DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 16-17 are rejected under 35 U.S.C. 101 because the claimed invention is direct to non-statutory subject matter as follows. Claims 16-17 define a program embodying function description material. However, the claimed does not define a non-transitory computer-readable medium or memory and is thus non-statutory for that reason (i.e., “when functional descriptive material is recorded on some computer-readable medium it becomes structurally and functionally interrelated to the medium and will be statutory in most cases since use of technology permits the function of the descriptive material to be realized”- Guidelines Annex IV). That is, the scope of the presently claimed a program can range formed paper on which the program is written, to a program simply contemplated and memorized by a person.
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 1-3, 5 and 7-17 are rejected under 35 U.S.C. 103 as being unpatentable over LIU et al. (US 2022/0042962 A1) in view of Pescarmona (US Patent No. 11,295,214 B2).
In considering claim 1, LIU et al. discloses all the claimed subject matter, note 1) the claimed a basin data acquiring unit that acquires basin data of a basin of a river of interest from an image containing the basin is met by Step 1: analyzing the biota distribution characteristics of a river basin to Step 4: establishing a resident toxicity database for the river basin (Fig. 1, page 3, paragraph #0051 to page 4, paragraph #0064), 2) the claimed a candidate model acquiring unit that acquires one or more candidate models for estimating information of the river based on the basin data is met by Step 5, comparing the degrees of fitting of species sensitivity distribution (SSD) models and identifying the optimal SSD model (Fig. 1, page 4, paragraph #0065 to page 5, paragraph #0080), 3) the claimed a verifying unit that verifies validity of at least one candidate model out of the one or more candidate models using verification data is met by identifying the optimal SSD model and Step 6, validating the water quality criterion value (Fig. 1, page 4, paragraph #0065 to page 5, paragraph #0080), 4) the claimed an estimation model acquiring unit that acquires one candidate model out of the one or more candidate models as an estimation model based on a verification result is met by comparing the fit coefficients, and selecting the model with the best fitness as the optimal SSD model (Fig. 1, page 4, paragraph #0065 to page 5, paragraph #0080), and 5) the claimed a model accumulating unit that accumulates the acquired estimation model is met by calculating the cumulative probability of each species (Fig. 1, page 4, paragraph #0065 to page 5, paragraph #0080).
However, LIU et al. explicitly do not disclose the claimed estimating information on a flow rate of the river.
Pescarmona teaches that the first stage involves the measurement and survey of historical data; in the second stage we differentiate among predictive systems using machine learning methods and hydrology modeling, these predictions need to be validated using the information gathered by the streams flow and reservoir levels monitoring stations (Fig. 2, col. 6, line 56 to col. 7, line 49).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the flow rate information as taught by LIU et al. into Pescarmona’ system in order to study the behavior of the basin data of a river.
In considering claim 2, the claimed wherein the verifying unit performs verification of the candidate model, using information on a flow rate obtained using the candidate model that is to be verified, and information obtained by applying a value of a parameter corresponding to the basin data to one or more predetermined formulas regarding a flow rate of a river is met by a transfer function, the calculated flows will be validated and this information will be fed back to the entire chain with the flow measurement points in all key areas of the basin, in this manner, the model and the forecast network will be continuously updated to improve its predictions (Fig. 5, col. 8, lines 1-63 and col. 10, lines 6-67 of Pescarmona).
The motivation to combine the references has been discussed in claim 1 above.
In considering claim 3, the claimed wherein the one or more predetermined formulas include at least one of a uniform flow formula representing a relationship between a water level and a flow rate and a rational formula representing a peak flow rate is met by a transfer function, the calculated flows will be validated and this information will be fed back to the entire chain with the flow measurement points in all key areas of the basin (Fig. 5, col. 8, lines 1-63 and col. 10, lines 6-67 of Pescarmona).
The motivation to combine the references has been discussed in claim 1 above.
In considering claim 5, 1) the claimed further comprising an existing data acquiring unit that acquires information on a flow rate of the river from a hydrological database is met by the process of hydrology analysis and management in its preferred execution consists of three fundamental stages for its development and input to the database is satellite information, which is obtained through a tool with automatic access to the server or database of the entity that provides the service (Fig. 2, col. 6, line 56 to col. 7, line 49 of Pescarmona), and 2) the claimed wherein the verifying unit performs verification of the candidate model, using at least information acquired by the existing data acquiring unit and information on a flow rate obtained using the candidate model that is to be verified is met by identifying the optimal SSD model and Step 6, validating the water quality criterion value (Fig. 1, page 4, paragraph #0065 to page 5, paragraph #0080 of LIU et al.).
The motivation to combine the references has been discussed in claim 1 above.
In considering claim 7, the claimed which is configured to repeat acquisition of one or more candidate models by the candidate model acquiring unit and verification regarding the one or more candidate models by the verifying unit, until the estimation model acquiring unit determines that a verification result satisfies a predetermined condition is met by Step 1: analyzing the biota distribution characteristics of a river basin to Step 4: establishing a resident toxicity database for the river basin (Fig. 1, page 3, paragraph #0051 to page 4, paragraph #0064 of LIU et al.).
The motivation to combine the references has been discussed in claim 1 above.
In considering claim 8, the claimed wherein the estimation model is a rainfall runoff inundation model, and the basin data includes land use data is met by Runoffs which are calculated in each sub-basin using the Soil Conservation Service (SCS) method which allows differentiating surface runoff from groundwater flow due to infiltration (col. 2, line 4 to col. 3, line 35 of Pescarmona).
The motivation to combine the references has been discussed in claim 1 above.
In considering claim 9, the claimed further comprising an image acquiring unit that acquires an image to be used, based on positional information indicating the basin of the river of interest, wherein the basin data acquiring unit acquires basin data of the basin from the acquired image is met by Step 1: analyzing the biota distribution characteristics of a river basin to Step 4: establishing a resident toxicity database for the river basin (Fig. 1, page 3, paragraph #0051 to page 4, paragraph #0064 of LIU et al.).
The motivation to combine the references has been discussed in claim 1 above.
Claim 10 is rejected for the same reason as discussed in claim 1 above, and further the claimed a flow rate information acquiring unit that acquires information on a flow rate of a river of interest, using the estimation model accumulated by the model accumulating unit; and a flow rate information output unit that outputs the information on the flow rate is met by a transfer function, the calculated flows will be validated and this information will be fed back to the entire chain with the flow measurement points in all key areas of the basin, in this manner, the model and the forecast network will be continuously updated to improve its predictions (Fig. 5, col. 8, lines 1-63 and col. 10, lines 6-67 of Pescarmona).
In considering claim 11, the claimed further comprising a weather data acquiring unit that acquires weather data of the basin of the river of interest, wherein the flow rate information acquiring unit acquires the information on the flow rate by applying the weather data to the estimation model is met by the integral hydrologic model, composed of a large number of multi-parameter equations, calculates scenarios on the hydrologic state of the watershed for different weather forecasts (Fig. 1, col. 5, line 43 to col. 6, line 55 of Pescarmona).
The motivation to combine the references has been discussed in claim 1 above.
In considering claim 12, the claimed wherein the flow rate information acquiring unit acquires, through calculation, a specific flow rate of each pre-divided area in the basin, using a result output by using the estimation model is met by a transfer function, the calculated flows will be validated and this information will be fed back to the entire chain with the flow measurement points in all key areas of the basin, in this manner, the model and the forecast network will be continuously updated to improve its predictions (Fig. 5, col. 7, line 4 to col. 8, line 63 of Pescarmona).
The motivation to combine the references has been discussed in claim 1 above.
In considering claim 13, the claimed further comprising: a recharge area information acquiring unit that acquires information on an area that satisfies a predetermined recharge condition, using the specific flow rate of each area; and a recharge area information output unit that outputs the information on the area is met by a transfer function, the calculated flows will be validated and this information will be fed back to the entire chain with the flow measurement points in all key areas of the basin, in this manner, the model and the forecast network will be continuously updated to improve its predictions (Fig. 5, col. 7, line 4 to col. 8, line 63 of Pescarmona).
The motivation to combine the references has been discussed in claim 1 above.
Claim 14 is rejected for the same reason as discussed in claim 1 above.
Claim 15 is rejected for the same reason as discussed in claim 10 above.
Claim 16 is rejected for the same reason as discussed in claim 1 above.
Claim 17 is rejected for the same reason as discussed in claim 10 above.
Allowable Subject Matter
Claims 4 and 6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
LEE et al. (2012/0179373 A1) disclose method for measuring total phosphorus using multi-parameter water quality data.
Jadon et al. (US Patent No. 11,501,190 B2) disclose machine learning pipeline for predictions regarding a network.
Yamanaka et al. (US Patent No. 6,474,153 B1) disclose predicting system and predicting method configured to predict inflow volume of rainwater.
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June 10, 2026
/TRANG U TRAN/Primary Examiner, Art Unit 2422
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