DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined
under the first inventor to file provisions of the AIA .
Rejections - 35 USC § 112
2. 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.
3. Claims 18-20 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 pre-AIA the applicant regards as the invention.
Regarding claims 18 and 20, the recitation of the phrases "normalizes/scales” and “normalized/scaled” renders the claims indefinite, because the literal definition of "/" is unknown. It is unclear whether it refers to an "and", "or" or something else.
Claim 19, which depends on claim 15, recites “the at least further function”. There is insufficient antecedent basis for this limitation in the claims.
Therefore, the examiner comprehends the claims based on her best interpretations to these phrases.
Claim Rejections - 35 USC § 101
4. 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 therefore, subject to the conditions and requirements of this title.
5. Claims 25 and 26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 25 and 26 drawn to a storage medium carrying processor executable computer program instructions itself is directed non-statutory subject matter, since the BRI of machine readable media can encompass non-statutory transitory forms of signal transmission. The Federal Circuit court held that “a transitory, propagating signal does not fall within any statutory category [of 35 USC 101].” See MPEP 2106.03
Claim Rejections - 35 USC § 102
6. 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; or
(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.
7. Claims 15-28 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chapman et al. (US 20220225901 A1).
Regarding claims 15 and 24-26, Chapman discloses a data processing system and a computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the following method (Abstract; para. 0033, 0076) including the steps of: providing a plurality of sensors (para. 0090); determining data (e.g., the production data) that have been already collected during the manufacturing and/or compensating of the sensor or the plurality of sensors (para. 0008, 0010, 0023, 0093-0094, 0097-0098); providing the determined data to a neural network configured to determine compensation coefficients (para. 0054, 0093: “The calibration process can use the production data to predict various performance metrics about each microsensor and set corresponding operational parameters of the biosensor accordingly (e.g., to account for sensor sensitivity, drift, background current, gain, etc.)”, “the calibration process includes one or more artificial intelligence and/or machine learning models … to predict the performance metrics based on a collection of the production data”); saving the determined compensation coefficients within the sensor so that the sensor can output compensated sensor values with the help of the determined compensation coefficients (para. 0093: “the predicted performance and/or the calibration adjustments metrics are stored on a memory device … and included in the biosensor and/or biosensor component”; para. 0109: “each biosensor can include a memory device and/or a unique identifier. The biomonitoring system can use the projected performance to adjust operation parameters and/or an interpretation of signals received from the biosensor during operation”).
Regarding claims 16-17, Chapman discloses: wherein the compensation coefficients are determined for a given first function (e.g., ordinary least squares regression, linear regression, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing) via the neuronal network (para. 0054); wherein the given first function is a polynomial function (para. 0054: “linear regression” encompasses a polynomial function).
Regarding claims 18 and 20, Chapman discloses: wherein at least a further function is given, which normalizes/scales a measuring raw value of the sensor to a normalized/scaled measuring value (para. 0061-0062); wherein normalized/scaled measuring values of the sensor are calculated with the help of the further given function and/or the further coefficients and wherein the calculated normalized/scaled measuring values are used as data provided to the neuronal network for determining the compensation coefficients (para. 0062, 0093).
Regarding claim 19, Chapman discloses: wherein further coefficients for a further function (e.g., predict various performance metrics about each microsensor) are determined using a further neural network (para. 0054, 0093).
Regarding claim 21, Chapman discloses: wherein data is used that was determined during previous manufacturing steps of the sensor (para. 0008, 0010, 0023, 0093-0094, 0097-0098).
Regarding claim 22, Chapman discloses: wherein the neuronal network is trained before the compensation coefficients are determined (para. 0146, 0164).
Regarding claim 23, Chapman discloses: wherein for training the neuronal network historical data (e.g., past production data) from the entire production line are used (para. 0146, 0353; see also discussion of “production data”).
Regarding claims 27-28, Chapman discloses a sensor comprising a memory having saved coefficients and a sensor adapted to output a compensated sensor value using the coefficients (para. 0093: “the predicted performance and/or the calibration adjustments metrics are stored on a memory device … and included in the biosensor and/or biosensor component”; para. 0109: “each biosensor can include a memory device and/or a unique identifier. The biomonitoring system can use the projected performance to adjust operation parameters and/or an interpretation of signals received from the biosensor during operation”), wherein the coefficients are determined using the following method steps: providing a plurality of sensors (para. 0090); determining data (e.g., the production data) that have been already collected during the manufacturing and/or compensating of the sensor or the plurality of sensors (para. 0008, 0010, 0023, 0093-0094, 0097-0098); providing the determined data to a neural network configured to determine compensation coefficients (para. 0054, 0093); saving the determined compensation coefficients within the sensor so that the sensor can output compensated sensor values with the help of the determined compensation coefficients (para. 0093, 0109).
Contact Information
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIUQIN SUN whose telephone number is (571)272-2280. The examiner can normally be reached 9:30am-6:00pm.
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, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/X.S/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights