DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant has amended claims 1-4, 6-7, 10, 13 and 16-17 and added claims 18-19.
Claim Objections
Claim 17 is objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim. The claim recites “the connected device of claim 16….according to the method of claim 1”, which is improperly dependent, as it is not referring to the other claims in the alternative only. See MPEP § 608.01(n). Accordingly, the claim 17 not been further treated on the merits.
Claim Rejections - 35 USC § 112
Claims 1-19 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 1 recites “during the acquisition of the measurements” There is insufficient antecedent basis for this limitation in the claim. The only acquisition step in the now amended method of claim 1 is “acquiring raw measurements using the measurement sensor” Therefore correction is required.
Claim 2 similarly recites “during the acquisition of the measurements”, and therefore also suffers from a lack of antecedent basis.
Claim 3 recites “comparing the measurement sequence transmitted to the interpreting application with the standard sequence of measurements.” However, amended claim 1 recites “transmitting data, established with the sequence of raw measurements or pre-processed raw measurements”. Therefore, it is unclear what sequence is “the measurement sequence” since 3 such sequences have now been defined.
Claim 4 recites “following acquisition of a sequence of raw measurements” which leads to indefiniteness as “a sequence of raw measurements” has already been defined in claim 1 on which claim 4 depends. Therefore, it is unclear if this is the same sequence of a different sequence.
Dependent claims 2-19 inherit the indefiniteness of claim 1 on which they depend, and are therefore also rejected under 35 U.S.C. 112 (b).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is/are directed to a method, which would fall into a statutory category of invention. However, the claim includes steps of “periodically verifying the conformity of the values of each sensor acquisition parameter with respect to the value specified for said acquisition parameter, for each sensor acquisition parameter respectively, verification being considered to be: negative when at least one value of an acquisition sensor parameter does not conform with the value specified for said sensor acquisition parameter; positive when the value of each sensor acquisition parameter does conform with the value specified for said sensor acquisition parameter; following a negative verification, updating each non-conforming acquisition- parameter sensor-parameter value, by replacing each non-conforming acquisition parameter value with the value specified for said acquisition parameter.” Under step 2a prong 1 These steps are taken to be mathematical calculations, as they are a verification which is a form of an inequality (i.e. is a value greater than or less than) and therefore mathematics. Under step 2a prong 2, the abstract idea is not taken to integrated into a practical application as other then a determination there is no tying of the determination to an application, only a value replacement which itself can be considered to be math and abstract.
Under step 2b, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the details provided in the preamble regarding the measurement data and transmission, as well as the actual acquisition and transmission steps are no more than required and routine data gathering, required to perform the abstract math. Further the use of central processing unit to perform the math is no more than routine automation by a computer which the courts have repeatedly stated is no more than the abstract idea itself.
Dependent claims 2-16 fail to incorporate anything significantly more than the abstract idea of claim 1.
Claims 2-3, 13 and 19 further define the analysis and math from the collected data, which is abstract itself.
Claims 4-11 define a calibration, which is also a form of math even when incorporating neural networks as they are simply computer automation of mathematical relationships.
Claim 12 lists various types of known sensors, and claim 14 states what type of data is collected, which are no more than use of known and routine data gathering and therefore are not significantly more than the abstract idea itself.
Claim 15 requires the computer automation of the method, which has been repeatedly determined by the courts to not be significantly more.
Claims 16-18 introduce a generic “connected device” which discusses the data gathering but fails to tie to a specific device and integration and therefore also do not introduce more than the abstract idea of claim 1.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-7 and 11-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Goode Jr et al (2005/0043598) hereby referred to as Goode.
Regarding claim 1: Goode discloses a method for processing measurements, acquired by a measurement sensor (Goode paragraph 0230, 0273), at various measurement times (Goode Fig 7A, &b, paragraph 0206-0207, 0228 where measurements over time in intervals are explicitly defined), the measurement sensor being: being integrated into a connected device worn and/or borne by a user (Goode paragraph 0273) and configured to be connected to a wireless communication network (Goode paragraph 0304 where both wired and wireless connections are disclosed) the measurement sensor being configured to acquire, at each measurement time, a raw measurement (Goode fig 7A, &b, paragraph 0206-0207, 0228) representative of a movement of the user or of a physiological characteristic of the user (Goode 0228, 0230 where the glucose measure is a physiological characteristic) the measurement sensor being connected to an electronic acquisition circuit configured to control acquisition of raw measurements by the sensor (Goode paragraph 0228, 02330304) and/or pre-processing of raw measurements acquired by the sensor (Goode paragraph 0304 where the addressing of signal noise is the preprocessing), the electronic control circuit comprising at least one control register and the measurement sensor being parameterized by acquisition parameters, each acquisition parameter being stored in one control register of the electronic control circuit, to each acquisition parameter corresponding to one specified value (Goode paragraph 0281-0282 where the oxygen value is the acquisition parameter, as it much be in a correct range to get valid glucose levels); the method comprising:
acquiring raw measurements using the measurement sensor at various measurement times, so as to form a sequence of raw measurements (Goode figure 7A, 7B, paragraph 0206-0207 where raw data is explicitly collected over a time period (4 hours in 7A and 36 hours in 7B) and presented in a sequence in the form of the graph);
transmitting data, established with the sequence of raw measurements or pre-processed raw measurements, to an interpreting application (Goode paragraph 0304 where a sensor data receiving module is explicitly described which would be receiving transmitted raw data (the data stream) via wired or wireless communication. Further this may be replaced with signals “to address signal noise” which would cover the “or pre-processed” data), the interpreting application being programmed to estimate, on the basis of the transmitted data, a user state, the user state being selected from a plurality of predetermined states (Goode paragraph 0314 where the conversion and matching is an estimation of state, paragraph 0320 where determination of hypoglycemic or hyperglycemic conditions are also estimation of state);
wherein the method further comprises, during acquisition of the measurements, and using a central processing unit (Goode paragraph 0233):
periodically verifying the conformity of the values of each sensor acquisition parameter with respect to the value specified for said acquisition parameter(Goode paragraph 0282, 0337 where signal artifact determination is described and based on oxygen, the previously discussed acquisition parameter), for each sensor acquisition parameter respectively, verification being considered to be:
negative when at least one value of an acquisition sensor parameter does not conform with the value specified for said sensor acquisition parameter (Goode paragraph 0342 where the low oxygen state (ischemia) is the negative value);
positive when the value of each sensor acquisition parameter does conform with the value specified for said sensor acquisition parameter (Goode paragraph 0347 where the states are compared and where a determination of non-ischemia would be the positive determination);
following a negative verification, updating each non-conforming acquisition- parameter sensor-parameter value, by replacing each non-conforming acquisition parameter value with the value specified for said acquisition parameter (Goode paragraph 0034, 0338 where signal artifact replacement is introduced see also paragraph 0390 and discussions of the “cone of possibility” methods which further described a positive and negative determination to handle the signal artifacts).
Regarding claim 2: Goode discloses the method of claim 1 as described above. Goode also discloses during acquisition of the measurements, a plurality of successive verifications of the conformity of the values of each acquisition parameter, wherein, following a negative verification, measurements acquired since a preceding positive verification and/or until a following positive verification are considered doubtful or invalid (Goode paragraph 0338 where the estimation and replacement is continuously run and therefore is iterative as required here).
Regarding claim 3: Goode discloses the method of claim 1 as described above. Goode also discloses considering a standard sequence of raw or pre-processed measurements by the connected device (Goode paragraph 0339 which provides projected or historical values which would be the “standard”);
transmitting the standard sequence of the raw or pre-processed measurements to the interpreting application, this being done by the connected device (Goode paragraph 0339);
comparing the measurement sequence transmitted to the interpreting application with the standard sequence of measurements (Goode paragraph 0339 where the system looks for outliers that will skew data which is this comparison).
Regarding claim 4: Goode discloses the method of claim 1 as described above. Goode also discloses following acquisition of a sequence of raw measurements:
processing each raw or pre-processed measurement by means of a calibration model, so as to estimate, on the basis of each measurement, a reference measurement (Goode paragraph 0303 where glucose sensor calibration is explicitly disclosed);
transmitting each estimated reference measurement to the interpreting application, the reference measurements then forming the data transmitted to the interpreting application (Goode paragraph 0304 where sending and receiving are explicitly disclosed); wherein the calibration model is established during a calibrating phase, comprising various calibration times (Goode paragraph 0303-0305 where there is storage of data over time and the initial and reference data calibrations are disclosed), the calibrating phase comprising:
i) acquiring calibration measurements using the measurement sensor, at the various calibration times (Goode paragraph 0304 where data at various times is utilized), and obtaining reference measurements, at each calibration time, such that each reference measurement corresponds to a calibration measurement (Goode paragraph 0305 where reference data is disclosed at various points 0307 where each reference measurement corresponds to a calibration measurement which is a data points/time point), at least one reference measurement being representative of a user state among the predetermined states (Goode paragraph 0305);
ii) on the basis of the reference measurements and of the calibration measurements, defining a calibration model, the calibration model being configured to estimate reference measurements on the basis of measurements acquired by the measurement sensor (Goode paragraph 0305 where the calibration includes utilization of the reference glucose data and therefore is on the basis of the reference measurement) ;
wherein each reference measurement measures the same physical or chemical quantity as each calibration measurement (Goode paragraph 0305- where the reference data used is glucose data and therefore when comparing glucose data from the glucose sensor it is the same data type); and
wherein ii) is implemented using the processing unit (Goode paragraph 0284) .
Regarding claim 5: Goode discloses the method of claim 4 as described above. Goode also discloses wherein, in step i), the measurement sensor is placed on a phantom, representative of at least one user state, the reference measurements being obtained from the phantom (Goode paragraph 0305 where in the received reference data can come from the self monitor which may be a “phantom” as claimed).
Regarding claim 6: Goode discloses the method of claim 5 as described above. Goode also discloses wherein the phantom comprises a reference sensor, of the same type as the measurement sensor (Goode paragraph 0305 where the data comes from a glucose sensor and therefore they are both glucose data), the reference sensor delivering a reference measurement at each calibration time (Goode paragraph 0305).
Regarding claim 7: Goode discloses the method of claim 4 as described above. Goode also discloses wherein, in step i), the calibration sensor is placed on at least one test individual, the test individual also wearing and/or bearing a reference sensor, the reference sensor delivering a reference measurement at each calibration time (Goode paragraph 0274 where the minimally invasive sensing device is disclosed, Goode paragraph 0303-0307 where the calibration steps are disclosed at calibration times), wherein the reference sensor is of the same type as the measurement sensor (Goode paragraph 0305).
Regarding claim 11: Goode discloses the method of claim 4 as described above. Goode also discloses between steps i) and ii), time synchronizing of the calibration measurements with respect to the reference measurements (Goode paragraph 0307 where the data matching module matches the reference data with times corresponding to the sensor data which is time synchronization).
Regarding claim 12: Goode discloses the method of claim 1 as described above. Goode also discloses wherein the sensor comprises at least: a motion sensor, of the accelerometer and/or gyrometer and/or magnetometer type; and/or a pressure sensor; and/or an optical sensor; and/or an electrical sensor; and/or a chemical sensor; and/or a temperature sensor; and/or a physiological sensor, configured to determine a physiological characteristic of the user (Goode paragraph 0012-0013, 0343-0344).
Regarding claim 13: Goode discloses the method of claim 1 as described above. Goode also discloses wherein the user state is selected from one of the following states: a state describing a physical activity of the user; a state of stress; a state of sleep or drowsiness; a pathological state; a symptomatic state; a state corresponding to occurrence of a situation putting the user at risk (Goode paragraph 0006, 0299, 0320-0321 where hypoglycemic and hyperglycemic states are risk states).
Regarding claim 14: Goode discloses the method of claim 1 as described above. Goode also discloses wherein the user is a living human being or a living animal (Goode paragraph 0240).
Regarding claim 15: Goode discloses the method of claim 1 as described above. Goode also discloses, wherein the interpreting application is implemented by a microprocessor integrated into the connected device, or by a microprocessor remote from the connected device and connected to the latter by a wired or wireless link (Goode paragraph 0304 where both wired and wireless connections are disclosed).
Regarding claim 16: Goode discloses a connected device, configured to be worn and/or borne by a user, comprising a measurement sensor (Goode paragraph 0273);
the measurement sensor being configured to acquire, at various measurement times (Goode Fig 7A, &b, paragraph 0206-0207, 0228 where measurements over time in intervals are explicitly defined), a measurement representative of a movement of the user or of a physiological characteristic of the user (Goode 0228, 0230 where the glucose measure is a physiological characteristic);
the measurement sensor being parametrized by acquisition parameters (Goode paragraph 0281-0282 where the oxygen value is the acquisition parameter, as it much be in a correct range to get valid glucose levels);
each acquisition parameter being stored in a control register of a control circuit of the sensor (Goode paragraph 0281-0282 where the oxygen value is the acquisition parameter, as it much be in a correct range to get valid glucose levels);
the device being configured to activate an interpreting application, the interpreting application being programmed to estimate, on the basis of the measurements acquired by the measurement sensor, a user state, the user state being selected from a plurality of predetermined states (Goode paragraph 0314 where the conversion and matching is an estimation of state, paragraph 0320 where determination of hypoglycemic or hyperglycemic conditions are also estimation of state);
wherein the device comprises a central unit programmed to verify the conformity of the value of each acquisition parameter according to the method of claim 1 (see claim 1 rejection above).
Regarding claim 17 (as best understood due to the multiple dependent nature of the claim): Goode discloses the device of claim 16 as described above. Goode also discloses wherein the central unit is further programmed to update each non-conforming acquisition parameter value according to the method of claim 1 (see claim 1 rejection above).
Regarding claim 18: Goode discloses the device of claim 16 as described above. Goode also discloses wherein the central unit is within the device and/or remotely operated (Goode paragraph 0295 which discloses both remote and local processing).
Regarding claim 19: Goode discloses the method of claim 1 as described above. Goode also discloses wherein at least one the acquisition parameter is at least one of: a sampling frequency of the raw measurements; a parameter of a frequency-domain filter applied to raw measurements; a cut-off frequency filter applied to the raw measurements; a duration of each sequence of raw measurements; a time interval between two sequences of successive raw measurements; a time interval between two raw measurements; a parameter of a calculation of a mean value or a sliding mean value or a median value; a parameter controlling a threshold of the raw measurement; a weighing factor assigned the raw measurement (Goode paragraph 0026-0027 which determines the artifact with frequency monitoring as the “acquisition parameter”, paragraph 0031-0033 which disclose artifact determination through filtering of the raw data, paragraph 0393 where the cone method uses time intervals to determine size).
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) 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Goode in view of Kurfirst (US 2012/0375456) hereby referred to as Kurfirst .
Regarding claim 8: Goode discloses the method of claim 4 as described above. Goode does not explicitly disclose wherein the calibration model implements a supervised artificial-intelligence algorithm, the supervised artificial-intelligence algorithm being parametrized in the course of the calibrating phase.
Kurfirst discloses implements a supervised artificial-intelligence algorithm, the supervised artificial-intelligence algorithm (e.g., The smart vital device 108 may use machine learning algorithms (e.g., supervised artificial intelligence algorithms, unsupervised artificial intelligence algorithms, and/or deep learning algorithms) to determine whether the sensor information indicates the individual 102 has one or more health conditions [0019]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Goode, Jr. with Kurfirst for implementing a supervised artificial-intelligence algorithm as this would give the advantage to determine whether the sensor information indicates the individual has one or more health conditions and provides an indication or other type of notification/alert to the individual of their symptoms and/or provides instructions for the individual, (see Kurfirst, [0019-0020).
Regarding claim 9: Goode and Kurfirst discloses the method of claim 8 as described above.
Goode does not disclose wherein the supervised artificial- intelligence algorithm comprises a neural network.
Kurfirst discloses the supervised artificial-intelligence algorithm comprises a neural network (e.g., The machine learning datasets 314 may be an unsupervised machine learning dataset, a supervised machine learning dataset, and/or a deep learning (e.g., neural network) dataset [0032]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Goode, Jr, with Kurfirst for the supervised artificial-intelligence algorithm comprises a neural network as this would give the advantage to determine one or more health conditions, furthermore, the health conditions may indicate health characteristics, traits, or symptoms of the individual, (see Kurfirst, [0032]).
Claim 10 rejected under 35 U.S.C. 103 as being unpatentable over Goode and Kurfirst in further view of Smurro (US 2021/0313077) hereby referred to as Smurro.
Regarding claim 10: Goode and Kurfirst discloses the method of claim 9 as described above.
Goode, Jr. further discloses the neural network comprising an input layer and an output layer (e.g., other algorithms could be used to determine the conversion function, for example forms of linear and non-linear regression, for example fuzzy logic, neural networks [0323]; At block 51, a sensor data receiving module, also referred to as the sensor data module, receives sensor data (i.e., comprising an input layer) (e.g., a data stream), including one or more time-spaced sensor data points, from a sensor via the receiver [304]; At block 57, an output module provides output (i.e., comprising an output layer) to the user via the user interface [0317]), during the course of processing of each measurement by the calibration model: each raw or pre-processed measurement acquired by the measurement sensor forms the input layer of the neural network (e.g., At block 51, a sensor data receiving module (i.e., forms the input layer of the neural network), also referred to as the sensor data module, receives sensor data (e.g., a data stream) (i.e., each measurement acquired by the measurement sensor), including one or more time-spaced sensor data points (i.e., during the course of processing of each measurement by the calibration model), from a sensor via the receiver [304]), the output layer corresponds to the estimate of at least one reference measurement (e.g., The output is representative (i.e., the output layer corresponds) of the estimated glucose value (i.e., to the estimate of at least one reference measurement), which is determined by converting the sensor data into a meaningful glucose value [0317]).
Goode, Jr. and Kurfirst do not explicitly disclose the neural network comprises a recurrent neural network, and preferably a bidirectional recurrent neural network.
Smurro discloses the neural network comprises a recurrent neural network, and preferably a bidirectional recurrent neural network (e.g., the invention may include, but are not limited to, various combinations of algorithms, applications, tools and techniques for machine learning in medicine, e.g., deep learning, transfer learning, reinforcement learning, convolutional neural networks, recurrent neural networks [0241]; The invention enables live stream multicasting of N-way multi-party collaborations, including multisensory data stream visualization and bi-directional knowledge exchange [0016]; see also Fig. 71-73).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Goode, Jr. and Kurfirst with Smurro for the neural network comprises a recurrent neural network, and preferably a bidirectional recurrent neural network as this would give the advantage for concurrent transmission of secure, encrypted clinical cognitive visemes across collaborative file sharing data networks for informatics-enriched learning, specialist skills acquisition and accelerated knowledge exchange, (see Smurro, [0016]).
Response to Arguments
Applicant's arguments regarding the 101 rejections been fully considered but they are not persuasive.
First applicant has stated that the previous action failed to identify the abstract idea, and simply bolded limitations. The examiner does not agree with this argument, as the bolding was clear to identify the abstract limitations and further within the rejection it specifically stated that these limitations fell into the mathematical relationships, formulas or calculations groupings (see page 5 of the rejection mailed 5/30/2025). In order to more clearly define such limitations, the rejection has been presented in a new format above. The applicant then argues that under step 2a prong one there is no monopolization and no particular math. However, as clarified above there is a comparison, which is a form of inequality which is math. Further, the monopolization argument does not define patentability when looking at abstract ideas and therefore such an argument is not persuasive.
Regarding step 2a prong 2 the applicant has argued that the limitations are integrated into a device and used in control are therefore integrated into a practical application. This is not persuasive, as the device provides the data but there is no positive recitation of control in the body of the claim . the only mention of control is that a circuit is configured to control, but the abstract idea is not tied to any actual controlling in the method. The Examiner notes that were such a control step for the device to be positively claimed in the body of the claim further consideration of the 101 eligibility would be required.
Applicant's arguments regarding the 102 rejections been fully considered but they are not persuasive
Regarding claim 1: Applicant has argued that the Goode reference does not modify “acquisition parameters” stating that these must be parameters that control the circuit as per the amended language. However, as cited above Goode utilizes parameters such as O2 readings and temperature in the control of measurement by determining if the values are in ranges allowing for accurate glucose sensing. These parameters are then considered to be “acquisition parameters” as claimed, since the control and determination of final value is based upon a verification of the range of the parameter.
Regarding claim 4, 6 and 7: Applicant has argued that Goode fails to disclose the newly amended language of the reference measurement measuring the same physical or chemical property as each calibration measurement. However, as clearly cited above the glucose sensor is calibrated using glucose reading from another glucose sensor and therefore the reference is the same property.
Applicant's arguments regarding the 103 rejections been fully considered but they are not persuasive. Applicant has simply argued that the secondary references fail to remedy issues in the Goode reference, which have all been discussed above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHANIE E BLOSS whose telephone number is (571)272-3555. The examiner can normally be reached M-Th 7a-4p, F 7a-11a.
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/STEPHANIE E BLOSS/Supervisory Primary Examiner, Art Unit 2852