DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is sent in response to Applicant's Communication received on April 17, 2024 for application number 18/702,216. This Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawings, Abstract, Oath/Declaration, and Claims.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
This application claims the benefit of foreign priority under 35 U.S.C. 119(a)-(d), filed on January 7, 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/17/2025 is noted. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is not statutory for the following reasons:
The claims lack the necessary physical articles or objects to constitute a machine or manufacture within the meaning of 35 U.S.C. 101.
For example, Claim 1 discloses “A system…. comprising…. a data mining module…”, does not inherently mean that the claim is directed to a machine. Only if at least one of the claimed elements of the system is a physical part of a device can the system as claimed constitute part of a device or combination of devices to be a machine within the meaning of 101. In this case the “data mining module” can be interpreted as software.
Claim 1 limitations do not include any physical structure to perform the steps recited in claim 1, the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (process, machine, manufacture, or composition of matter), e.g., the claim(s) is/are directed to a signal per se, mere information in the form of data, a contract between two parties, or a human being (see MPEP § 2106, subsection I). These claims lack the necessary physical articles or objects to constitute a machine or a manufacture within the meaning of 35 USC 101. They are clearly not a series of steps or acts to be a process nor are they a combination of chemical compounds to be a composition of matter. As such, they fail to fall within a statutory category. They are, at best, functional descriptive material per se.
Claims 1-10 lack the necessary physical articles or objects to constitute a machine or manufacture within the meaning of 35 U.S.C. 101.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. See MPEP 2106.04(a)(2)(III).
Claim 1 recites (emphasis added):
(Original) A system for acquiring data for assessing a scientific research capacity based on discipline development, comprising: a data mining module, a data reporting module, and an internet, wherein an output end of the internet is electrically connected to an input end of the data mining module
, an output end of the data mining module is electrically connected to an input end of a data preprocessing module, an output end of the data reporting module is electrically connected to the input end of the data preprocessing module, an output end of the data preprocessing module is electrically connected to an input end of a feature extraction module, and an output end of the feature extraction module is electrically connected to an input end of a research correction module; and an output end of the research correction module is electrically connected to an input end of a cluster analysis module, an output end of the cluster analysis module is electrically connected to an input end of an association module, an output end of the association module is electrically connected to an input end of a quantitative calculation module, an output end of the quantitative calculation module is electrically connected to an input end of a data dimension reduction module, an output end of the data dimension reduction module is electrically connected to an input end of a database, and an output end of the database is electrically connected to the input end of the feature extraction module.
Examiner finds that the emphasized portions of claim 1 recite an abstract idea—namely, mental processes. See MPEP 2106.04(a)(2)(III).
When read as a whole, the recited limitations are directed to using mental steps to observe, evaluate, and make judgments about data. See id (“Accordingly, the ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions”). For example, the claimed invention as a whole is directed to observing and evaluating a natural language input, and, based on that evaluation, making a judgment and/or opinion as to how to create a query language query based on replacing the natural language input with placeholder names and values. See, for example, Applicant’s specification (Spec) at ¶ 7.
Turning to the individual modules, the modules are connecting input end to output end, merely requires observation and evaluation of data and a judgment/opinion as to connecting input end to output end. Applicant’s specification (Spec) at ¶ 7.
Turning to the additional elements and whether they integrate the exception, the elements “1. A system. . . ., comprising:…” Modules, there are no additional elements such as, one or more memories storing instructions; and one or more processors coupled to the one or more memories and configured to execute the instructions. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are recited at a high level of generality and such that they amount no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea.
With respect to inventive concept, mere instructions to implement an abstract idea on a computer and/or field of use limitations cannot provide an inventive concept. See MPEP 2106.05(I)(A).
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of receiving data and collecting data (receiving or transmitting over a network), are well-understood, routine and conventional activity according to MPEP 2106.05(d)(II)(i), thus, cannot provide an inventive concept.
As a result, representative claim(s) 1, 14, and 20 do not recite any elements, or ordered combination of elements, which transforms the abstract idea into a patent-eligible subject matter. In addition, the claim(s) does not recite (i) an improvement to the functionality of a computer or other technology or technical field (see MPEP 2106.05(a); (ii) a “particular machine” to apply or use the judicial exception (see MPEP 2106.05(b); (iii) a particular transformation of an article to a different state or thing (see 2106.05(c). Further, the claim does not recite any improvement to computer functionality or specify how the one or more processors are used to improve functionality of a computing device. Considering the claim(s) as a whole, the additional elements fail to apply or use the abstract idea in a meaningful way and the additional limitations recited beyond the judicial exception itself fail to integrate the exception into a practical application. Accordingly, the claims of this application are rejected.
Claims 2-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the abstract idea of a mental process, for example the claims are directed toward the mental process of connecting input end to output end, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The limitations associated with module data elements are considered to be an abstract idea that falls in the “Mental Processes” grouping of abstract ideas.
Thus, for the reasons above, claim 1 recites an abstract idea without significantly more. The claim(s) 1-10 are rejected.
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 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 do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
- a data mining module in claim 3 and claim 4;
- a data preprocessing module in claim 5;
- a cluster analysis module in claim 6;
- a data dimension reduction module in claim 9;
- a text recording module in claim 10;
- a voice recording module in claim 10;
- a image recording module in claim 10; and
- a feature extraction module in claim 10.
Despite Applicant’s assertion in para [0007] of the specification that the term “module” are not intended as generic terms, the above-listed elements still meet the criteria of the three-pronged test for 35 USC 112(f) interpretation. That is, Applicant’s mere assertion that a “module” is not a generic placeholder does not make it so, and each of these elements are recited without sufficient structure for performing each claimed function because a “module” does not have inherent structural meaning.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Specifically, each of the various “module” elements listed above are interpreted as computer-executable instructions implemented with general purpose processor-based computing devices, consistent with at least paras. [0009-0012] and [0016-0017] of Applicant’s specification. The collaboration platforms of claims 1 and 10 are similarly interpreted as software implemented on a general purpose computing device that may perform any type of broad data analysis and data generation functions in accordance with these paragraphs.
If applicant does not intend 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 avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 6 recites the limitation "the different categories" in line 10. There is insufficient antecedent basis for this limitation in the claim. There is no prior disclosure of "different categories".
Appropriate action is required.
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2 and 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Kowolenko et al. (US 2021/0272038) (hereinafter Kowolenko) in view of Chan et al. (US 2015/0254330) (hereinafter Chan).
Regarding claim 1, Kowolenko teaches a system for acquiring data for assessing a scientific research capacity based on discipline development, comprising: a data mining module, a data reporting module, and an internet, wherein an output end of the internet is electrically connected to an input end of the data mining module, an output end of the data mining module is electrically connected to an input end of a data preprocessing module, an output end of the data reporting module is electrically connected to the input end of the data preprocessing module (see Figs. 1-3, para [0005], para [0041], para [0058], discloses data mining, sourcing data from web using web crawler and outputting reports, relating to electronic medical records, EMR); an output end of the data preprocessing module is electrically connected to an input end of a feature extraction module, and an output end of the feature extraction module is electrically connected to an input end of a research correction module (see Fig. 1, para [0058-0059], discloses normalizing source data input, via permissions to access to text documents via text document web crawler and use of csv converter).
Kowolenko does not explicitly teach an output end of the research correction module is electrically connected to an input end of a cluster analysis module, an output end of the cluster analysis module is electrically connected to an input end of an association module, an output end of the association module is electrically connected to an input end of a quantitative calculation module, an output end of the quantitative calculation module is electrically connected to an input end of a data dimension reduction module, an output end of the data dimension reduction module is electrically connected to an input end of a database, and an output end of the database is electrically connected to the input end of the feature extraction module.
Chan teaches an output end of the research correction module is electrically connected to an input end of a cluster analysis module, an output end of the cluster analysis module is electrically connected to an input end of an association module, an output end of the association module is electrically connected to an input end of a quantitative calculation module (see Figs. 2-3, Fig. 9, para [0051], para [0128], discloses HADOOP cluster data sources receiving database updates that include corresponding quantitative measurements and a model capturing quantitative facts to classifying captured data to arrive at hypotheses), an output end of the quantitative calculation module is electrically connected to an input end of a data dimension reduction module (see Figs. 9-10, para [0131], para [0147], discloses Knowledge-Intensive Database System, KIDS connected to OLAP data structure with dimensions and concepts reflecting relation in an entity model, extracting statistical measures that include trends that are transformed into qualitative values), an output end of the data dimension reduction module is electrically connected to an input end of a database, and an output end of the database is electrically connected to the input end of the feature extraction module (see Fig. 11A, para [0147-0148], para [0253], discloses outputting trend data in qualitative values from time-series data).
Kowolenko/Chan are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Kowolenko to include quantitative calculation and data dimension reduction from disclosure of Chan. The motivation to combine these arts is disclosed by Chan as “reduces a large volume of time series data into a more concise sequence of events, with the number of events proportional to the number of substantive trend changes over the time window of observations” (para [0148]) and including quantitative calculation and data dimension reduction are well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 2, Kowolenko/Chan teach a system of claim 1.
Kowolenko does not explicitly teach wherein the data reporting module comprises: a text recording module, a voice recording module, and an image recording module, wherein an output end of the text recording module, an output end of the voice recording module, and an output end of the image recording module are electrically connected to the input end of the data preprocessing module.
Chan teaches wherein the data reporting module comprises: a text recording module, a voice recording module, and an image recording module, wherein an output end of the text recording module, an output end of the voice recording module, and an output end of the image recording module are electrically connected to the input end of the data preprocessing module (see Fig. 6, para [0110-0112], discloses voice, image, and text modules outputting information from computers).
Regarding claim 7, Kowolenko/Chan teach a system of claim 1.
Kowolenko does not explicitly teach wherein the association module is configured to perform the following associated steps based on an association rule: assume that I ={i,is, ..., im} is a set of m different items, and set a transactional database D, wherein each transaction T is a set of a group of items in I, that is, TI, T has a unique identifier TD, the association rule is an implication X = Y, wherein X c I, Y c I, X n Y =cD, an establishment condition for the association rule is to satisfy a support Sand a confidence C, and in the support S, at least S% transactions in D comprise X U Y, that is, Support(X = Y) = P(X U Y); and in the confidence C and in transactions that comprise X and that are in D, at least C% transactions comprise Y, that is, Confidence(X Y) = P(X I Y), mining of the association rule is to find, from the transactional database D, an association rule that is given by a user and that satisfies a minimum support Smin and a minimum confidence Cmin.
Chan teaches wherein the association module is configured to perform the following associated steps based on an association rule: assume that I ={i,is, ..., im} is a set of m different items, and set a transactional database D, wherein each transaction T is a set of a group of items in I, that is, TI, T has a unique identifier TD, the association rule is an implication X = Y, wherein X c I, Y c I, X n Y =cD, an establishment condition for the association rule is to satisfy a support Sand a confidence C, and in the support S, at least S% transactions in D comprise X U Y, that is, Support(X = Y) = P(X U Y); and in the confidence C and in transactions that comprise X and that are in D, at least C% transactions comprise Y, that is, Confidence(X Y) = P(X I Y), mining of the association rule is to find, from the transactional database D, an association rule that is given by a user and that satisfies a minimum support Smin and a minimum confidence Cmin (see para [0036], para [0205], filter objects implementing confidence level and determining quality or data objects that are sufficient to invoke transformation).
Regarding claim 8, Kowolenko/Chan teach a system of claim 1.
Kowolenko does not explicitly teach wherein a calculation formula of the quantitative calculation module is as follows: m A=-.--WijP(th, Sh,'''Kif...x)j=1 wherein, A represents a quantitative score of performance assessment of a scientific researcher, th and Sh respectively represent a quantity of personnel and ranking of personnel in a scientific research activity h of a scientific researcher who satisfies a quantitative indicator Kg...x,Kg...x represents an actual quantity of scientific research achievements of a scientific researcher who satisfies a quantitative indicator Kg...x, and Kg...x represents a value of the quantitative indicator Kg...x satisfied by the scientific researcher.
Chan teaches wherein a calculation formula of the quantitative calculation module is as follows: m A=-.--WijP(th, Sh,'''Kif...x)j=1 wherein, A represents a quantitative score of performance assessment of a scientific researcher, th and Sh respectively represent a quantity of personnel and ranking of personnel in a scientific research activity h of a scientific researcher who satisfies a quantitative indicator Kg...x,Kg...x represents an actual quantity of scientific research achievements of a scientific researcher who satisfies a quantitative indicator Kg...x, and Kg...x represents a value of the quantitative indicator Kg...x satisfied by the scientific researcher (see Fig. 9, para [0128], para [0147], discloses quantitative trends transformed into quantitative values).
Regarding claim 9, Kowolenko/Chan teach a system of claim 1.
Kowolenko does not explicitly teach wherein the data dimension reduction module is configured to: perform dimension reduction on multi-dimensional data through on-line analytical processing (OLAP), convert the multi-dimensional data into a report, and store the report in the database; and perform querying in the database, and perform assessment on a scientific research capacity based on data.
Chan teaches wherein the data dimension reduction module is configured to: perform dimension reduction on multi-dimensional data through on-line analytical processing (OLAP), convert the multi-dimensional data into a report, and store the report in the database; and perform querying in the database, and perform assessment on a scientific research capacity based on data (see Fig. 12, para [0054], para [0147], discloses OLAP data and filtered time series data).
Regarding claim 10, Kowolenko/Chan teach a system of claim 1.
Kowolenko does not explicitly teach wherein: the text recording module is configured to record data information in a text inputting manner; the voice recording module is configured to record the data information in a voice inputting manner; the image recording module is configured to record the data information in an image inputting manner; the internet is used to search, obtain, and share the data information; the database is used to manage, classify, and sort the data information in a system, and store the data information; and the feature extraction module is configured to screen and extract important features and characteristics of mined data information.
Chan teaches wherein: the text recording module is configured to record data information in a text inputting manner (see Fig. 6, para [0112], discloses visually conveying text); the voice recording module is configured to record the data information in a voice inputting manner (see para [0110], discloses voice command recognition system); the image recording module is configured to record the data information in an image inputting manner (see Fig. 6, para [0111], discloses image scanners); the internet is used to search, obtain, and share the data information (see Fig. 6, para [0070], para [0090], discloses internet utilized in searching, obtaining and sharing data information); the database is used to manage, classify, and sort the data information in a system, and store the data information (see para [0147], discloses Online Analytical Processing, OLAP for OLAP operations and OLAP data cubes defined with dimensions and concepts reflecting relations); and the feature extraction module is configured to screen and extract important features and characteristics of mined data information (see Figs. 11A-11, para [0148], para [0191], discloses extracting classification information relating to feature entities).
Claims 3 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Kowolenko et al. (US 2021/0272038) (hereinafter Kowolenko) in view of Chan et al. (US 2015/0254330) (hereinafter Chan) as applied to claim 1, and in further view of McGee et al. (US 2014/0310243) (hereinafter McGee).
Regarding claim 3, Kowolenko/Chan teach a system of claim 1.
Kowolenko/Chan does not explicitly teach wherein the data mining module is configured to mine the following data: data related to a scientific research capacity, wherein the scientific research capacity comprises a scientific and technological innovation capacity, a scientific and technological conversion capacity, a scientific and technological competition capacity, and a scientific and technological support capacity, the scientific and technological innovation capacity comprises three elements, namely, theoretical innovation, technological innovation, and collaborative innovation, the scientific and technological conversion capacity comprises two elements, namely, military benefit and economic benefit, the scientific and technological competition capacity comprises three elements, namely, academic competition, talent competition, and development potential, and the scientific and technological support capacity comprises two elements, namely, platform support and management support.
McGee teaches wherein the data mining module is configured to mine the following data: data related to a scientific research capacity, wherein the scientific research capacity comprises a scientific and technological innovation capacity, a scientific and technological conversion capacity, a scientific and technological competition capacity, and a scientific and technological support capacity, the scientific and technological innovation capacity comprises three elements, namely, theoretical innovation, technological innovation, and collaborative innovation (see Figs. 2-3, para [0084-0085], discloses network of networks wide area shared group environment, collaborating in cooperative activities and events), the scientific and technological conversion capacity comprises two elements, namely, military benefit and economic benefit (see Fig. 4, para [0081], para [0116], discloses military “mission-aware” networking and economic advantages), the scientific and technological competition capacity comprises three elements, namely, academic competition, talent competition, and development potential, and the scientific and technological support capacity comprises two elements, namely, platform support and management support (see Figs. 6-7, para [0082], para [0145], discloses converting intent, decisions and schemes of maneuver into network configuration management and making decisions on allocation of resources).
Kowolenko/Chan/McGee are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Kowolenko/Chan to include military and economic benefits from disclosure of McGee. The motivation to combine these arts is disclosed by McGee as “improve through stochastic harmonization, and reliable reporting, will systematically help stabilize effects on global business cycles and re-align financial interests with long-term sustainability between micro-economic and macro-economic systems” (para [0114]) and including military and economic benefits are well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 6, Kowolenko/Chan teach a system of claim 1.
Kowolenko/Chan does not explicitly teach wherein the cluster analysis module is specifically configured to: S1, randomly select a threshold value for clustering, determine a category of each cluster via a random algorithm, perform clustering on research-corrected data via a clustering algorithm, to obtain a cluster with a category: C = {CI, C2, ..., Ck}, and calculate discrimination between any two clusters in each feature; S2, calculate a mean value Meani of the differentiation between any two clusters in each feature, calculate a maximum value Maxi and a minimum value Mini of average differentiation between the different categories on each feature, calculate differentiation f f = (Maxi - Mini)/Meani of each feature on the different categories, and sort features in descending order based on the differentiation f, to obtainf *(i= 1, 2, ...,m); and S3, represent results obtained in S2 by a broken line graph, and find a point or an inflection point io that changes greatly, wherein f*-fo* is a selected subset of features, and form a feature cluster analysis document with the subset of features.
McGee teaches wherein the cluster analysis module is specifically configured to: S1, randomly select a threshold value for clustering, determine a category of each cluster via a random algorithm, perform clustering on research-corrected data via a clustering algorithm, to obtain a cluster with a category: C = {CI, C2, ..., Ck}, and calculate discrimination between any two clusters in each feature (see Fig. 7, para [0283], discloses setting threshold relevant to current market conditions and numeric precedence categories); S2, calculate a mean value Meani of the differentiation between any two clusters in each feature, calculate a maximum value Maxi and a minimum value Mini of average differentiation between the different categories on each feature, calculate differentiation f f = (Maxi - Mini)/Meani of each feature on the different categories, and sort features in descending order based on the differentiation f, to obtainf *(i= 1, 2, ...,m) (see para [0269], discloses synchronized collaborative activities describing minimum high level set of requirements); and S3, represent results obtained in S2 by a broken line graph, and find a point or an inflection point io that changes greatly, wherein f*-fo* is a selected subset of features, and form a feature cluster analysis document with the subset of features (see Fig. 1, para [0253], discloses a graph of incremental changes from positive or negative values to equitable meters).
Kowolenko/Chan/McGee are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Kowolenko/Chan to include military and economic benefits from disclosure of McGee. The motivation to combine these arts is disclosed by McGee as “improve through stochastic harmonization, and reliable reporting, will systematically help stabilize effects on global business cycles and re-align financial interests with long-term sustainability between micro-economic and macro-economic systems” (para [0114]) and including military and economic benefits are well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Kowolenko et al. (US 2021/0272038) (hereinafter Kowolenko) in view of Chan et al. (US 2015/0254330) (hereinafter Chan) as applied to claim 1, and in further view of McGee et al. (US 2014/0310243) (hereinafter McGee) and Crabtree et al. (US 2017/0124481) (hereinafter Crabtree).
Regarding claim 4, Kowolenko/Chan teach a system of claim 1.
Kowolenko/Chan/McGee does not explicitly teach wherein the data mining module is configured to: crawl data by a web crawler tool, crawl data from a web across screens by a Scrapy crawling framework, crawl structural data from a page, and crawl data from a website by a Python-based Scrapy technology framework; and perform association algorithm analysis on the data crawled by the data mining module through data mining based on needs of data assessment (see Figs. 1-2, para [0041], para [0051], discloses Scrapy framework and automated predictive decision making and planning).
Kowolenko/Chan/McGee/Crabtree are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Kowolenko/Chan/McGee to include Python-based Scrapy technology framework from disclosure of Crabtree. The motivation to combine these arts is disclosed by McGee as “handle more than a single aspect of the whole task” (para [0006]) and including Python-based Scrapy technology framework is well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Regarding claim 5, Kowolenko/Chan teach a system of claim 1.
Kowolenko/Chan does not explicitly teach wherein the data preprocessing module is configured to: convert the data crawled by the data mining module into a dataset for computer recognition and computation; and perform the following operations on the dataset: deleting abnormal data, checking for a data spelling error, removing a data duplicate record, calculating missing data through derivation, filling in incomplete recorded data, removing interference and noise from the data through a filtering technology and data cleaning, and performing enhancement on useful information.
McGee teaches wherein the data preprocessing module is configured to: convert the data crawled by the data mining module into a dataset for computer recognition and computation (see Fig. 5, para [0281], discloses converting message types into standard or common XML artifacts); and perform the following operations on the dataset: deleting abnormal data, checking for a data spelling error, removing a data duplicate record, calculating missing data through derivation, filling in incomplete recorded data, removing interference and noise from the data through a filtering technology and data cleaning, and performing enhancement on useful information (see Fig. 5, para [0152], discloses configuration management to create, replace, update, and delete data).
Kowolenko/Chan/McGee are analogous arts as they are each from the same field of endeavor of database systems.
Before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to modify the system of Kowolenko/Chan to include military and economic benefits from disclosure of McGee. The motivation to combine these arts is disclosed by McGee as “improve through stochastic harmonization, and reliable reporting, will systematically help stabilize effects on global business cycles and re-align financial interests with long-term sustainability between micro-economic and macro-economic systems” (para [0114]) and including military and economic benefits are well known to persons of ordinary skill in the art, and therefore one of ordinary skill would have good reason to pursue the known options within his or her technical grasp that would lead to anticipated success.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Watson US Patent No. 11,868,852.
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/Courtney Harmon/Primary Examiner, Art Unit 2159