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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/19/2025 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/08/2026 was filed after the mailing date of the Final Rejection on 09/24/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Applicant’s amendment filed 12/19/2025 has been entered. Claims 1-20 remain pending.
Response to Arguments
Applicant's arguments, see Pages 11-14, filed 12/19/2025 with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
Applicant argues on Page 12 that the microwave radiometer and the GNSS receiver are not generic components and that the hardware configuration is technically rooted in meteorological measurement systems and cannot be categorized as merely generic computing components. Applicant argues on Page 12 that the recited machine learning model training is not a “mathematical concept” under the 2025 USPTO AI Memorandum. Applicant argues on Page 12-13 that conventional microwave radiometers require periodic calibration using liquid nitrogen and that the claimed system uses machine learning to fuse GNSS-derived atmospheric delay information with multi-frequency radiometer spectral intensity patterns, and thus as a result the calibration of the radiometer is accomplished through this pairing rather than with liquid nitrogen calibration.
Examiner respectfully disagrees. Examiner notes that the Final Rejection on 09/24/2025 did not detail the microwave radiometer or the GNSS receiver as being generic computing components. The additional limitation of "a microwave radio meter configured to receive radio waves" is considered to be well-understood, routine, and conventional activity in the art. This is evidenced by Solheim (US20110218734) in [0058] and Nelson (US4873481) on Column 1, Lines 1-40. Similarly, the additional element of a GNSS receiver configured to receive a GNSS signal are considered is considered to be well understood, routine, and conventional activity in the art. This is evidenced by Wu (CN109001382A) in [0006] and Yao (CN108387169A) in [0009]. The utilization of these devices to acquire data relating to weather is well understood, routine, and convention in the art as detailed in Solheim, Nelson, Wu, and Yao (as detailed above). Furthermore, as the GNSS receiver and the microwave radio meter are detailed as sensors acquiring data, the operation thereof is considered to be necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. acquiring data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
The 2025 USPTO AI Memorandum does not state that machine learning model training is not a “mathematical concept”. Furthermore, the 2025 USPTO AI Memorandum cites an example in Claim 2 of Example 47 as a machine learning training with a model as a mathematical concept. Furthermore, The MPEP recites in 2106.04(a)(2)(I) that “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989).” The MPEP further recites in 2106.04(a)(2)(I)(C) that “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Claims 1 and 17 detail the limitation of “perform machine learning to train an estimation model using, as training data, a pair of the radio wave intensities of the plurality of frequencies and the precipitable water vapor at each of the plurality of time points in the training period”. The claim amendment is interpreted under broadest reasonable interpretation in light of the specification. As the claims are detailing the estimation model as taking a numerical input (radio wave intensities of the plurality of frequencies) and outputting a numerical output (precipitable water vapor), the estimation model is performing a mathematical calculation. The specification details in [0036] the estimation model by what is listed as Formula 1. Thus, the claims recite an abstract concept of a mathematical calculation.
In regards to Applicant’s argument that conventional microwave radiometers require periodic calibration using liquid nitrogen and the claimed system does not perform calibration of the radiometer through the pairing of the radio wave intensities and precipitable water vapor, Examiner points out that it is well understood in the art that using liquid nitrogen is only one of the means to perform calibration of a microwave radiometer. Han (Yong Han et. al., “Analysis and Improvement of Tipping Calibration for Ground-Based Microwave Radiometers”, 05/03/2000, IEEE Transactions on Geoscience and Remote Sensing, Volume 38, No 3, https://radiometrics.com/wp-content/uploads/2021/10/Han_TGRS_2000.pdf) details the tipping curve calibration method which is an alternative method for the calibration of microwave radiometers that do not utilize liquid nitrogen. Kuchler (N. Kuchler et. al., “Calibration ground-based microwave radiometers: Uncertainty and drifts”, 03/31/2016, Radio Science, Volume 51, Issue 4) further details the tipping curve calibration method that is an alternative to utilizing liquid nitrogen. Additionally, the MPEP details in 2106.05(a) that “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)).” The evaluation of the integration of the judicial exception into a practical application relies on the evaluation of the additional elements. The additional elements to the claims are directed towards two sensors (microwave radio meter and GNSS receiver) which acquire data (radio wave intensities and precipitable water from a calculation) that are considered to be well understood, routine, and convention in the art, and to further to be mere data gathering. The other additional element of the claim is generic processing circuitry. Generic computer elements are not considered significantly more than the abstract idea and do not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94. Therefore, the claims do not detail any additional elements that integrate the judicial exception into a practical application. Thus as the additional elements are generic computer elements with well understood, convention, and routine sensors utilized in the art, the claim does not integrate the judicial exception into a practical application.
Applicant argues on Page 14 that for dependent Claims 2-4 the standardization process and PCA-based dimensionality reduction are used to remove device-dependent bias and variability, and extract representative features and that these operations enable the model to train stably on multi-frequency radiometer and GNSS-derived data under real deployment conditions and help reduce computational load during training. Applicant argues that these steps contribute to the practical application of the system for the stated physical measurement task.
Examiner respectfully disagrees. Claims 2-4 detail the standardization process with principal component analysis based dimensionality reduction. These limitations fall under the mathematical concept abstract grouping as the limitation is reciting a specific statistical mathematical calculation being performed.
Applicant’s arguments, see Pages 14-15, filed 12/19/2025, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly discovered prior art Okumura (WO2020230501A1) under 35 U.S.C. 102(a)(2).
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 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 17 details the limitation “perform machine learning to train an estimation model using, as training data, a pair of the radio wave intensities of the plurality of frequencies and the precipitable water vapor at each of the plurality of time points in the training period”. It is not clear nor distinct whether the “pair” refers to a set of radio wave intensities or a set consisting of the radio wave intensities and the precipitable water vapor.
Claims 6 and 18 details similarly details the limitation with “the estimation model was subjected to machine learning using a pair of training radio wave intensities of a plurality of training frequencies and training precipitable water vapor at each of a plurality of time points in a training period”. The limitation is interpreted in the same way as the pair in Claims 1 and 17.
Examiner interprets the limitation as the pair is the set of radio wave intensities and precipitable water vapor.
Claims 2-5 and 1-13 are rejected due to dependence on Claim 1
Claims 7-10 and 14-16 are rejected due to dependence on Claim 6
Claim 19 is rejected due to dependence on Claim 17
Claim 20 is rejected due to dependence on Claim 18.
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 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing abstract steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
1. (Currently Amended) A learning system of a precipitable water vapor estimation model, comprising:
a microwave radio meter configured to receive radio waves;
a GNSS receiver configured to receive a GNSS signal;
processing circuitry configured to:
acquire radio wave intensities of a plurality of frequencies among the radio waves received from the microwave radiometer at each of a plurality of time points in a training period;
acquire a precipitable water vapor calculated based on an atmospheric delay of the GNSS signal received from the GNSS receiver at each of the plurality of time points in the training period; and
perform machine learning to train an estimation model using, as training data, a pair of the radio wave intensities of the plurality of frequencies and the precipitable water vapor at each of the plurality of time points in the training period,
wherein the estimation model being configured to receive radio wave intensities of a plurality of frequencies among radio waves received from the microwave radiometer at a designated time point as input data and to output an estimated precipitable water vapor.
6. (Currently Amended) A precipitable water vapor estimation system comprising:
a microwave radio meter configured to receive radio waves; and
processing circuitry configured to:
acquire radio wave intensities of a plurality of frequencies among radio waves received from the microwave radiometer at a designated time point; and
input the acquired radio wave intensities of the plurality of frequencies at the designated time point as input data to an estimation model and receive a precipitable water vapor output from the estimation model,
wherein the estimation model was subjected to machine learning using a pair of training radio wave intensities of a plurality of training frequencies and training precipitable water vapor at each of a plurality of time points in a training period
17. (Currently Amended) A learning method of a precipitable water vapor estimation model, comprising:
acquiring radio wave intensities of a plurality of frequencies among radio waves received by a microwave radiometer at each of a plurality of time points in a training period;
acquiring a precipitable water vapor calculated based on an atmospheric delay of a GNSS signal received by a GNSS receiver at each of a plurality of time points in a training period; and
perform machine learning to train an estimation model using, as training data, a pair of the radio wave intensities of the plurality of frequencies and the precipitable water vapor at each of the plurality of time points in the training period,
wherein the estimation model being configured to receive radio wave intensities of a plurality of frequencies among radio waves received from the microwave radiometer at a designated time point as an input and to output an estimated precipitable water vapor
18. (Currently Amended) A precipitable water vapor estimation method comprising:
acquiring radio wave intensities of a plurality of frequencies among radio waves received by a microwave radiometer at a designated time point; and
inputting the acquired radio wave intensities of the plurality of frequencies at the designated time point as input data to an estimation model and receiving a precipitable water vapor output from the estimation model,
wherein the estimation model was subjected to machine learning using a pair of training radio wave intensities of a plurality of training frequencies and training precipitable water vapor at each of a plurality of time points in a training period
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematical calculations.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea. Claims 1, 6, and 17-18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a step of acquiring radio wave intensities and precipitable water vapor is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. acquiring data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). The processing circuitry is interpreted under broadest reasonable interpretation to be a generic computer processor. Generic computer elements are not considered significantly more than the abstract idea and do not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94. The additional elements of a microwave radio meter configured to receive radio waves as detailed in Claim 1 and 6 is considered to be a well understood, routine, and conventional activity. This is evidenced by Solheim (US20110218734) in [0058] and Nelson (US4873481) on Column 1, Lines 1-40. The additional element of a GNSS receiver configured to receive a GNSS signal are considered is considered to be well understood, routine, and conventional activity. This is evidenced by Wu (CN109001382A) in [0006] and Yao (CN108387169A) in [0009]. Furthermore, as the GNSS receiver and the microwave radio meter are detailed as sensors acquiring data, the operation thereof is considered to be necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. acquiring data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
Claims 2-5, 7-16, and 19-20 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
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.
Claims 1, 6, and 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Okumura (WO2020230501A1).
The applied reference has a common assignee with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed; or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement.
In regards to Claim 1, Okumura teaches “a microwave radio meter configured to receive radio waves (“acquire a water vapor index calculated based on radio wave intensities of at least two frequencies among a plurality of radio waves received by a microwave radiometer” – [0007]);
a GNSS receiver configured to receive a GNSS signal (“acquire a GNSS precipitable water amount calculated based on an atmospheric delay of a GNSS signal received by a GNSS receiver” – [0007]);
processing circuitry (processing circuitry – [0007]) configured to:
acquire radio wave intensities of a plurality of frequencies among the radio waves received from the microwave radiometer at each of a plurality of time points in a training period (“acquire a water vapor index calculated based on radio wave intensities of at least two frequencies [i.e. plurality of frequencies] among a plurality of radio waves received by a microwave radiometer” – [0007]; “The predetermined period, i.e. training period, starts from a past time point with reference to the measurement time point of the microwave radiometer used for calculating the precipitable water amount; and the predetermined period is a sliding window which slides as the measurement time point of the microwave radiometer changes” – [0008]);
acquire a precipitable water vapor calculated based on an atmospheric delay of the GNSS signal received from the GNSS receiver at each of the plurality of time points in the training period (“acquire a GNSS precipitable water amount calculated based on an atmospheric delay of a GNSS signal received by a GNSS receiver” – [0007]; “The conversion equation is an approximate equation generated by fitting the water vapor index and the GNSS precipitable water amount at a plurality of time points in the predetermined period, i.e. training period” – [0010]); and
perform machine learning to train an estimation model using, as training data, a pair of the radio wave intensities of the plurality of frequencies and the precipitable water vapor at each of the plurality of time points in the training period (“In the present embodiment, an approximate expression as a conversion expression is calculated by fitting the water vapor index and the GNSS precipitable water amount at a plurality of time points, and the precipitable water amount is calculated from the water vapor index based on the conversion expression. That is, the water vapor index and the GNSS precipitable water amount at a plurality of points in time are used as a learning data [i.e. training data], a learning model [i.e. estimation model] for outputting the corresponding precipitable water amount when the water vapor index is inputted is generated as the correlation data D 1 , and the precipitable water amount is outputted from the water vapor index by using the correlation data D 1” – [0030]),
wherein the estimation model being configured to receive radio wave intensities of a plurality of frequencies among radio waves received from the microwave radiometer at a designated time point as input data and to output an estimated precipitable water vapor (learning model, i.e. estimation model, receives the water vapor index [value calculated from the radio wave intensities] and outputs the precipitable water – [0030]).”
In regards to Claim 6, Okumura teaches “a microwave radio meter configured to receive radio waves (“acquire a water vapor index calculated based on radio wave intensities of at least two frequencies among a plurality of radio waves received by a microwave radiometer” – [0007]); and
processing circuitry (processing circuitry – [0007]) configured to:
acquire radio wave intensities of a plurality of frequencies among radio waves received from the microwave radiometer at a designated time point (“acquire a water vapor index calculated based on radio wave intensities of at least two frequencies [i.e. plurality of frequencies] among a plurality of radio waves received by a microwave radiometer” – [0007]; “The predetermined period starts from a past time point with reference to the measurement time point, i.e. designated time point, of the microwave radiometer used for calculating the precipitable water amount; and the predetermined period is a sliding window which slides as the measurement time point of the microwave radiometer changes” – [0008]); and
input the acquired radio wave intensities of the plurality of frequencies at the designated time point as input data to an estimation model and receive a precipitable water vapor output from the estimation model (“In the present embodiment, an approximate expression as a conversion expression is calculated by fitting the water vapor index and the GNSS precipitable water amount at a plurality of time points, and the precipitable water amount is calculated from the water vapor index based on the conversion expression. That is, the water vapor index and the GNSS precipitable water amount at a plurality of points in time are used as a learning data, a learning model [i.e. estimation model] for outputting the corresponding precipitable water amount when the water vapor index [water vapor index is calculated based on the radio wave intensities as detailed above] is inputted is generated as the correlation data D 1 , and the precipitable water amount is outputted from the water vapor index by using the correlation data D 1” – [0030]),
wherein the estimation model was subjected to machine learning using a pair of training radio wave intensities of a plurality of training frequencies and training precipitable water vapor at each of a plurality of time points in a training period (“The predetermined period, i.e. training period, starts from a past time point with reference to the measurement time point of the microwave radiometer used for calculating the precipitable water amount; and the predetermined period is a sliding window which slides as the measurement time point of the microwave radiometer changes” – [0008]; “In the present embodiment, an approximate expression as a conversion expression is calculated by fitting the water vapor index and the GNSS precipitable water amount at a plurality of time points, and the precipitable water amount is calculated from the water vapor index based on the conversion expression. That is, the water vapor index and the GNSS precipitable water amount at a plurality of points in time are used as a learning data [i.e. training data], a learning model [i.e. estimation model] for outputting the corresponding precipitable water amount when the water vapor index is inputted is generated as the correlation data D 1 , and the precipitable water amount is outputted from the water vapor index by using the correlation data D 1” – [0030]).”
In regards to Claim 17, Okumura teaches “acquiring radio wave intensities of a plurality of frequencies among radio waves received by a microwave radiometer at each of a plurality of time points in a training period (“acquire a water vapor index calculated based on radio wave intensities of at least two frequencies [i.e. plurality of frequencies] among a plurality of radio waves received by a microwave radiometer” – [0007]; “The predetermined period, i.e. training period, starts from a past time point with reference to the measurement time point of the microwave radiometer used for calculating the precipitable water amount; and the predetermined period is a sliding window which slides as the measurement time point of the microwave radiometer changes” – [0008]);
acquiring a precipitable water vapor calculated based on an atmospheric delay of a GNSS signal received by a GNSS receiver at each of a plurality of time points in a training period (“acquire a GNSS precipitable water amount calculated based on an atmospheric delay of a GNSS signal received by a GNSS receiver” – [0007]; “The conversion equation is an approximate equation generated by fitting the water vapor index and the GNSS precipitable water amount at a plurality of time points in the predetermined period, i.e. training period” – [0010]); and
perform machine learning to train an estimation model using, as training data, a pair of the radio wave intensities of the plurality of frequencies and the precipitable water vapor at each of the plurality of time points in the training period (“In the present embodiment, an approximate expression as a conversion expression is calculated by fitting the water vapor index and the GNSS precipitable water amount at a plurality of time points, and the precipitable water amount is calculated from the water vapor index based on the conversion expression. That is, the water vapor index and the GNSS precipitable water amount at a plurality of points in time are used as a learning data [i.e. training data], a learning model [i.e. estimation model] for outputting the corresponding precipitable water amount when the water vapor index is inputted is generated as the correlation data D 1 , and the precipitable water amount is outputted from the water vapor index by using the correlation data D 1” – [0030]),
wherein the estimation model being configured to receive radio wave intensities of a plurality of frequencies among radio waves received from the microwave radiometer at a designated time point as an input and to output an estimated precipitable water vapor (learning model, i.e. estimation model, receives the water vapor index [value calculated from the radio wave intensities] and outputs the precipitable water – [0030]).”
In regards to Claim 18, Okumura teaches “acquiring radio wave intensities of a plurality of frequencies among radio waves received by a microwave radiometer at a designated time point (“acquire a water vapor index calculated based on radio wave intensities of at least two frequencies [i.e. plurality of frequencies] among a plurality of radio waves received by a microwave radiometer” – [0007]; “The predetermined period starts from a past time point with reference to the measurement time point, i.e. designated time point, of the microwave radiometer used for calculating the precipitable water amount; and the predetermined period is a sliding window which slides as the measurement time point of the microwave radiometer changes” – [0008]); and
inputting the acquired radio wave intensities of the plurality of frequencies at the designated time point as input data to an estimation model and receiving a precipitable water vapor output from the estimation model (“In the present embodiment, an approximate expression as a conversion expression is calculated by fitting the water vapor index and the GNSS precipitable water amount at a plurality of time points, and the precipitable water amount is calculated from the water vapor index based on the conversion expression. That is, the water vapor index and the GNSS precipitable water amount at a plurality of points in time are used as a learning data, a learning model [i.e. estimation model] for outputting the corresponding precipitable water amount when the water vapor index [water vapor index is calculated based on the radio wave intensities as detailed above] is inputted is generated as the correlation data D 1 , and the precipitable water amount is outputted from the water vapor index by using the correlation data D 1” – [0030]),
wherein the estimation model was subjected to machine learning using a pair of training radio wave intensities of a plurality of training frequencies and training precipitable water vapor at each of a plurality of time points in a training period (“The predetermined period, i.e. training period, starts from a past time point with reference to the measurement time point of the microwave radiometer used for calculating the precipitable water amount; and the predetermined period is a sliding window which slides as the measurement time point of the microwave radiometer changes” – [0008]; “In the present embodiment, an approximate expression as a conversion expression is calculated by fitting the water vapor index and the GNSS precipitable water amount at a plurality of time points, and the precipitable water amount is calculated from the water vapor index based on the conversion expression. That is, the water vapor index and the GNSS precipitable water amount at a plurality of points in time are used as a learning data [i.e. training data], a learning model [i.e. estimation model] for outputting the corresponding precipitable water amount when the water vapor index is inputted is generated as the correlation data D 1 , and the precipitable water amount is outputted from the water vapor index by using the correlation data D 1” – [0030]).”
In regards to Claim 19, Okumura discloses the claimed invention as detailed above. Okumura further teaches “A non-transitory computer-readable medium having stored thereon computer-executable instructions which, when executed by a computer, cause the computer to: execute the learning method of a precipitable water vapor estimation model according to claim 17 (“The program of this embodiment is a program for causing a computer (one or more processors) to execute the method. The computer readable temporary recording medium according to the present embodiment stores the program.” – [0045]).”
In regards to Claim 20, Okumura discloses the claimed invention as detailed above. Okumura further teaches “A non-transitory computer-readable medium having stored thereon computer-executable instructions which, when executed by a computer, cause the computer to: execute the precipitable water vapor estimation method according to claim 18 (“The program of this embodiment is a program for causing a computer (one or more processors) to execute the method. The computer readable temporary recording medium according to the present embodiment stores the program.” – [0045]).”
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-3, 7-8, 12, and 15 rejected under 35 U.S.C. 103 as being unpatentable over Okumura (WO2020230501A1) in view of Islam (Tanvir Islam et al, “Variational Bayes and the Principal Component Analysis Coupled with Bayesian Regulation Backpropagation Network to Retrieve Total Precipitable Water (TPW) From GCOM-W1/AMSR2”, October 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Volume 8, Number 10).
In regards to Claim 2, Okumura discloses the claimed invention as detailed above in Claim 1. Okumura is silent with regards to the language of “wherein the processing circuitry is further configured to: perform a dimension reduction process on the radio wave intensities of the plurality of frequencies acquired at each of the plurality of time points in the training period to generate input data of the training data.”
Islam teaches “wherein the processing circuitry is further configured to: perform a dimension reduction process on the radio wave intensities of the plurality of frequencies acquired at each of the plurality of time points in the training period to generate input data of the training data (algorithm named Bayes Principal components Backpropagation Network (BBN) for the retrieval of the TPW from the GCOMW-1/AMSR2 instrument where the algorithm is tested over the ocean surface and applicable for both clear and cloudy conditions, where the algorithm is developed through principal component analysis and trained with Bayesian regulation backpropagation network to retrieve the TPW, where the inversion is done in a physical manner as the algorithm employs a radiative transfer model by constructing a training database of simulated radiances to facilitate the physical inversion of the radiances representing the physical state of the atmosphere and further employs a variational Bayes bias correction technique and employs a random forest algorithm for the purpose of screening precipitation scenes – Page 4819 Right Column and Page 4820 Left Column; PCA transformation detailed in section C on Page 4822 with details towards the dimensions of the problems being reduced).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Okumura to incorporate the teaching of Islam to employ Principal Component Analysis to the data. By employing principal component analysis this is an improvement in the evaluation of the total precipitable water in the atmosphere from the microwave radiometer measurement system.
In regards to Claim 3, Okumura in view of Islam discloses the claimed invention as detailed above. Okumura is silent with regards to the language of “wherein the processing circuitry is further configured to: select a particular number of principal components from a first principal component onward as the input data of the training data, based on the dimension reduction process according to principal component analysis.”
Islam further teaches “wherein the processing circuitry is further configured to: select a particular number of principal components from a first principal component onward as the input data of the training data, based on the dimension reduction process according to principal component analysis (PCA transformation – processing step in the BPBN retrieval algorithm is to transform the brightness temperature to principal components so that the dimensions are reduced, and the PCA transformation is carried out – Page 4822, Right Column).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Okumura to incorporate the teaching of Islam to employ Principal Component Analysis to the data. By employing principal component analysis this is an improvement in the evaluation of the total precipitable water in the atmosphere from the microwave radiometer measurement system.
In regards to Claim 7, Okumura discloses the claimed invention as detailed above in Claim 6. Okumura is silent with regards to the language of “the processing circuitry is further configured to: perform a dimension reduction process on the radio wave intensities of the plurality of frequencies to generate the input data.”
Islam teaches “the processing circuitry is further configured to: perform a dimension reduction process on the radio wave intensities of the plurality of frequencies to generate the input data (algorithm named Bayes Principal components Backpropagation Network (BBN) for the retrieval of the TPW from the GCOMW-1/AMSR2 instrument where the algorithm is tested over the ocean surface and applicable for both clear and cloudy conditions, where the algorithm is developed through principal component analysis and trained with Bayesian regulation backpropagation network to retrieve the TPW, where the inversion is done in a physical manner as the algorithm employs a radiative transfer model by constructing a training database of simulated radiances to facilitate the physical inversion of the radiances representing the physical state of the atmosphere and further employs a variational Bayes bias correction technique and employs a random forest algorithm for the purpose of screening precipitation scenes – Page 4819 Right Column and Page 4820 Left Column; PCA transformation detailed in section C on Page 4822 with details towards the dimensions of the problems being reduced).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Okumura to incorporate the teaching of Islam to employ Principal Component Analysis to the data. By employing principal component analysis this is an improvement in the evaluation of the total precipitable water in the atmosphere from the microwave radiometer measurement system.
In regards to Claim 8, Okumura in view of Islam discloses the claimed invention as detailed above. Okumura is silent with regards to the language of “wherein the processing circuitry is further configured to: select a particular number of principal components from a first principal component onward as the input data based on the dimension reduction process according to principal component analysis.”
Islam further teaches “wherein the processing circuitry is further configured to: select a particular number of principal components from a first principal component onward as the input data based on the dimension reduction process according to principal component analysis (PCA transformation – processing step in the BPBN retrieval algorithm is to transform the brightness temperature to principal components so that the dimensions are reduced, and the PCA transformation is carried out – Page 4822, Right Column).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Okumura to incorporate the teaching of Islam to employ Principal Component Analysis to the data. By employing principal component analysis this is an improvement in the evaluation of the total precipitable water in the atmosphere from the microwave radiometer measurement system.
In regards to Claims 12 and 15, Okumura discloses the claimed invention as detailed above. Okumura further teaches “wherein the processing circuitry is further configured to: acquire radio wave intensities of N different frequencies, where N is a natural number greater than or equal to 3 (“The water vapor index acquisition module 41 shown in FIG. 1 acquires a water vapor index calculated based on radio wave intensities of at least two frequencies among a plurality of radio waves received by a microwave radiometer 3 . Although it is considered that there are various methods for calculating the water vapor index, in this embodiment, as shown in FIG. 3, a peak of the intensity of radio waves emitted from water vapor and cloud water in the sky is 22 GHz. In order to remove an amount of cloud water contained in the radio wave of 22 GHz, a cloud water amount calculation module 40 for calculating the amount of cloud water at 22 GHz based on the radio wave intensity of 26.5 GHz is provided. The water vapor index is calculated by subtracting the amount of cloud water at 22 GHz from the radio wave intensity at 22 GHz. In the present embodiment, p(f) is the received intensity of the microwave radiometer 3 , and f is the frequency. As shown in FIG. 3, a model af2 of the amount of cloud water is generated based on the reception intensity p (26.5 GHz) of 26.5 GHz. That is, the constant a is determined so that p (26.5 GHz)=af2 . The water vapor index is calculated as p (f)−af2 =p (22 GHz)−a (22 GHz)2 . The water vapor index acquired by the water vapor index acquisition module 41 is stored in a storage module 45 as a time-series data of the water vapor index” – [0022], Figure 3; Figure 3 shows more than 3 frequencies).”
Okumura is silent with regards to the explicit language of “dimensionally reduce the radio wave intensities of the N frequencies to the input data having a number smaller than N”
Islam further teaches “dimensionally reduce the radio wave intensities of the N frequencies to the input data having a number smaller than N (PCA transform reduces the dimensions of the problem – Page 4822, Right Column, Section C)”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Okumura to incorporate the teaching of Islam to employ Principal Component Analysis to the data. By employing principal component analysis this is an improvement in the evaluation of the total precipitable water in the atmosphere from the microwave radiometer measurement system.
Claims 4-5 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Okumura in view of Islam as applied to claims 3 and 8 above, and further in view of Elkabetz (US20190339416).
In regards to Claim 4, Okumura in view of Islam discloses the claimed invention as detailed above. Okumura is silent with regards to the language of “wherein the processing circuitry is further configured to: perform a standardization process on the radio wave intensities of the plurality of frequencies acquired at the plurality of time points in the training period before the dimension reduction process.”
Elkabetz further teaches “wherein the processing circuitry is further configured to: perform a standardization process on the radio wave intensities of the plurality of frequencies at the plurality of time points before the dimension reduction process (information collection is performed and of the collected data, normalizing, i.e. standardization process, is performed on the data – [0053]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Okumura in view of Islam to incorporate the further teaching of Elkabetz to normalize the data. By normalizing data this is an improvement to the sensing and determination of current weather phenomena and generation of accurate weather and precipitation forecasts.
In regards to Claims 5 and 10, Okumura in view of Islam and Elkabetz discloses the claimed invention as detailed above. Okumura further teaches “wherein the processing circuitry is further configured to: acquire radio wave intensities of N different frequencies, where N is a natural number greater than or equal to 3 (“The water vapor index acquisition module 41 shown in FIG. 1 acquires a water vapor index calculated based on radio wave intensities of at least two frequencies among a plurality of radio waves received by a microwave radiometer 3 . Although it is considered that there are various methods for calculating the water vapor index, in this embodiment, as shown in FIG. 3, a peak of the intensity of radio waves emitted from water vapor and cloud water in the sky is 22 GHz. In order to remove an amount of cloud water contained in the radio wave of 22 GHz, a cloud water amount calculation module 40 for calculating the amount of cloud water at 22 GHz based on the radio wave intensity of 26.5 GHz is provided. The water vapor index is calculated by subtracting the amount of cloud water at 22 GHz from the radio wave intensity at 22 GHz. In the present embodiment, p(f) is the received intensity of the microwave radiometer 3 , and f is the frequency. As shown in FIG. 3, a model af2 of the amount of cloud water is generated based on the reception intensity p (26.5 GHz) of 26.5 GHz. That is, the constant a is determined so that p (26.5 GHz)=af2 . The water vapor index is calculated as p (f)−af2 =p (22 GHz)−a (22 GHz)2 . The water vapor index acquired by the water vapor index acquisition module 41 is stored in a storage module 45 as a time-series data of the water vapor index” – [0022], Figure 3; Figure 3 shows more than 3 frequencies).”
Okumura is silent with regards to the explicit language of “dimensionally reduce the radio wave intensities of the N frequencies to the input data having a number smaller than N”
Islam further teaches “dimensionally reduce the radio wave intensities of the N frequencies to the input data having a number smaller than N (PCA transform reduces the dimensions of the problem – Page 4822, Right Column, Section C)”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Okumura in view of Islam and Elkabetz to incorporate the further teaching of Islam to employ Principal Component Analysis to the data. By employing principal component analysis this is an improvement in the evaluation of the total precipitable water in the atmosphere from the microwave radiometer measurement system.
In regards to Claim 9, Okumura in view of Islam discloses the claimed invention as detailed above. Okumura is silent with regards to the language of “wherein the processing circuitry is further configured to: perform a standardization process on the radio wave intensities of the plurality of frequencies using a predetermined standardization parameter before the dimension reduction process.”
Elkabetz teaches “wherein the processing circuitry is further configured to: perform a standardization process on the radio wave intensities of the plurality of frequencies using a predetermined standardization parameter before the dimension reduction process (information collection is performed and of the collected data, normalizing, i.e. standardization process, is performed on the data – [0053]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Okumura in view of Islam to incorporate the further teaching of Elkabetz to normalize the data. By normalizing data this is an improvement to the sensing and determination of current weather phenomena and generation of accurate weather and precipitation forecasts.
Claims 11 and 14 rejected under 35 U.S.C. 103 as being unpatentable over Okumura (WO2020230501A1) in view of Elkabetz (US20190339416).
In regards to Claim 11, Okumura discloses the claimed invention as detailed above in Claim 1. Okumura is silent with regards to the language of “wherein the processing circuitry is further configured to: perform a standardization process on the radio wave intensities of the plurality of frequencies at the plurality of time points before a dimension reduction process.”
Elkabetz further teaches “wherein the processing circuitry is further configured to: perform a standardization process on the radio wave intensities of the plurality of frequencies at the plurality of time points before the dimension reduction process (information collection is performed and of the collected data, normalizing, i.e. standardization process, is performed on the data – [0053]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Okumura to incorporate the teaching of Elkabetz to normalize the data. By normalizing data this is an improvement to the sensing and determination of current weather phenomena and generation of accurate weather and precipitation forecasts.
In regards to Claim 14, Okumura discloses the claimed invention as detailed above in Claim 6. Okumura is silent with regards to the language of “wherein the processing circuitry is further configured to: perform a standardization process on the radio wave intensities of the plurality of frequencies using a predetermined standardization parameter before a dimension reduction process.”
Elkabetz teaches “wherein the processing circuitry is further configured to: perform a standardization process on the radio wave intensities of the plurality of frequencies at the plurality of time points before the dimension reduction process (information collection is performed and of the collected data, normalizing, i.e. standardization process, is performed on the data – [0053]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Okumura to incorporate the teaching of Elkabetz to normalize the data. By normalizing data this is an improvement to the sensing and determination of current weather phenomena and generation of accurate weather and precipitation forecasts.
Claims 13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Okumura in view of Elkabetz as applied to claims 11 and 14 above, and further in view of Islam.
In regards to Claims 13 and 16, Okumura in view of Elkabetz discloses the claimed invention as detailed above. Okumura further teaches “wherein the processing circuitry is further configured to: acquire radio wave intensities of N different frequencies, where N is a natural number greater than or equal to 3 (“The water vapor index acquisition module 41 shown in FIG. 1 acquires a water vapor index calculated based on radio wave intensities of at least two frequencies among a plurality of radio waves received by a microwave radiometer 3 . Although it is considered that there are various methods for calculating the water vapor index, in this embodiment, as shown in FIG. 3, a peak of the intensity of radio waves emitted from water vapor and cloud water in the sky is 22 GHz. In order to remove an amount of cloud water contained in the radio wave of 22 GHz, a cloud water amount calculation module 40 for calculating the amount of cloud water at 22 GHz based on the radio wave intensity of 26.5 GHz is provided. The water vapor index is calculated by subtracting the amount of cloud water at 22 GHz from the radio wave intensity at 22 GHz. In the present embodiment, p(f) is the received intensity of the microwave radiometer 3 , and f is the frequency. As shown in FIG. 3, a model af2 of the amount of cloud water is generated based on the reception intensity p (26.5 GHz) of 26.5 GHz. That is, the constant a is determined so that p (26.5 GHz)=af2 . The water vapor index is calculated as p (f)−af2 =p (22 GHz)−a (22 GHz)2 . The water vapor index acquired by the water vapor index acquisition module 41 is stored in a storage module 45 as a time-series data of the water vapor index” – [0022], Figure 3; Figure 3 shows more than 3 frequencies).”
Okumura is silent with regards to the explicit language of “dimensionally reduce the radio wave intensities of the N frequencies to the input data having a number smaller than N”
Islam teaches “dimensionally reduce the radio wave intensities of the N frequencies to the input data having a number smaller than N (PCA transform reduces the dimensions of the problem – Page 4822, Right Column, Section C)”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Okumura in view of Elkabetz to incorporate the teaching of Islam to employ Principal Component Analysis to the data. By employing principal component analysis this is an improvement in the evaluation of the total precipitable water in the atmosphere from the microwave radiometer measurement system.
Conclusion
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/YOSSEF KORANG-BEHESHTI/Examiner, Art Unit 2857