DETAILED ACTION
This action is filed in response to the application filed on 3/28/2024.
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 .
Information Disclosure Statement
Acknowledgement is made of Applicant’s Information Disclosure Statements (IDS) form PTO-1149 filed on 3/28/2024. This IDS has been considered.
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 1-8 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract -concept of performing mental steps without significantly more. Claim 1 recites the following abstract concepts in BOLD of:
A method for optimizing a lithium-potassium anticline structure target area, comprising the following steps:
obtaining data of a historical lithium-potassium anticline structure area, and classifying the data of the historical lithium-potassium anticline structure area to generate classified data;
carrying out a parameter assignment on the classified data to obtain a parameter data set;
constructing a neural network model, inputting the parameter data set into the neural network model for a training, and obtaining a target area optimal neural network model; and
based on the target area optimal neural network model, carrying out a target area optimization in a deep lithium-potassium anticline structure area, and obtaining an optimal result.
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 claim is considered to be in a statutory category as Claim 1 teaches a method.
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 limitations that 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 mathematics or mental steps. The steps of classifying and applying parameters to data can be interpreted as a mental process that can be performed in the human mind. The step of optimizing data can be interpreted as performing mathematics ([See July 2024 Subject Matter Eligibility Example 47 Claim 3, pg. 10] “When given their broadest reasonable interpretation in light of the disclosure, the backpropagation algorithm and gradient descent algorithm are mathematical calculations. The plain meaning of these terms are optimization algorithms which compute neural network parameters using a series of 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 the claimed methods and system are not tied to a particular machine or apparatus, and 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.
Under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the first limitation teaches obtaining data which is considered necessary data gathering and does not integrate the abstract idea into a practical application. The limitation amounts to necessary data gathering and outputting. See 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) (presenting offers and gathering statistics amounted to mere data gathering).
Additionally, the limitation regarding constructing a neural network model and inputting the data into the model merely recites instructions of apply the abstract ideas on a computer. As discussed in MPEP 2106.05(f) and in the July 2024 Subject Matter eligibility Example 47 on pages 8-9, “considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.”
In the instant application the optimization mathematics are applied to computer environment with a high level of generality that invokes the machinery only as a tool. Therefore those limitations are not significantly more than the abstract ideas.
Claims 2-8 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:
Claims 2-5 further limit the abstract mental processes of claim 1 by reciting sorting and filtering data which is insignificant extra solution activity that is not considered to integrate the abstract ideas, See MPEP 2106.05(d)(II).
Claims 6-8 further define applying the mathematics performed to a computer environment with a high level of generality. This constitutes mere instructions to “apply it” or to apply the abstract ideas on a computer, which does not integrate the abstract ideas into a practical application. See MPEP 2016.05(f).
Claim 8 also recites the additional abstract idea of comparing data, which can be considered a mental process which also does not integrate the abstract ideas of Claim 1 into a practical application.
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 1-2 and 6 are rejected as being unpatentable over Chen (CN114925918 A) in view of Liu (CN104730592 A).
Regarding Claim 1, Chen teaches a method for optimizing a target area (e.g. see [n0003] “The purpose of this invention is to provide a method and system for the selection of target areas for overseas metal mineral exploration, which can solve the problem of rapid selection of target areas for overseas metal mineral exploration under conditions of small scale and insufficient data, and is conducive to improving the efficiency of overseas metal mineral prediction and improving the accuracy and reliability of prediction results”), comprising the following steps:
obtaining data of a[n] area (e.g. see [n0006] “Obtain the mineralized geological conditions and external development conditions of the model area”), and classifying the data of the area to generate classified data (e.g. see [n0007] “An index database was constructed based on the aforementioned mineralization geological conditions and external development conditions;”);
carrying out a parameter assignment on the classified data to obtain a parameter data set (e.g. see [n0008-0009] “an indicator system is constructed based on the indicator library, and the indicator system includes spatial indicators and attribute indicators; The indicator system is quantified to obtain a database”);
constructing a neural network model, inputting the parameter data set into the neural network model for a training, and obtaining a target area optimal neural network model (e.g. see [n0010-0011] “Construct a target area optimization model; The target region optimization model is trained using the data in the database to obtain the trained target region optimization model”); and
based on the target area optimal neural network model, carrying out a target area optimization in a[n] area, and obtaining an optimal result (e.g. see [n0012] “The trained target area optimization model is used to calculate the target area to be optimized, and the level and probability of the target area to be optimized are obtained”).
While Chen teaches the method of optimizing any target area, Chen does not explicitly disclose a lithium-potassium anticline structure target area. In the same field of endeavor, Liu teaches a lithium-potassium anticline structure target area (e.g. see [0011-0012] “(1) Determine the location of exploration wells: Locate the anticline structure in the predicted potash layer exploration area, determine the highest and second highest points along the axis of the anticline structure, and set up boreholes (2) Drilling and ore sampling,” and [0019] “During continuous sampling, the potassium ion content in the mud samples was ≥1%; elemental analysis showed that magnesium, strontium, boron, lithium ions and potassium ions in the mud showed a positive correlation, and magnesium ions were ≥2%; potassium ions in the core and rock cuttings samples were ≥1%.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine Chen’s method of optimizing a target area with Liu’s specific target area of a lithium potassium anticline structure for the purpose of optimizing the data of a target area with the advantage of utilizing an area with the components of most interest to the user.
Regarding Claim 2, Chen and Liu teach the limitations of Claim 1. Chen further discloses wherein a process of generating the classified data comprises: obtaining the data of the area based on an existing database (e.g. see [n0041] “an index database is established based on the detailed geological survey reports and drilling exploration reports of the mining areas”);
carrying out a feature identification on the data of area to obtain feature data of a structure area; and based on data features, carrying out a data classification on the feature data of the structure area to generate the classified data (e.g. see [n0042-0043] “Step 102: Construct an index library based on the aforementioned mineralization geological conditions and external development conditions. Among them, based on the metallogenic geological conditions and external development conditions of the model area, a target area optimization index library is established. Based on the metallogenic geological conditions, and taking into account external development conditions such as engineering development, geopolitics, policies, economy, and geography, a target area optimization index library is established as the basis for the subsequent construction of the index system”).
While Chen teaches the method of optimizing any target area, Chen does not explicitly disclose a lithium-potassium anticline structure target area. In the same field of endeavor, Liu teaches a lithium-potassium anticline structure target area (e.g. see [0011-0012] “(1) Determine the location of exploration wells: Locate the anticline structure in the predicted potash layer exploration area, determine the highest and second highest points along the axis of the anticline structure, and set up boreholes (2) Drilling and ore sampling,” and [0019] “During continuous sampling, the potassium ion content in the mud samples was ≥1%; elemental analysis showed that magnesium, strontium, boron, lithium ions and potassium ions in the mud showed a positive correlation, and magnesium ions were ≥2%; potassium ions in the core and rock cuttings samples were ≥1%.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine Chen’s method of optimizing a target area with Liu’s specific target area of a lithium potassium anticline structure for the purpose of optimizing the data of a target area with the advantage of utilizing an area with the components of most interest to the user.
Regarding Claim 6, Chen and Liu teach the limitations of Claim 1. Chen further discloses wherein a process of obtaining the target area optimal neural network model comprises: dividing the parameter data set to generate a training set and a test set (e.g. see [n0072] “The model area data in the database is used as the training set, the target area to be selected is used
as the prediction set, and 20% of the training set is selected as the validation set”);
constructing the neural network model, and inputting the training set into the neural network model to obtain an optimal neural network model (e.g. see [n0075] “input the training set index parameters and scores, train the target area optimization mathematical model”);
inputting the test set into the optimal neural network model for a testing, and generating a test result; and based on the test result, fine-tuning the optimal neural network model to obtain the target area optimal neural network model (e.g. see [n0075] “use the validation set to verify the accuracy. If the accuracy exceeds 80%, it is considered to have passed the validation; if it fails the validation, adjust the model parameters until it passes the validation”).
Claim 3 is rejected as being unpatentable over Chen (CN114925918 A) in view of Liu (CN104730592 A), and in further view of Li (CN109444982A).
Regarding Claim 3, Chen and Liu teach the limitations of Claim 2. Chen further discloses the classified data comprises lithofacies data and buried depth data (e.g. see [n0041] “This database includes the country, region, tectonic location, metallogenic belt, ore-forming type, metallogenic epoch, metallogenic process, ore-bearing strata, host rock, alteration type, ore type, ore body characteristics, resources, reserves, average grade, metallogenic temperature, metallogenic salinity, latest drilling time, depth of ore bearing borehole, grade of ore-bearing borehole, geophysical anomalies, geochemical anomalies, mineralization and alteration, engineering geological conditions, hydrogeological conditions, engineering geological conditions, infrastructure, and environmental protection requirements,” and [n0043-0044] “Among them, based on the metallogenic geological conditions and external development conditions of the model area, a target area optimization index library is established…Construct an indicator system based on the indicator library, the indicator system including spatial indicators and attribute indicators”).
Chen does not explicitly disclose wherein the classified data comprises material source data, anticline formation time data, and paleoclimatic condition data.
In the same field of endeavor, Liu teaches wherein the classified data comprises material source data (e.g. see [0034] “Exploration wells are deployed at the high points of anticlines within the predicted potash deposit exploration area, penetrating from the Quaternary to the upper Tertiary—that is, the overlying strata—generally 1000-2000 meters. Faults generally cannot penetrate the loose or weakly consolidated Quaternary strata; therefore, this depth requires drilling. After the wells penetrate the Quaternary, mineral samples are taken for observation”), anticline formation time data (e.g. see [0041-0043] “Quaternary: Strata deposited more than 2.5 million years ago are usually referred to as the Quaternary. Upper Tertiary: Strata deposited between 23 million and 2.5 million years ago are called Upper Tertiary. Anticline: An anticline is a type of upward-convex fold structure in strata. Its core is composed of older strata, and the strata are arranged from older to younger from the core to the flanks”).’
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the classification data of Chen with the additional types of data taught by Liu for the purpose of optimizing the lithium-potassium target area with the advantage of additional input data for the model to enhance the accuracy of the model output.
Chen as modified by Liu does not explicitly disclose paleoclimatic condition data. In the same field of endeavor, Li teaches paleoclimatic condition data (e.g. see [0030-31] “Using a combination of exploration methods and technologies—combining "ore-forming system + medium-scale stereo mapping + seismic inversion + high-precision electromagnetic spectrum (or magnetoelectric sounding) measurement + drilling verification"—established for deep brine potassium (lithium) salt deposits in the Qaidam Basin, the prospecting effectiveness of this combination is briefly described below, taking the deep brine potassium deposit in the northern Dalangtan area of the Qaidam Basin as an example 1. Understanding the source and ore-controlling factors of potassium (lithium) salts in deep brine of the basin from the perspective of mineralization system., “and [0036] “during the deposition of ancient salt rock layers, in the Shizigou period of the Pliocene or earlier, under arid climatic conditions, chemical sedimentary rocks, such as halite and gypsum, were produced, forming an important source of deep gravel pore brine potassium deposits”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the classification data of Chen and Liu with the paleoclimatic conditions taken into account in Li for the purpose of optimizing the lithium-potassium target area with the advantage of additional input data for the model to enhance the accuracy of the model output.
Claims 4 is rejected as being unpatentable over Chen (CN114925918 A) in view of Liu (CN104730592 A), and in further view of Mei (CN112800115A).
Regarding Claim 4, Chen and Liu teach the limitations of Claim 1. Chen does not explicitly disclose wherein a process of obtaining the parameter data set comprises: screening a classified data set based on different categories to obtain a screened data set; and setting a data threshold range, and evaluating the screened data set based on the data threshold range to obtain the parameter data set.
In the same field of endeavor, Mei teaches wherein a process of obtaining the parameter data set comprises: screening a classified data set based on different categories to obtain a screened data set (e.g. see [pg. 8 paragraph 2] “clustering the sample data in the sample data set to obtain the clustering processing result; the clustering processing result is used for indicating the clustering category to which each group of sample data belongs”); and
setting a data threshold range (e.g. see [pg. 11 paragraph 4] “according to the attribute parameter corresponding to each target clustering type, determining the target upper limit threshold value and target lower limit threshold corresponding to the sample data set”),and evaluating the screened data set based on the data threshold range to obtain the parameter data set (e.g. see [pg. 5 paragraph 3] “based on the data processing method provided by the solution, determining the target upper limit threshold value and target lower limit threshold value can be filter for the abnormal data”)..
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the parameter data set of Chen with the method of obtaining the parameter data set taught by Mei for the purpose of further categorizing the data with the advantage of creating smaller more manageable data sets comprising only the data that will enhance the optimization model.
Claims 5 is rejected as being unpatentable over Chen (CN114925918 A) in view of Liu (CN104730592 A), and in further view of Mei (CN112800115A) and Short (WO2020190480 A1).
Regarding Claim 5, Chen, Liu, and Mei teach the limitations of Claim 4. Chen does not explicitly disclose wherein a process of obtaining the screened data set comprises: judging and identifying attribute description features of the screened data set to obtain an attribute data set;splitting an attribute description in the attribute data set into character tuples, and then carrying out a clustering test to obtain category attribute weights; performing an attribute similarity matching based on the category attribute weights to obtain an attribute matching result; and matching the attribute matching result with the screened data set to obtain the screened data set.
In the same field of endeavor, Mei teaches wherein a process of obtaining the screened data set comprises: judging and identifying attribute description features of the screened data set to obtain an attribute data set (e.g. see [pg. 10 paragraph 6] “according to the clustering processing result, determining the attribute parameter corresponding to the sample data belonging to the target clustering category; the target clustering category is any cluster category satisfying specific condition in the clustering processing result”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the parameter data set of Chen with the attribute data set of Mei for the purpose of further categorizing the data with the advantage of creating smaller more manageable data sets comprising only the data that will enhance the optimization model.
Chen as modified by Liu and Mei does not explicitly disclose splitting an attribute description in the attribute data set into character tuples, and then carrying out a clustering test to obtain category attribute weights; performing an attribute similarity matching based on the category attribute weights to obtain an attribute matching result; and matching the attribute matching result with the screened data set to obtain the screened data set.
In the same field of endeavor, Short teaches splitting an attribute description in the attribute data set into character tuples, and then carrying out a clustering test to obtain category attribute weights; performing an attribute similarity matching based on the category attribute weights to obtain an attribute matching result; and matching the attribute matching result with the screened data set to obtain the screened data set (e.g. see [0007] “system for identifying an image or object by classifying an input data set of attributes within a data category using multiple data recognition tools is disclosed. The system includes an identification module configured to identify at least a first attribute and a second attribute of the data category; a classification module configured to classify the at least first attribute via at least a first data recognition tool and the at least second attribute via at least a second data recognition tool, the classification including allocating a confidence factor for each of the at least first and second attributes that indicates a confidence regarding the presence and identification of each of the at least first and second attributes in the input data set; and a combination module configured to combine outputs of the classifying into a single output confidence by using a weighted fusion of the allocated confidence factors to indicate a confidence that the input data (i.e., image or object) has been classified correctly according to data category.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the parameter data set of Chen with the attribute data set of Short for the purpose of further categorizing the data with the advantage of creating smaller more manageable data sets comprising only the data that will enhance the optimization model.
Claim 7-8 rejected as being unpatentable over Chen (CN114925918 A) in view of Liu (CN104730592 A) and in further view of Gao (CN115374702).
Regarding Claim 7, Chen and Liu teach the limitations of Claim 1. Chen further discloses herein a process of obtaining the optimal result comprises: obtaining real-time data of a[n] area, inputting the real-time data of the lithium-potassium anticline structure area into the target area optimal neural network model for a target area quality calculation, and obtaining a calculation result (e.g. see [0086] “The calculation module 207 is used to calculate the target area to be optimized using the trained target area optimization model to obtain the level and probability of the target area to be optimized”).
While Chen teaches the method of optimizing any target area, Chen does not explicitly disclose a lithium-potassium anticline structure target area. In the same field of endeavor, Liu teaches a lithium-potassium anticline structure target area (e.g. see [0011-0012] “(1) Determine the location of exploration wells: Locate the anticline structure in the predicted potash layer exploration area, determine the highest and second highest points along the axis of the anticline structure, and set up boreholes (2) Drilling and ore sampling,” and [0019] “During continuous sampling, the potassium ion content in the mud samples was ≥1%; elemental analysis showed that magnesium, strontium, boron, lithium ions and potassium ions in the mud showed a positive correlation, and magnesium ions were ≥2%; potassium ions in the core and rock cuttings samples were ≥1%.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine Chen’s method of optimizing a target area with Liu’s specific target area of a lithium potassium anticline structure for the purpose of optimizing the data of a target area with the advantage of utilizing an area with the components of most interest to the user.
Chen does not explicitly disclose setting a calculation threshold range, and comparing the calculation result with the calculation threshold range to obtain the optimal result. In the same field of endeavor, --Gao teaches setting a calculation threshold range, and comparing the calculation result with the calculation threshold range to obtain the optimal result. (e.g. see [pg. 15 final paragraph] “outputting the mining target area prediction probability value of each target convolutional neural network to the convolution output value, if the target function value is less than the preset value, updating the convolution kernel size of the target convolutional neural network to the target function value, and returning the convolution kernel size of the updated target convolutional neural network to continue to execute the Hadamard calculation module, until reaching the preset maximum iteration times or target function value reaches the preset value, then outputting the mining target area prediction probability value of each target convolutional neural network to the convolution output value, wherein the calculation formula of the target function value calculated according to the classification loss”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the calculation result of Chen with the threshold and calculation result of Gao for the purpose of optimizing data with the advantage of repetitive calculations to ensure the output is accurate and meets necessary requirements.
Regarding Claim 8, Chen, Liu, and Gao teach the limitations of Claim 7. Chen further discloses dividing the calculation threshold range into a first calculation threshold range, a second calculation threshold range and a third calculation threshold range based on a weighted value range; and comparing the calculation result with the calculation threshold range, and a first optimal result is considered if the calculation result is within the first calculation threshold range, a second optimal result is considered if the calculation result is within the second calculation threshold range, and a third optimal result is considered if the calculation result is within the third calculation threshold range (e.g. see [n0060] “tiered scoring divides data into several ranges based on the statistical frequency of their occurrence, and assigns scores to each range, “and [n0078] “The process involves generating charts and outputting a results report based on the target area level and probability to be optimized. The target area is divided into three levels: Level I, Level II, and Level III, which reflect the probability of mineralization and potential value of the target area”).
Conclusion
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/NYLA GAVIA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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