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 .
Notice of Reply
This communication is responsive to the amendment(s) and/or argument(s) filed 5/27/26. The previous ground(s) of objection and/or rejection is/are withdrawn. The following new and/or reiterated ground(s) of rejection is/are set forth hereinbelow.
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 1-3 and 8-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, wherein the abstract idea may reasonably be considered a mental process of extracting, calibrating, generating and predicting.
For independent claim 1, the claim(s) recite(s) a process of predicting blood glucose values using blood glucose information including the steps of extracting a first feature, calibrating and extracting a second feature, generating a feature vector by reducing and coming the two features, and predicting a biometric value by applying the vector to a prediction model while also, prior to extracting the second feature, calibrating with pre-processing for unit discrepancy by assigning weighting based on blood glucose state change.
As broadly as claimed these steps may be reasonably considered as the judicial exception of a mental process performable within the human mind, including by observation, evaluation, judgement and opinion forming, or by a human using pen and paper (see MPEP 2106.04(a)(2) subsection III). For example, at least, these limitations are nothing more than a medical professional capturing data , printing it out, and using the data to mentally extract, classify or learn from data features to determine a biometric value prediction. For example at least, a medical professional monitoring and/or evaluating a blood glucose measurement may conduct the prediction entirely using mental arithmetic and/or via pen and paper and/or a glucose monitoring patient may mentally conduct the prediction.
This judicial exception is not integrated into a practical application because the process steps as broadly as claimed are not tied to nor required to be performed, executed, or programmed on a special purpose computer.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because (i) the preliminary steps of using information measured from a sensor are well-known, routine and conventional amounting to insignificant data gathering as pre-solution activity and (ii) although claim 1 positively recites the method is executed by a communication terminal, this generic processing is well-known, routine and conventional amounting to insignificant data manipulation.
Depending claims 2-3 and 8-12 inherit and do not remedy the non-statutory deficiency noted above, despite further specifying steps relating to the sensor measuring, pre-processing, and/or re-learning prediction, because they may be reasonably performed mentally, do not integrate into a practical application, and they do not amount to significantly more than the abstract idea with additional elements.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3 and 8-12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Malin et al. (US 6,280,381 B1, hereinafter Malin).
For claim 1, Malin discloses a method of predicting a blood glucose value of a user, executed by a communication terminal, using blood glucose information measured from a sensor (Figs 14-18) (Cols 4-21), the method comprising inter alia:
extracting a first feature value (one of seven eigenvectors output at 99) from the measured blood glucose information of the user (Figs 14-18) (Cols 4-21);
calibrating (at 99) the measured blood glucose information of the user and extracting a second feature value (at 147) from the calibrated blood glucose information (99) (Figs 14-18) (Cols 4-21);
generating a feature vector value (at 148) by reducing and combining the first feature value and the second feature value (Figs 14-18) (Cols 4-21); and
predicting the blood glucose value (101) of the user by applying the generated feature vector value to a prediction model (at 149) (Figs 14-18) (Cols 4-21),
wherein unit discrepancy (at 140) included in pre-processed blood glucose information is calibrated before extracting the second feature value (Figs 14-18) (Cols 4-21), and
wherein the unity discrepancy is calibrated (at 142,146) by assigning a weight based on a rising state or decreasing state of the blood glucose information, such that, when the blood glucose information indicates rising, the weight is assigned a low value in inverse proportion to the rising speed, and when the blood glucose information indicates decreasing, the weight is assigned a high value in proportion to the decreasing speed (Figs 15-16 show and are described as two membership function examples accounting for rising and falling gaussian distributions, while Col 18 li 57-63 indicate other sub-sets are used for accurate prediction including “blood glucose information” parameters).
For claim 2, Malin discloses the method of predicting the blood glucose value according to claim 1, wherein the sensor is a sensor (NIR light) partially inserted into body of the user for a certain period of time and continuously measuring the blood glucose information of the user (Figs 14-18) (Cols 4-21).
For claim 3, Malin discloses the method of predicting the blood glucose value according to claim 2, further including pre-processing (99) the measured blood glucose information by removing noise from the measured blood glucose information (Figs 14-18) (Cols 4-21), wherein the first feature value and the second feature value are extracted from the pre- processed blood glucose information (Figs 14-18) (Cols 4-21).
For claim 8, Malin discloses the method of predicting the blood glucose value according to claim 1, further comprising: calculating a prediction error from a difference between a predicted blood glucose value at a first prediction time and a blood glucose value actually measured at the first prediction time (150-154) (Figs 14-18) (Cols 4-21); and determining whether to re-learn the prediction model based on the prediction error (149) (Figs 14-18) (Cols 4-21).
For claim 9, Malin discloses the method of predicting the blood glucose value according to claim 8, wherein if the prediction error is greater than a threshold or a threshold ratio, it is determined that the prediction model is to be re-learned (149) (Figs 14-18) (Cols 4-21).
For claim 10, Malin discloses the method of predicting the biometric value according to claim 8, further comprising determining whether to re-generate the prediction model based on expression characteristics of the prediction error during a unit time (Figs 14-18) (Cols 4-21).
For claim 11, Malin discloses the method of predicting the blood glucose value according to claim 10, wherein the expression characteristics are at least one of a number of consecutive times of excess of the prediction error over the threshold or the threshold ratio during the unit time and a total number of times of excess of the prediction error over the threshold or the threshold ratio during the unit time (155) (Figs 14-18) (Cols 4-21).
For claim 12, Malin discloses the method of predicting the blood glucose value according to claim 8, wherein the re-learning of the prediction model or re-generating of the prediction model uses a subsequent data set generated from biometric information of the user measured up to current time except a previous data set which was used to create the prediction model (Figs 14-18) (Cols 4-21).
Response to Arguments
Applicant’s arguments with respect to newly amended claim(s) 1-3 and 8-12 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument because Malin is newly applied distinctly with regard to the newly amended claims. However, to the extent the arguments apply, they are addressed hereinbelow in the interest of compact prosecution.
Applicant's arguments filed 5/27/26 have been fully considered but they are not persuasive, wherein Applicant argues the following:
For the rejection under 35 U.S.C. § 101:
"Claims that are directed to improvements in computer functionality or other technology are not abstract." MPEP § 2106; see also Enfish, 822 F.3d at 1339, (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not an abstract idea); and Visual Memory LLC V. NVIDIA Corp., 867 F.3d 1253, 1259-60 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea).
Applicant respectfully asserts that independent claim 1 is explicitly directed to just such an improvement in glucose monitoring. Specifically, embodiments of the claimed invention are directed to a blood glucose value prediction method that can predict future blood glucose levels of a user using less memory and computation resources.
Conventional glucose prediction models used to predict future blood glucose levels are created using extensive blood glucose history information, meal history information, activity history, etc. This information may not only include the user, but also other surrounding users. As such, a large memory space is required to store the information needed to create the prediction model, and a large calculation is required to predict future blood glucose levels by applying signals from a user to the prediction model. The creation and modification of these prediction models and the prediction of future blood glucose levels using the prediction model must be done through a separate server, which requires the communication terminal must always communicate with the server to predict future blood glucose levels.
In contrast, embodiments of the independent claim provide a blood glucose value prediction method that can predict the future blood glucose level of a user with a relatively small memory and computational amount through a communication terminal (e.g., a smartphone). Embodiments further provide a prediction model which is personalized to a user based on biometric history information and provide a method for predicting future biometric values without access to a server. Embodiments also provide a blood glucose value prediction method that determines the expression characteristics of the prediction error by calculating the prediction error from the predicted biometric values and actual biometric values to accurately predict the future biometric values of a user by relearning or regenerating the prediction model according to the expression characteristics of the prediction error. Embodiments provide a personalized prediction model for a user using a first feature value extracted from preprocessed biometric information and a second feature value extracted from the time-calibrated biometric information and unit-calibrated biometric information and can provide a method for accurately predicting the biometric values of a user through the generated prediction model. Specifically, embodiments assign a value of a weight in inverse proportion to the rising speed when the blood glucose is rising and assigns a higher value in proportion to the decreasing speed when blood glucose is decreasing. See paragraphs [0018]-[0023] of the specification.
In view of the above, by assigning the weight as defined above, embodiments provide a prediction model which is personalized to a user that requires less data and may be used without accessing a server. As described in paragraphs [0083] and [0108], the blood glucose value can be predicted more accurately by more immediately reflecting the rapid change in blood glucose information. This is clearly an improvement in the technology of glucose monitoring. Therefore, Applicant respectfully asserts that the claims are directed to an "improvement in computer functionality or other technology." See, e.g., Enfish; see also MPEP § 2106.
Further, assuming arguendo that the amended claims somehow explicitly recite an enumerated abstract idea, Applicant respectfully asserts that such an idea is clearly integrated into a practical application. "A claim is not "directed to" a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception." Id. at page 13. Applicant respectfully asserts that, if the amended claims actually did explicitly recite a judicial exception (which they do not), the claims clearly integrate the judicial exception into a practical application.
Specifically, the amended independent claims require a device assigning a weight value in inverse proportion to the rising speed when the blood glucose is increasing and assigning a higher value in proportion to the decreasing speed when blood glucose is decreasing. Therefore, embodiments integrate the concepts therein into the practical application of using a device to monitor glucose levels in real time. The amended independent claim includes such a device and a concrete modeling operation that uses sensor-determined data. Such features are clearly more than a drafting attempt to monopolize a judicial exception.
"A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception." Id. Because the claims clearly integrate any possible judicial exception into the practical application of recognizing an appropriate speed limit for driver assistance, Applicant respectfully asserts that the claims are not "directed" an abstract idea.
For the rejection under 35 U.S.C. § 102:
Claims 1-12 stand rejected under § 102(a)(1) as being anticipated over U.S. Patent No. 6,280,381 ("Malin"). Claims 4-7 have been cancelled by way of this reply; therefore, this rejection is moot with respect to these claims. To the extent that this rejection may still apply to the amended claims, the rejection is respectfully traversed.
Embodiments of the claimed invention provide a method to calibrate unit discrepancy between the current value measured by the sensor (e.g., nA) and the actual blood glucose value (e.g., mg/dL). By dynamically assigning a weight to the calibration slope based on the direction and rate of change of the blood glucose information, embodiments improve the prediction accuracy.
Accordingly, amended independent claim 1 recites, in part, that "unit discrepancy included in pre-processed blood glucose information is calibrated before extracting the second feature value, and wherein the unit discrepancy is calibrated by assigning a weight based on a rising state or decreasing state of the blood glucose information, such that, when the blood glucose information indicates rising, the weight is assigned a low value in inverse proportion to the rising speed, and when the blood glucose information indicates decreasing, the weight is assigned a high value in proportion to the decreasing speed."
Malin fails to disclose or suggest at least the above-referenced limitations of amended independent claim 1.
Malin discloses predicting blood glucose from non-invasive absorbance spectra. Specifically, control step (99) in Malin performs a principal component analysis (PCA) and residual analysis on the measured non-invasive absorbance spectrum to detect spectral outliers. This is a quality control step for identifying anomalous measurements and bears no relation to unit discrepancy calibration. See Figure 10 of Malin. Malin also discloses pre-processing 5 (143) that clips the wavelength range of the measured absorbance spectrum to the 1100-1800 nm region. See Figure 15B of Malin. This is a spectral windowing step, which is fundamentally different from the unit discrepancy calibration of the claimed invention.
In view of the above, Malin fails to disclose or suggest the specific claimed method of calibrating unit discrepancy using dynamic weights based on the directional speed (rising VS. decreasing) of the blood glucose signal.
The Examiner respectfully disagrees and in response notes the following:
Regarding the 101:
In response to applicant's argument that the claimed invention is statutory, it is noted that the features upon which applicant relies (i.e., (i) “improvement in glucose monitoring. Specifically, embodiments of the claimed invention are directed to a blood glucose value prediction method that can predict future blood glucose levels of a user using less memory and computation resources.”, (ii) “blood glucose value prediction method that can predict the future blood glucose level of a user with a relatively small memory and computational amount through a communication terminal (e.g., a smartphone). Embodiments further provide a prediction model which is personalized to a user based on biometric history information and provide a method for predicting future biometric values without access to a server. Embodiments also provide a blood glucose value prediction method that determines the expression characteristics of the prediction error by calculating the prediction error from the predicted biometric values and actual biometric values to accurately predict the future biometric values of a user by relearning or regenerating the prediction model according to the expression characteristics of the prediction error. Embodiments provide a personalized prediction model for a user using a first feature value extracted from preprocessed biometric information and a second feature value extracted from the time-calibrated biometric information and unit-calibrated biometric information and can provide a method for accurately predicting the biometric values of a user through the generated prediction model. Specifically, embodiments assign a value of a weight in inverse proportion to the rising speed when the blood glucose is rising and assigns a higher value in proportion to the decreasing speed when blood glucose is decreasing.”, (iii) “a prediction model which is personalized to a user that requires less data and may be used without accessing a server”, (iv) “glucose monitoring”, and/or (v) “a device assigning a weight value in inverse proportion to the rising speed when the blood glucose is increasing and assigning a higher value in proportion to the decreasing speed when blood glucose is decreasing. Therefore, embodiments integrate the concepts therein into the practical application of using a device to monitor glucose levels in real time. The amended independent claim includes such a device and a concrete modeling operation that uses sensor-determined data.”) are not recited in the rejected claim(s) (emphasis added). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant’s arguments regarding the technological improvement and/or practical application integration are not commensurate in scope with the claimed invention, particularly as broadly as claimed.
Regarding the 102:
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “current value measured by the sensor (e.g., nA) and the actual blood glucose value (e.g., mg/dL). By dynamically assigning a weight to the calibration slope based on the direction and rate of change of the blood glucose information, embodiments improve the prediction accuracy”) are not recited in the rejected claim(s) (emphasis added). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
With respect to “wherein the unity discrepancy is calibrated (at 142,146) by assigning a weight based on a rising state or decreasing state of the blood glucose information, such that, when the blood glucose information indicates rising, the weight is assigned a low value in inverse proportion to the rising speed, and when the blood glucose information indicates decreasing, the weight is assigned a high value in proportion to the decreasing speed”, Malin in Figs 15-16 shows and describes as two, of many, membership function examples (gender) accounting for positioning on rising and falling gaussian distributions, while Col 18 li 57-63 indicate other sub-sets are used for accurate prediction including “blood glucose information” parameters, particularly when stating “The membership functions described have been designed for a specific population of subjects and cannot be generalized to all potential individuals. The invention, however, covers the arbitrary use of membership functions to assign a degree of membership in a given class to a subject for blood analyte prediction.
(174) Other sub-sets, for example, include the level of hydration, skin thickness, thickness of adipose tissue, volume fraction of blood in tissue, blood pressure, and hematocrit levels. The number of sub-sets per general set can also be increased arbitrarily depending on the necessarily level of discrimination for the accurate prediction of blood analytes.”.
Conclusion
THIS ACTION IS MADE FINAL. 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 Jeffrey G. Hoekstra whose telephone number is (571)272-7232. The examiner can normally be reached Monday through Thursday from 5am-3pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles A. Marmor II can be reached at (571)272-4730. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Jeffrey G. Hoekstra
Primary Examiner
Art Unit 3791
/JEFFREY G. HOEKSTRA/ Primary Examiner, Art Unit 3791
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