangular wave, and a sharp wave (Para 43; “The signal typically has any one of the following waveforms: sine wave, triangle wave, rectangular wave, and/or sawtooth wave.”, with a frequency range of 1-1000 Hz (Para 44), and a current intensity (Para 268).
Ziv does not disclose a current intensity of 0.1-20 mA.
However, Carballo discloses a wearable neurostimulation device (Abstract) and teaches and a current intensity of 0.1-20 mA (Para 38; examiner also notes that the frequency is also within the range and therefore acceptable to combine).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have disclosed a current intensity with the range taught by Carballo, in the invention of Ziv, in order to deliver treatment based on a user level of comfort (Carballo; Para 38-39).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AYA ZIAD BAKKAR whose telephone number is (313)446-6659. The examiner can normally be reached on 7:30 am - 5:00 pm M-Th.
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/AYA ZIAD BAKKAR/
Examiner, Art Unit 3796
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792
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