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 .
This communication is responsive to application filed on 07/24/2023.
Claims 1-5 are presented for examination.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is NOT invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” or a term used as a substitute for “means” that is a generic placeholder but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitations are: the model construction module is configured to construct, the model training module is configured to obtain, the prediction module is configured to perform, the high-pressure jet grouting pile diameter output module is configured to determine in claim 1.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
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 1-4 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (Does this claim fall within at least one statutory category?):
Claims 1 is directed to a system.
Claims 2-4 are directed to a method.
Therefore, claims 1-4 fall into at least one of the four statutory categories.
Step 2A, Prong 1: ((a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2)):
Claim 1:
A real-time dynamic prediction system of a three-dimensional shape of a high- pressure jet grouting pile, comprising
a model construction module, a model training module, a prediction module, and a high-pressure jet grouting pile diameter output module, wherein the model construction module is connected with the model training module, the model training module is connected with the prediction module, and the prediction module is connected with the high-pressure jet grouting pile diameter output module [a generic computer element for performing a generic computer function such as software components];
the model construction module is configured to construct a high-pressure jet grouting pile diameter prediction model based on a bidirectional recurrent neural network (BRNN) and a gated recurrent unit (GRU) [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion) and/or [mathematical concepts]];
the model training module is configured to: obtain a training data set, and train the high- pressure jet grouting pile diameter prediction model based on the training data set [insignificant extra solution, e.g. mere data-gathering];
the prediction module is configured to perform prediction based on the trained high- pressure jet grouting pile diameter prediction model, to obtain diameter prediction information in a construction process of a construction project [mathematical concepts]; and
the high-pressure jet grouting pile diameter output module is configured to: determine whether the obtained diameter prediction information matches a diameter mode; if the obtained diameter prediction information does not match the diameter mode, adjust an operation parameter of the high-pressure jet grouting pile diameter prediction model and perform prediction again; and if the obtained diameter prediction information matches the diameter mode, output the diameter prediction information [mathematical concepts].
Step 2A, Prong 2 (1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application): The claim is directed to the judicial exception.
Claim 1 recites additional elements of “obtaining, “BRNN”, and “GRU”. The additional element of “obtaining” is insignificant pre-solution (i.e. data gathering). the additional elements of “BRNN” and “GRU are used to generally apply the abstract idea without placing any limits on how the neural network used. The recitation of “using neural network model” merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using neural network” limits the identified judicial exception “prediction of diameter mode”, this type of limitation merely confines the use of the abstract idea to a particular technology environment (machine learning) and thus fails to add an inventive concept to the claims.
Step 2B: (Does the claim recite additional elements that amount to significantly more than the judicial exception? No): As discussed above with respect to the integration of the abstract into a practical application, the additional element of “obtaining” is insignificant pre-solutions (i.e. data gathering). At most the additional element is not found to including anything more than data gathering or mere data output. See MPEP 2106.04(d) referencing MPEP 2106.05(g), example (iv) - Obtaining information about transactions. Further, as explained above with respect to Step 2A, Prong two, the additional elements of “neural network such as “BRNN” and “GRU”” are at best mere instructions to “apply” the abstract ideas, which can not provide an inventive concept. See MPEP 2106.05(f).
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claim merely drawn to computer software per se. The claim does not seem to require any hardware or physical component to perform its function. As such, the claim appears to be system software per se and are therefore non-statutory.
Allowable Subject Matter
Claims 1-5 are allowable over prior art.
The following is a statement of reasons for the indication of allowable subject matter:
Chen et al (US Publication No. 2017/0362790 A1) discloses [0021] Embodiment 2: as shown in FIG. 3 and FIG. 4, if the repelling of hammering occurs when driving a pipe pile 1 to the sand layer 32 beneath the pile tip, five or more drilling holes 2 are arranged inside the pipe pile 1 shaft, depending on the diameter of the pipe pile 1. The diameter of the drilling holes 2 is in the range of 76˜108 mm. Firstly, drilling holes inside the pipe pile 1 to 0.5-1.0 m above the sand layer 32, and then start jetting high-pressure water or gas-water mixture. Keep drilling and jetting until the drill head reach a depth of 3D below pile tip, and D is the diameter of the pipe pile. Construction equipments for jet grouting are used for hole drilling and water jetting, with the pressure of the water or gas-water mixture controlled in a range of 1˜25 MPa. Through jetting the high-pressure water or gas-water mixture, the disturbed region 4 of the sand layer 32 is developed and consequently the tip resistance from the sand layer 32 is reduced. Thus the pipe pile 1 can be re-driven to the preset depth.
Li et al (US Patent No. 11, 323, 177 B2) discloses arious embodiments provide a method for free space optical communication performance prediction method. The method includes: in a training stage, collecting a large number of data representing FSOC performance from external data sources and through simulation in five feature categories; dividing the collected data into training datasets and testing datasets to train a prediction model based on a deep neural network (DNN); evaluating a prediction error by a loss function and adjusting weights and biases of hidden layers of the DNN to minimize the prediction error; repeating training the prediction model until the prediction error is smaller than or equal to a pre-set threshold; in an application stage, receiving parameters entered by a user for an application scenario; retrieving and preparing real-time data from the external data sources for the application scenario; and generating near real-time FSOC performance prediction results based on the trained prediction model (Abstract).
However, none of the cited prior art references of record fully anticipate or render obvious the independent claims in particular the limitation of:
“the model construction module is configured to construct a high-pressure jet grouting pile diameter prediction model based on a bidirectional recurrent neural network (BRNN) and a gated recurrent unit (GRU); the model training module is configured to: obtain a training data set, and train the high- pressure jet grouting pile diameter prediction model based on the training data set; the prediction module is configured to perform prediction based on the trained high- pressure jet grouting pile diameter prediction model, to obtain diameter prediction information in a construction process of a construction project; and the high-pressure jet grouting pile diameter output module is configured to: determine whether the obtained diameter prediction information matches a diameter mode; if the obtained diameter prediction information does not match the diameter mode, adjust an operation parameter of the high-pressure jet grouting pile diameter prediction model and perform prediction again; and if the obtained diameter prediction information matches the diameter mode, output the diameter prediction information” in combination with the remaining steps recited in claim 1,
“step 2: constructing, by a model construction module, a high-pressure jet grouting pile diameter prediction model based on a bidirectional recurrent neural network (BRNN) and a gated recurrent unit (GRU); step 3: training, by a model training module, the high-pressure jet grouting pile diameter prediction model based on the training data set; step 4: performing, by a prediction module, prediction based on the trained high-pressure jet grouting pile diameter prediction model, to obtain diameter prediction information in a construction process of a construction project; and step 5: determining, by a high-pressure jet grouting pile diameter output module, whether the diameter prediction information matches a diameter mode; if the diameter prediction information matches the diameter mode, outputting the diameter prediction information; and if the diameter prediction information does not match the diameter mode, adjusting an operation parameter of the high-pressure jet grouting pile diameter prediction model, and repeating prediction until the diameter prediction information matches the diameter mode, and outputting the diameter prediction information” in combination with the remaining steps recited in claim 2.
Conclusion
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 09/11/2026
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