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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 9/5/2025 has been entered.
Response to Amendment
Applicant' s amendment and response filed 9/5/2025 has been entered and made record. This application contains 6 pending claims.
Claims 1, and 7-8 have been amended.
Claim 6 has been cancelled.
Response to Arguments
Applicant’s arguments filed 9/5/2025 regarding claims rejections under 35 U.S.C. 101 in claim 1 and 3-8 have been fully considered but they are not persuasive.
The applicant argues on pages 9-12 of the remark filed on 9/5/2025 that “The claim, when considered as a whole, integrates the allegedly judicial exception into a practical application of the exception ... Finally, the independent claims are further amended to emphasize the practical application of the present invention by adding the following limitations: wherein the method transforms the raw urban weather observation data into an integrated database, enhances the accessibility and reliability of the raw urban weather observation data, and thereby improves the accuracy of analyzing the raw urban weather observation data and predicting future meteorological phenomena in the urban area ... Moreover, the Office fails to consider the claim as a whole, concluding that the additional elements are not sufficient to integrate the abstract idea into a practical application …”.
The Examiner respectfully disagrees applicant' s argument. Practical application can be demonstrated by additional elements that are sufficient to integrate the judicial exception into a practical application. The additional element “collecting raw urban weather observation data from a plurality of urban weather observation networks” is
considered necessary data gathering and thus, not sufficient to integrate the abstract idea into a practical application. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. retrieving the geophysical 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). Reporting the one or more differences to a user via a display are a standard procedure in monitoring and processing geophysical data associated with a borehole. As recited in MPEP section 2106.05(g), displaying analysis/results is considered extra solution activity in light of Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
The additional elements of “storing the raw urban weather observation data to generate first files of a predetermined format according to an order of observation time at each observation point of the urban weather observation networks”, “quality inspection for each predetermined weather element”; “storing the masking-processed data as a second file”, “the urban weather observation networks include Road Weather Information (RWI), Surface Energy Balance (SEB), Integrated Meteorological Sensor (IMS), and Urban-Boundary- Green (UBG)”, “wherein the method is executed by a computing system comprising at least one processor and memory storing instructions”, and “wherein the method transforms the raw urban weather observation data into an integrated database, enhances the accessibility and reliability of the raw urban weather observation data, and thereby improves the accuracy of analyzing the raw urban weather observation data and predicting future meteorological phenomena in the urban area” are not sufficient to integrate the abstract idea into a practical application. The alleged improvement in the accuracy of analyzing the raw urban weather observation data and predicting future meteorological phenomena in the urban area relates to improvement to the abstract idea itself. Therefore, the claims 1 and 7-8 do not recite additional elements that are indicative of integration of an abstract idea into a practical application.
The applicant argues on pages 12-14 of the remark filed that “Even assuming, arguendo, that the claims are directed to an abstract idea, Applicant submits that the claim amounts to "significantly more" than mere abstract idea ... The present invention provides a unique technological solution to the problem by transforming urban weather observation data into an integrated database ([08]), which is different from conventional forms of weather database ... As explained above, the claimed invention, considered as a whole, is novel and nonobvious over conventional systems, as evidenced by the fact that the present claims are subject to no prior art-based rejections.”
The Examiner respectfully disagrees applicant’s argument. Significantly more can be demonstrated by additional elements that are not well-understood and conventional that integrate the abstract idea into a practical application. The limitation of transforming urban weather observation data into an integrated database is well-understood and conventional, and this is a routine in managing and constructing customized urban weather database as an integrated database. Therefore, the claims 1 and 7-8 do not contain additional elements that are not well-understood and conventional that integrate the abstract idea into a practical application.
Dependent claims 3-5 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application.
Hence, the Examiner submits that the rejections of Claims 1, 3-5, and 7-8 are proper.
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, 3-5, and 7-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As to claim 1, the claim recites “A computer-implemented Python-based integrated management method of an urban customized weather database, the method comprising the steps of:
collecting raw urban weather observation data from a plurality of urban weather observation networks;
storing the raw urban weather observation data to generate first files of a predetermined format according to an order of observation time at each observation point of the urban weather observation networks, wherein the storing step comprises performing a time-axis correction by identifying and removing duplicate or time-reversed observation data entries created at a same observation time and at a same observation point of the urban weather observation networks;
extracting data of each observation point of the urban weather observation networks from the stored first files according to the order of observation time, for a predetermined weather element and analysis period, wherein the extracting step comprises:
identifying different variable names used for the same predetermined weather element across the different urban weather observation networks, and
generalizing the different variable names identified into a standardized variable name for the predetermined weather element;
quality inspection for each predetermined weather element, the quality inspection comprising masking observation values belonging to a predetermined masking condition, among observation values included in the extracted data; and
storing the masking-processed data as a second file,
wherein, the urban weather observation networks include Road Weather Information (RWI), Surface Energy Balance (SEB), Integrated Meteorological Sensor (IMS), and Urban-Boundary- Green (UBG), and
wherein the method is executed by a computing system comprising at least one processor and memory storing instructions, and
wherein the method transforms the raw urban weather observation data into an integrated database, enhances the accessibility and reliability of the raw urban weather observation data, and thereby improves the accuracy of analyzing the raw urban weather observation data and predicting future meteorological phenomena in the urban area.”
Under the Step 1 of the eligibility analysis, we determine whether the claim is directed 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 claim is considered to be in a statutory category (process for claims 1 and 7-8).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the bold type portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and
mental processes (concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions).
In claim 1, the step of “performing a time-axis correction by identifying and removing duplicate or time-reversed observation data entries created at a same observation time and at a same observation point of the urban weather observation networks” is a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea.
The steps of “extracting data of each observation point of the urban weather observation networks from the first files according to the order of observation time, for a predetermined weather element and analysis period”,
“identifying different variable names used for the same predetermined weather element across the different urban weather observation networks”, and
“masking observation values belonging to a predetermined masking condition, among observation values included in the extracted data” are mental processes, therefore, they are considered to be an abstract idea.
The step of “generalizing the different variable names identified into a standardized variable name for the predetermined weather element” is a mathematical concept, therefore, it is considered to be an abstract idea.
Next, under the 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.
The claim comprises the following additional elements:
collecting raw urban weather observation data from a plurality of urban weather observation networks; storing the raw urban weather observation data to generate first files of a predetermined format according to an order of observation time at each observation point of the urban weather observation networks; storing the masking-processed data as a second file, wherein the urban weather observation networks include Road Weather Information (RWI), Surface Energy Balance (SEB), Integrated Meteorological Sensor (IMS), and Urban- Boundary-Green (UBG), wherein the method is executed by a computing system comprising at least one processor and memory storing instructions, and wherein the method transforms the raw urban weather observation data into an integrated database, enhances the accessibility and reliability of the raw urban weather observation data, and thereby improves the accuracy of analyzing the raw urban weather observation data and predicting future meteorological phenomena in the urban area.
The additional element “collecting raw urban weather observation data from a plurality of urban weather observation networks“ represents necessary data gathering and does not integrate the limitation into a practical application.
The additional elements “storing the raw urban weather observation data to generate first files of a predetermined format according to an order of observation time at each observation point of the urban weather observation networks”; “storing the masking-processed data as a second file”, “wherein the urban weather observation networks include Road Weather Information (RWI), Surface Energy Balance (SEB), Integrated Meteorological Sensor (IMS), and Urban- Boundary-Green (UBG)”, and “wherein the method is executed by a computing system comprising at least one processor and memory storing instructions”, and “wherein the method transforms the raw urban weather observation data into an integrated database, enhances the accessibility and reliability of the raw urban weather observation data, and thereby improves the accuracy of analyzing the raw urban weather observation data and predicting future meteorological phenomena in the urban area” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception.
In conclusion, the above additional elements, considered individually and in combination with the other claims elements do not reflect an improvement to other technology or technical field, do not reflect improvements to the functioning of the computer itself, do not recite a particular machine, do not effect a transformation or reduction of a particular article to a different state or thing, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claim is directed to a judicial exception and require further analysis under the Step 2B.
The above claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in a relevant art as evidenced by the prior art of record (Step 2B analysis).
For example, collecting raw urban weather observation data from a plurality of urban weather observation networks is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. receiving 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).
For example, storing the raw urban weather observation data to generate first files of a predetermined format according to an order of observation time at each observation point of the urban weather observation networks is disclosed by “Samadi US 20190379592”, [0046], [0054]; and “Kilty US 20150278596”, [0002], [0004], [0005], [0007], [0026], [0029], [0032], [0033].
The claim, therefore, is not patent eligible.
Independent claims 7 and 8 recite subject matter that are similar or analogous to that of claim 1, and therefore, the claims are also patent ineligible.
With regards to the dependent claims, claims 3-5 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application.
The dependent claims are, therefore, also not patent eligible.
Examiner’s Note
Regarding claims 1, 3-5, and 7-8, the most pertinent prior arts are “Samadi US 20190379592”, “Kilty US 20150278596”, “Chapman US 20050010365”, and “Yang CN 109685081 A”, “Robinson US 20020115422”, “Ospina US 20200372349”, “Li CN 112485846 A”, “Averbuch US 20180307729”, and “Hogan US 20140056645”.
As to claim 1, Samadi teaches storing the raw urban weather observation data to generate first files of a predetermined format according to an order of observation time at each observation point of the urban weather observation networks (Samadi, [0046]);
extracting data of each observation point of the urban weather observation networks from the stored first files according to the order of observation time, for a predetermined weather element and analysis period (Samadi, [0024], [0047], [0054], FIG. 3),
wherein the method is executed by a computing system comprising at least one processor and memory storing instructions (Samadi, [0014], [0109], [0110]).
Robinson teaches collecting raw urban weather observation data from a plurality of urban weather observation networks (Robinson, [0008], [0019], [0030]).
Ospina teaches wherein the storing step comprises performing a time-axis correction by identifying and removing duplicate or time-reversed observation data entries created at a same observation time and at a same observation point of the urban weather observation networks (Ospina, [0007], [0098] ,[0144]).
Li teaches identifying different variable names used for the same predetermined weather element across the different urban weather observation networks (Li, Abstract, [0008], [0032], [0033], [0036], [0040], [0052], [0062], [0075]), and
generalizing the different variable names identified into a standardized variable name for the predetermined weather element (Li, Abstract, [0008], [0033], [0036], [0062], [0064]).
Averbuch teaches quality inspection for each predetermined weather element, the quality inspection (Averbuch, Abstract, [0001], [0002], [0003], [0028], [0030], [0036], [0037], [0038], [0041], [0098]).
Kilty discloses masking observation values belonging to a predetermined masking condition, among observation values included in the extracted data ([0007], [0098]); and
storing the masking-processed data as a second file (Kilty, [0033], [0130]).
Hogan teaches wherein the method transforms the raw urban weather observation data into an integrated database, enhances the accessibility and reliability of the raw urban weather observation data, and thereby improves the accuracy of analyzing the raw urban weather observation data and predicting future meteorological phenomena in the urban area (Hogan, [0013], [0031], [0035], [0040]).
However, the prior arts of record, alone or in combination, do not fairly teach or suggest “the urban weather observation networks include Road Weather Information (RWI), Surface Energy Balance (SEB), Integrated Meteorological Sensor (IMS), and Urban-Boundary- Green (UBG” including all limitations as claimed.
As to claim 7, Samadi teaches storing raw urban weather observation data collected from a plurality of urban weather observation networks as first files of a predetermined format according to an order of observation time at each observation point of the urban weather observation networks (Samadi, [0046]);
extracting data of each observation point of the urban weather observation networks from the stored first files according to the order of observation time, for a predetermined weather element and analysis period (Samadi, [0024], [0047], [0054], FIG. 3),
wherein the method is executed by a computing system comprising at least one processor and memory storing instructions (Samadi, [0014], [0109], [0110]).
Robinson teaches collecting raw urban weather observation data from a plurality of urban weather observation networks (Robinson, [0008], [0019], [0030]).
Ospina teaches wherein the storing step comprises performing a time-axis correction by identifying and removing duplicate or time-reversed observation data entries created at a same observation time and at a same observation point of the urban weather observation networks (Ospina, [0007], [0098], [0144]).
Averbuch teaches quality inspection for each predetermined weather element, the quality inspection (Averbuch, Abstract, [0001], [0002], [0003], [0028], [0030], [0036], [0037], [0038], [0041], [0098]).
Kilty discloses masking observation values belonging to a predetermined masking condition, among observation values included in the extracted data ([0007], [0098]); and
storing the masking-processed data as a second file (Kilty, [0033], [0130]).
Hogan teaches wherein the method transforms the raw urban weather observation data into an integrated database, enhances the accessibility and reliability of the raw urban weather observation data, and thereby improves the accuracy of analyzing the raw urban weather observation data and predicting future meteorological phenomena in the urban area (Hogan, [0013], [0031], [0035], [0040]).
However, the prior arts of record, alone or in combination, do not fairly teach or suggest “wherein the predetermined masking condition includes masking, among the extracted observation values, negative values, values exceeding 75, and values of which the difference from a previous observation value is greater than 10 as missing values when the predetermined weather element is wind speed, masking, among the extracted observation values, values smaller than -90 and values greater than 60 as missing values when the predetermined weather element is air temperature, and masking, among the extracted observation values, negative values as missing values when the predetermined weather element is not wind speed nor air temperature”; and
“the method enhances the usability and reliability of urban weather datasets by providing an automated, Python-based structured data processing framework that ensures consistent integration and data validation across multiple observation networks” including all limitations as claimed.
As to claim 8, Samadi teaches storing raw urban weather observation data collected from a plurality of urban weather observation networks as first files of a predetermined format according to an order of observation time at each observation point of the urban weather observation networks (Samadi, [0046]);
extracting data of each observation point of the urban weather observation networks from the stored first files according to the order of observation time, for a predetermined weather element and analysis period (Samadi, [0024], [0047], [0054], FIG. 3),
wherein the method is executed by a computing system comprising at least one processor and memory storing instructions (Samadi, [0014], [0109], [0110]).
Robinson teaches collecting raw urban weather observation data from a plurality of urban weather observation networks (Robinson, [0008], [0019], [0030]).
Ospina teaches wherein the storing step comprises performing a time-axis correction by identifying and removing duplicate or time-reversed observation data entries created at a same observation time and at a same observation point of the urban weather observation networks (Ospina, [0007], [0098] ,[0144]).
Averbuch teaches quality inspection for each predetermined weather element, the quality inspection (Averbuch, Abstract, [0001], [0002], [0003], [0028], [0030], [0036], [0037], [0038], [0041], [0098]).
Kilty discloses masking observation values belonging to a predetermined masking condition, among observation values included in the extracted data ([0007], [0098]); and
storing the masking-processed data as a second file (Kilty, [0033], [0130]).
Hogan teaches wherein the method transforms the raw urban weather observation data into an integrated database, enhances the accessibility and reliability of the raw urban weather observation data, and thereby improves the accuracy of analyzing the raw urban weather observation data and predicting future meteorological phenomena in the urban area (Hogan, [0013], [0031], [0035], [0040]).
However, the prior arts of record, alone or in combination, do not fairly teach or suggest “wherein the second file further includes a ratio of non-missing values among all observation values with respect to the predetermined weather element” including all limitations as claimed.
Examiner notes, however, that claims 1, 3-5, and 7-8 are rejected under 35 U.S.C. 101, and therefore, not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Gail US 20210341645” teaches “A system (100) for using mobile data to improve weather information is provided. The system (100) includes a weather prediction station (120) configured to receive stationary observation data provided by a plurality of stationary weather stations (110) along with data from a plurality of input weather models (115) and generate unified weather model estimates based on the stationary observation data, the input weather model data, and a processor (130). The processor (130) is configured to aggregate mobile observation data provided by a plurality of non-stationary sensors (140) and use the aggregated mobile observation data to adjust the weather model estimates.”
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/LAL CE MANG/Examiner, Art Unit 2863