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 .
DETAILED CORRESPONDENCE
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 February 13, 2026 has been entered.
Status of Claims
Claim 1 has been amended.
No claims have been cancelled.
No claims have been added.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 – 5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
Step 1: Collect historical maintenance data of different power plants based on power generation platforms corresponding to the different power plants, and construct historical maintenance behaviors of the corresponding power plants, wherein each power generation platform has a dedicated platform-representation database, from which the historical maintenance data can be converted to obtain a maintenance representation;
Step 2: Set maintenance tags for the historical maintenance behaviors of each power plant to obtain a plurality of tag setting results;
Step 3: Carry out a behavioral consistency analysis on all tag setting results of the maintenance tags, and extract a plurality of consistent sub-behaviors and a plurality of inconsistent sub-behaviors;
Step 4: Determine first maintenance representations for each of a large amount of first maintenance data in a comprehensive maintenance set of each of the plurality of consistent sub-behaviors according to the dedicated platform-representation database based on the power generation platforms, extract first probability common representations from all the first maintenance representations corresponding to the comprehensive maintenance set, and construct standardized representations for the same consistent sub-behaviors based on the power plant cross platform;
Step 5: Determine independent maintenance representations for each of the plurality of inconsistent sub-behaviors according to the dedicated platform-representation database based on the power generation platforms, and construct the standardized representation for each independent maintenance representation based on a platform switching relationship between the power plant cross platform and the own generation platform which is matched with the corresponding inconsistent sub-behavior;
Step 6: Store all standardized representations and platform maintenance representations of own power generation platform that each standardized representation has a switching relationship;
Step 7: in response to determining that maintenance is to be performed, automatically dispatch a maintenance switching mapping table with storage results consistent with that of the power plant, which is to be maintained, from the power plant cross platform to achieve standardized maintenance;
wherein, constructing the historical maintenance behaviors of the corresponding power plants includes:
Acquire the historical maintenance data recorded by a power generation platform corresponding one of the different power plants;
Divide the historical maintenance data by a maintenance time associated with each maintenance event to obtain a plurality of groups of maintenance data;
Carry out a behavioral analysis of each group of maintenance data based on a pre-trained maintenance analysis model to obtain a plurality of first behaviors and the historical maintenance behaviors of the corresponding power plant, wherein the pre-trained maintenance analysis model is obtained by training based on different maintenance data and their corresponding maintenance behaviors;
wherein, setting maintenance tags for the historical maintenance behaviors of each power plant includes:
Obtain all first behaviors of each power plant to analyze a behavior type of each first behavior of each power plant and classify the behaviors, and establish a first maintenance line for each behavior type;
in response to determining that the first maintenance line for each behavior type is established, establish a second maintenance line by occurrence time of each first behavior for the same power plant;
Set a first sub-tag for the corresponding first maintenance line according to results from comparing each first maintenance line and each second maintenance line;
Determine behavior characteristics corresponding to the first behavior according to a self-maintenance process and self-maintenance data application of each first behavior in each first maintenance line, and construct characteristics of the corresponding first maintenance line based on the corresponding first maintenance line;
Set a second sub-tag for the corresponding first maintenance line based on the line characteristics of the corresponding first maintenance line;
Construct the maintenance tag of the corresponding power plant according to the first sub-tags and the second sub-tags of all the first maintenance lines;
wherein, extracting first probability common representations from all the first maintenance representations corresponding to the same comprehensive maintenance set and constructing standardized representations for the same consistent sub-behaviors based on the power plant cross platform include:
Acquire a representation array of each first maintenance representation based on a representation pre-trained analytic model, and the representation array comprises a plurality of representation symbols and representation weighing of each representation symbol based on the corresponding first maintenance representation, wherein pre-trained representation analytic model is obtained by training based on different maintenance representations and symbols and weighting matched with the different maintenance representations as samples;
Acquire an occurrence number and occurrence weighing of a same representation symbol according to all representation arrays of the comprehensive maintenance set, and obtain a common value corresponding to the same representation symbol;
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Where, G1 represents the common value corresponding to the same symbol; ml represents the occurrence number corresponding to the same symbol; m2 represents the total number of representation arrays contained in the same comprehensive maintenance set, and m2 is more than ml; Pji represents the occurrence weighing at the J1st occurrence of the same symbol; Szong,j1 represents a total array weighting of the representation array corresponding to the J1st occurrence of the same symbol, and Szong,j1 is more than Pji;In represents the symbol of a logarithmic function; e represents a constant, which is 2.7;
Determine the symbols to be retained according to the common value, and regard the retained symbols as the first probability common representations;
Construct initial representations according to all first probability common representations contained in the same comprehensive maintenance set;
Obtain remaining non-common representations in each representation array, uniformly represent the non-common representations of an same type of maintenance meaning according to maintenance meaning of each remaining non-common representation and a public identification representation bias, and respectively establish change relationship between a unified representation and the corresponding non-common representation in the same type of maintenance meaning;
Adjust the initial representation based on the unified representation to obtain the standardized representation, and establish the relational index to the corresponding change relationship.
The invention is directed towards the abstract idea of collecting non-standardized information and standardizing information according to mathematical algorithms, which corresponds to “Mental Processes”, “Certain Methods of Organizing Human Activities”, and “Mathematical Concepts” as it is directed towards steps that can be performed by a human(s), in the human mind, and/or with the aid of pen and paper, e.g., having a first human collect non-standardized information from a plurality of sources and using a mathematical algorithm to convert the non-standardized information to standardized information. The invention can also be performed by first set of humans communicating the information from their respective source location to a second human to have the second human perform the conversion. Finally, the invention is also directed towards the abstract idea of collecting and organizing information, in this case, taking information from a plurality of sources with each source having their own manner of organizing information and converting the disparate formats into a single format that is easier, more efficient, and “better” to understand.
The limitations of:
Step 1: Collect historical maintenance data of different power plants based on power generation platforms corresponding to the different power plants, and construct historical maintenance behaviors of the corresponding power plants, wherein each power generation platform has a dedicated platform-representation database, from which the historical maintenance data can be converted to obtain a maintenance representation;
Step 2: Set maintenance tags for the historical maintenance behaviors of each power plant to obtain a plurality of tag setting results;
Step 3: Carry out a behavioral consistency analysis on all tag setting results of the maintenance tags, and extract a plurality of consistent sub-behaviors and a plurality of inconsistent sub-behaviors;
Step 4: Determine first maintenance representations for each of a large amount of first maintenance data in a comprehensive maintenance set of each of the plurality of consistent sub-behaviors according to the dedicated platform-representation database based on the power generation platforms, extract first probability common representations from all the first maintenance representations corresponding to the comprehensive maintenance set, and construct standardized representations for the same consistent sub-behaviors based on the power plant cross platform;
Step 5: Determine independent maintenance representations for each of the plurality of inconsistent sub-behaviors according to the dedicated platform-representation database based on the power generation platforms, and construct the standardized representation for each independent maintenance representation based on a platform switching relationship between the power plant cross platform and the own generation platform which is matched with the corresponding inconsistent sub-behavior;
Step 6: Store all standardized representations and platform maintenance representations of own power generation platform that each standardized representation has a switching relationship;
Step 7: in response to determining that maintenance is to be performed, automatically dispatch a maintenance switching mapping table with storage results consistent with that of the power plant, which is to be maintained, from the power plant cross platform to achieve standardized maintenance;
wherein, constructing the historical maintenance behaviors of the corresponding power plants includes:
Acquire the historical maintenance data recorded by a power generation platform corresponding one of the different power plants;
Divide the historical maintenance data by a maintenance time associated with each maintenance event to obtain a plurality of groups of maintenance data;
Carry out a behavioral analysis of each group of maintenance data based on a pre-trained maintenance analysis model to obtain a plurality of first behaviors and the historical maintenance behaviors of the corresponding power plant, wherein the pre-trained maintenance analysis model is obtained by training based on different maintenance data and their corresponding maintenance behaviors;
wherein, setting maintenance tags for the historical maintenance behaviors of each power plant includes:
Obtain all first behaviors of each power plant to analyze a behavior type of each first behavior of each power plant and classify the behaviors, and establish a first maintenance line for each behavior type;
in response to determining that the first maintenance line for each behavior type is established, establish a second maintenance line by occurrence time of each first behavior for the same power plant;
Set a first sub-tag for the corresponding first maintenance line according to results from comparing each first maintenance line and each second maintenance line;
Determine behavior characteristics corresponding to the first behavior according to a self-maintenance process and self-maintenance data application of each first behavior in each first maintenance line, and construct characteristics of the corresponding first maintenance line based on the corresponding first maintenance line;
Set a second sub-tag for the corresponding first maintenance line based on the line characteristics of the corresponding first maintenance line;
Construct the maintenance tag of the corresponding power plant according to the first sub-tags and the second sub-tags of all the first maintenance lines;
wherein, extracting first probability common representations from all the first maintenance representations corresponding to the same comprehensive maintenance set and constructing standardized representations for the same consistent sub-behaviors based on the power plant cross platform include:
Acquire a representation array of each first maintenance representation based on a representation pre-trained analytic model, and the representation array comprises a plurality of representation symbols and representation weighing of each representation symbol based on the corresponding first maintenance representation, wherein pre-trained representation analytic model is obtained by training based on different maintenance representations and symbols and weighting matched with the different maintenance representations as samples;
Acquire an occurrence number and occurrence weighing of a same representation symbol according to all representation arrays of the comprehensive maintenance set, and obtain a common value corresponding to the same representation symbol;
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Where, G1 represents the common value corresponding to the same symbol; ml represents the occurrence number corresponding to the same symbol; m2 represents the total number of representation arrays contained in the same comprehensive maintenance set, and m2 is more than ml; Pji represents the occurrence weighing at the J1st occurrence of the same symbol; Szong,j1 represents a total array weighting of the representation array corresponding to the J1st occurrence of the same symbol, and Szong,j1 is more than Pji;In represents the symbol of a logarithmic function; e represents a constant, which is 2.7;
Determine the symbols to be retained according to the common value, and regard the retained symbols as the first probability common representations;
Construct initial representations according to all first probability common representations contained in the same comprehensive maintenance set;
Obtain remaining non-common representations in each representation array, uniformly represent the non-common representations of an same type of maintenance meaning according to maintenance meaning of each remaining non-common representation and a public identification representation bias, and respectively establish change relationship between a unified representation and the corresponding non-common representation in the same type of maintenance meaning;
Adjust the initial representation based on the unified representation to obtain the standardized representation, and establish the relational index to the corresponding change relationship,
are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, conversations or interactions between humans, and/or with the aid of pen and paper as the claimed invention fails to recite any technology to perform any of the limitations of the claimed invention. That is, nothing in the claim element precludes the step from practically being performed in the mind, between humans, and/or with the aid of pen and paper. The context of this claim encompasses having a first human collect non-standardized information from a plurality of sources and using a mathematical algorithm to convert the non-standardized information to standardized information. The invention can also be performed by first set of humans communicating the information from their respective source location to a second human to have the second human perform the conversion. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, conversations or interactions between humans, and/or with the aid of pen and paper, then it falls within the “Mental Processes”, “Certain Methods of Organizing Human Activities”, and “Mathematical Concepts” groupings of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because no technology has been recited as being required to perform any of the limitations of the claimed invention. The Examiner asserts that “platform” and “application” are insufficient to demonstrate a practical application or technology performing the limitations of the claimed invention.
Moreover, “platform” could be nothing more than software, per se, and “application” could also be nothing more than software, per se, (NOTE: Even if one were to narrowly interpret these terms as software or that the claimed invention is being performed by software only, the Examiner asserts that a separate rejection under 35 USC 101 would be provided for claiming software, per se.) however, nothing in the claimed invention specifically identifies or defines these terms as having to be technology comprised of a computing device (or the like) executing software to perform the claimed invention.
Further still, although the claimed invention recites a “pre-trained maintenance analysis model” and a “pre-trained representation analytic model”, the models have not been positively recited as part of the claimed invention to perform some type of analysis, calculation, or etc. The claimed invention only recites that a behavioral analysis is carried out based on a “pre-trained maintenance analysis model” and acquire a representation array based on a “pre-trained representation analytic model”.
Each step of the claimed invention can be performed by a human. Additionally, where information is obtained from to perform the carrying and acquire step serves as nothing more than describing a source of information and not that the limitations are being performed by any technology. Even if one were to argue that the models are positively claimed as performing some type of analysis, calculation, or etc., the Examiner asserts that the claimed invention would still continue to fail to recite any improvement to the models or resolving an issue that arose in trained models because the claimed invention is relying on pre-trained models, thereby establishing that the claimed invention is not improving the models, but using existing, “off the shelf” models.
Accordingly, as there are not additional elements, the claimed invention does not integrate the abstract idea into a practical application because the claimed invention does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements that have been positively claimed to perform:
Step 1: Collect historical maintenance data of different power plants based on power generation platforms corresponding to the different power plants, and construct historical maintenance behaviors of the corresponding power plants, wherein each power generation platform has a dedicated platform-representation database, from which the historical maintenance data can be converted to obtain a maintenance representation;
Step 2: Set maintenance tags for the historical maintenance behaviors of each power plant to obtain a plurality of tag setting results;
Step 3: Carry out a behavioral consistency analysis on all tag setting results of the maintenance tags, and extract a plurality of consistent sub-behaviors and a plurality of inconsistent sub-behaviors;
Step 4: Determine first maintenance representations for each of a large amount of first maintenance data in a comprehensive maintenance set of each of the plurality of consistent sub-behaviors according to the dedicated platform-representation database based on the power generation platforms, extract first probability common representations from all the first maintenance representations corresponding to the comprehensive maintenance set, and construct standardized representations for the same consistent sub-behaviors based on the power plant cross platform;
Step 5: Determine independent maintenance representations for each of the plurality of inconsistent sub-behaviors according to the dedicated platform-representation database based on the power generation platforms, and construct the standardized representation for each independent maintenance representation based on a platform switching relationship between the power plant cross platform and the own generation platform which is matched with the corresponding inconsistent sub-behavior;
Step 6: Store all standardized representations and platform maintenance representations of own power generation platform that each standardized representation has a switching relationship;
Step 7: in response to determining that maintenance is to be performed, automatically dispatch a maintenance switching mapping table with storage results consistent with that of the power plant, which is to be maintained, from the power plant cross platform to achieve standardized maintenance;
wherein, constructing the historical maintenance behaviors of the corresponding power plants includes:
Acquire the historical maintenance data recorded by a power generation platform corresponding one of the different power plants;
Divide the historical maintenance data by a maintenance time associated with each maintenance event to obtain a plurality of groups of maintenance data;
Carry out a behavioral analysis of each group of maintenance data based on a pre-trained maintenance analysis model to obtain a plurality of first behaviors and the historical maintenance behaviors of the corresponding power plant, wherein the pre-trained maintenance analysis model is obtained by training based on different maintenance data and their corresponding maintenance behaviors;
wherein, setting maintenance tags for the historical maintenance behaviors of each power plant includes:
Obtain all first behaviors of each power plant to analyze a behavior type of each first behavior of each power plant and classify the behaviors, and establish a first maintenance line for each behavior type;
in response to determining that the first maintenance line for each behavior type is established, establish a second maintenance line by occurrence time of each first behavior for the same power plant;
Set a first sub-tag for the corresponding first maintenance line according to results from comparing each first maintenance line and each second maintenance line;
Determine behavior characteristics corresponding to the first behavior according to a self-maintenance process and self-maintenance data application of each first behavior in each first maintenance line, and construct characteristics of the corresponding first maintenance line based on the corresponding first maintenance line;
Set a second sub-tag for the corresponding first maintenance line based on the line characteristics of the corresponding first maintenance line;
Construct the maintenance tag of the corresponding power plant according to the first sub-tags and the second sub-tags of all the first maintenance lines;
wherein, extracting first probability common representations from all the first maintenance representations corresponding to the same comprehensive maintenance set and constructing standardized representations for the same consistent sub-behaviors based on the power plant cross platform include:
Acquire a representation array of each first maintenance representation based on a representation pre-trained analytic model, and the representation array comprises a plurality of representation symbols and representation weighing of each representation symbol based on the corresponding first maintenance representation, wherein pre-trained representation analytic model is obtained by training based on different maintenance representations and symbols and weighting matched with the different maintenance representations as samples;
Acquire an occurrence number and occurrence weighing of a same representation symbol according to all representation arrays of the comprehensive maintenance set, and obtain a common value corresponding to the same representation symbol;
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Where, G1 represents the common value corresponding to the same symbol; ml represents the occurrence number corresponding to the same symbol; m2 represents the total number of representation arrays contained in the same comprehensive maintenance set, and m2 is more than ml; Pji represents the occurrence weighing at the J1st occurrence of the same symbol; Szong,j1 represents a total array weighting of the representation array corresponding to the J1st occurrence of the same symbol, and Szong,j1 is more than Pji;In represents the symbol of a logarithmic function; e represents a constant, which is 2.7;
Determine the symbols to be retained according to the common value, and regard the retained symbols as the first probability common representations;
Construct initial representations according to all first probability common representations contained in the same comprehensive maintenance set;
Obtain remaining non-common representations in each representation array, uniformly represent the non-common representations of an same type of maintenance meaning according to maintenance meaning of each remaining non-common representation and a public identification representation bias, and respectively establish change relationship between a unified representation and the corresponding non-common representation in the same type of maintenance meaning;
Adjust the initial representation based on the unified representation to obtain the standardized representation, and establish the relational index to the corresponding change relationship,
amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Additionally:
Claims 2 – 5 are directed to “Mental Processes”, “Certain Methods of Organizing Human Activities”, and “Mathematical Concepts” as it is directed towards steps that can be performed by a human(s), in the human mind, and/or with the aid of pen and paper for the same/similar reasons that were discussed above.
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for converting non-standardized information to standardized information. Accordingly, the claims are not patent eligible.
Response to Arguments
Applicant's arguments filed 2/5/2026 have been fully considered but they are not persuasive.
Rejection under 35 USC 112(b)
The rejection under 35 USC 112(b) has been withdrawn due to amendments. +
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
The invention is directed towards the abstract idea of collecting non-standardized information and standardizing information according to mathematical algorithms, which corresponds to “Mental Processes”, “Certain Methods of Organizing Human Activities”, and “Mathematical Concepts” as it is directed towards steps that can be performed by a human(s), in the human mind, and/or with the aid of pen and paper, e.g., having a first human collect non-standardized information from a plurality of sources and using a mathematical algorithm to convert the non-standardized information to standardized information. The invention can also be performed by first set of humans communicating the information from their respective source location to a second human to have the second human perform the conversion. Finally, the invention is also directed towards the abstract idea of collecting and organizing information, in this case, taking information from a plurality of sources with each source having their own manner of organizing information and converting the disparate formats into a single format that is easier, more efficient, and “better” to understand.
Although, one may argue that the claimed invention does not seek to “tie up” the exception because of the claimed invention’s narrow scope, the Examiner asserts that clever draftsmanship of further narrowing the abstract idea does not change the fact that the invention is still directed towards an abstract idea. Here, the claimed invention is directed towards a similar scenario because the claimed invention is narrowing the abstract idea to a particular environment of use, i.e. managing maintenance data for power plants. Moreover, each of the recited steps of the claimed invention can be performed by a human, based the fact that the claimed invention fails to positively claim any step being performed by some technology, wherein the human can perform the steps based on the human activities of collecting information and using pen and paper to solve mathematical equations. The claimed invention fails to recite any automation.
The CAFC stated the following in Electric Power Group, LLC v Alstom S.A.:
“Information as such is an intangible. See Microsoft Corp. v. AT & T Corp., 550 U.S. 437, 451 n.12 (2007); Bayer AG v. Housey Pharm., Inc., 340 F.3d 1367, 1372 (Fed. Cir. 2003). Accordingly, we have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas. See, e.g., Internet Patents, 790 F.3d at 1349; OIP Techs., Inc. v. Amazon. com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347 (Fed. Cir. 2014); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011). In a similar vein, we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category. See, e.g., TLI Commc’ns, 823 F.3d at 613; Digitech, 758 F.3d at 1351; SmartGene, Inc. v. Advanced Biological Labs., SA, 555 F. App’x 950, 955 (Fed. Cir. 2014); Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372 (Fed. Cir. 2011); SiRF Tech., Inc. v. Int’l Trade Comm’n, 601 F.3d 1319, 1333 (Fed. Cir. 2010); see also Mayo, 132 S. Ct. at 1301; Parker v. Flook, 437 U.S. 584, 589–90 (1978); Gottschalk v. Benson, 409 U.S. 63, 67 (1972). And we have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis. See, e.g., Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014).”
Also, in BuySafe, Inc. v. Google, Inc. (Fed. Cir. 2014), the court stated that "abstract ideas, no matter how groundbreaking, innovative, or even brilliant, are outside what the statute means by "new and useful process, machine, manufacture, or composition of matter", and reference is made to Mryiad by the court for this position. Also stated in BuySafe is
“In defining the excluded categories, the Court has ruled that the exclusion applies if a claim involves a natural law or phenomenon or abstract idea, even if the particular natural law or phenomenon or abstract idea at issue is narrow. Mayo, 132 S. Ct. at 1303. The Court in Mayo rejected the contention that the very narrow scope of the natural law at issue was a reason to find patent eligibility, explaining the point with reference to both natural laws and one kind of abstract idea, namely, mathematical concepts.”
See also OIP Techs., 788 F3.d at 1362-63, stating:
“Lastly, although the claims limit the abstract idea to a particular environment that does not make the claims any less abstract for the step 1 analysis.”
Again, the Examiner would like to reiterate that this is a rejection under 35 USC 101 and not a rejection under 35 USC 102/103.
Therefore, because the claimed invention includes an abstract idea, the claim must be reviewed under the Step 2 analysis to determine whether the abstract idea has been applied in an eligible manner.
The claimed invention recites processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, conversations or interactions between humans, and/or with the aid of pen and paper as the claimed invention fails to recite any technology to perform any of the limitations of the claimed invention. That is, nothing in the claim element precludes the step from practically being performed in the mind, between humans, and/or with the aid of pen and paper. The context of this claim encompasses having a first human collect non-standardized information from a plurality of sources and using a mathematical algorithm to convert the non-standardized information to standardized information. The invention can also be performed by first set of humans communicating the information from their respective source location to a second human to have the second human perform the conversion. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, conversations or interactions between humans, and/or with the aid of pen and paper, then it falls within the “Mental Processes”, “Certain Methods of Organizing Human Activities”, and “Mathematical Concepts” groupings of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because no technology has been recited as being required to perform any of the limitations of the claimed invention. The Examiner asserts that “platform” and “application” are insufficient to demonstrate a practical application or technology performing the limitations of the claimed invention.
Moreover, “platform” could be nothing more than software, per se, and “application” could also be nothing more than software, per se, (NOTE: Even if one were to narrowly interpret these terms as software or that the claimed invention is being performed by software only, the Examiner asserts that a separate rejection under 35 USC 101 would be provided for claiming software, per se.) however, nothing in the claimed invention specifically identifies or defines these terms as having to be technology comprised of a computing device (or the like) executing software to perform the claimed invention.
Further still, although the claimed invention recites a “pre-trained maintenance analysis model” and a “pre-trained representation analytic model”, the models have not been positively recited as part of the claimed invention to perform some type of analysis, calculation, or etc. The claimed invention only recites that a behavioral analysis is carried out based on a “pre-trained maintenance analysis model” and acquire a representation array based on a “pre-trained representation analytic model”.
Each step of the claimed invention can be performed by a human. Additionally, where information is obtained from to perform the carrying and acquire step serves as nothing more than describing a source of information and not that the limitations are being performed by any technology. Even if one were to argue that the models are positively claimed as performing some type of analysis, calculation, or etc., the Examiner asserts that the claimed invention would still continue to fail to recite any improvement to the models or resolving an issue that arose in trained models because the claimed invention is relying on pre-trained models, thereby establishing that the claimed invention is not improving the models, but using existing, “off the shelf” models.
In addition, the database is simply being utilized to store information and, again, the claimed invention fails to recite any automation or technology to perform the claimed invention nor does the claimed invention recite any technology executing a mathematical process that results in improving technology or resolving an issue that arose in technology.
Finally, with respect to Step 7, again, no technology has been positively recited as performing Step 7 and, additionally, the Examiner asserts the term “automatically” does not mean without human interaction. Examiner asserts a process may be automatic even though a human initiates or may interrupt to the process. The term “automatically” can be construed to mean “once initiated by a human, the function is performed by a machine, without the need for manually performing the function.” Collegenet, Inc. v. Applyyourself, Inc. (CAFC, 04-1202,-1222,-1251, 8/2/2005).
Accordingly, as there are not additional elements, the claimed invention does not integrate the abstract idea into a practical application because the claimed invention does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited.
Vierira et al. (Operational Guide to stabilize standardize and increase power plant efficiency); Ritchie et al. (A sense of units and scale for electrical energy production and consumption); Sisense (Data Standardization); Meiresonne (Understanding the Complexity of Data Harmonization); Shalash et al. (The need for standardised methods of data collection sharing of data and agency coordination in humanitarian settings) – which are directed towards explanations for data standardization
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERARDO ARAQUE JR whose telephone number is (571)272-3747. The examiner can normally be reached Monday - Friday 8-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at 571-270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 2/27/2026