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
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 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 wherein the representation array comprises a plurality of representation symbols and representation weighing of each representation symbol based on the corresponding first maintenance representation, and 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;
PNG
media_image1.png
84
250
media_image1.png
Greyscale
, Where, G1 represents the common value corresponding to the same symbol; m1 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 m1; 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 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 wherein the representation array comprises a plurality of representation symbols and representation weighing of each representation symbol based on the corresponding first maintenance representation, and 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;
PNG
media_image1.png
84
250
media_image1.png
Greyscale
, Where, G1 represents the common value corresponding to the same symbol; m1 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 m1; 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, but for the recitation of a pre-trained maintenance analysis model and a pre-trained representation analytic model. That is, other than reciting a pre-trained maintenance analysis model and a pre-trained representation analytic model 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 Examiner further asserts that “platform” and “application” are insufficient to demonstrate a practical application or technology performing the limitations of the claimed invention and are nothing more than generic recitations of some generic technology and fails to recite the specifics of the technology. 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. In particular, the claim only recites additional elements – a pre-trained maintenance analysis model and a pre-trained representation analytic model have been recited at a high-level of generality and “applied” to obtain and acquire (i.e. collect) information, as well as performing operations that a human can perform in their mind, pen and paper, and/or conversation or interactions between humans, as was discussed above. The pre-trained maintenance analysis model and the pre-trained representation analytic model in the steps are recited at a high-level of generality (i.e., as the generic pre-trained models can perform the insignificant extra solution steps of obtaining and acquiring (i.e. collecting) information (See MPEP 2106.05(g) while also reciting that the pre-trained maintenance analysis model and the pre-trained representation analytic model are merely being applied to perform the steps that can be performed by a human(s), in the human mind, conversing/interacting with humans, and/or with the aid of pen and paper; "[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology (See MPEP 2106.05(f)) such that it amounts no more than mere instructions to apply the exception using the pre-trained maintenance analysis model and the pre-trained representation analytic model.
Each step of the claimed invention can be performed by a human and where information is obtained from to perform the carrying and acquire step serves as nothing more than describing a source of information. The Examiner asserts that the claimed invention continues 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 and then describing the information that the models are to obtain and the information comprising the training data.
Although the claim recites “apply a pre-trained maintenance model to carry out a behavioral analysis … to obtain a plurality of first behaviors and the historical maintenance behaviors” and “apply a pre-trained representation analytic model to acquire a representation array of each first maintenance representation” the claims and specification fail to provide sufficient disclosure regarding an improvement to how a models can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained and describing the information in the training data set, i.e. “different maintenance data and their corresponding maintenance behaviors” and “different maintenance representations and symbols and weighted matched with the different maintenance representations as samples”.
There is insufficient evidence from the specification to indicate that the use of the machine learning models involves anything other than the generic application of a known technique or that the claimed invention purports to improve the functioning of the computer itself or the machine learning models. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even training and applying the trained/pre-trained models is simply an application of a computer model, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being a “pre-trained maintenance model” and “pre-trained representation analytic model” are functional labels, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards collecting non-standardized information and standardizing information according to mathematical algorithms. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP § 2106.05(f), the training and pre-trained are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with models. Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do 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, the additional element of using the pre-trained maintenance analysis model and the pre-trained representation analytic model to perform the steps 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 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 wherein the representation array comprises a plurality of representation symbols and representation weighing of each representation symbol based on the corresponding first maintenance representation, and 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;
PNG
media_image1.png
84
250
media_image1.png
Greyscale
, Where, G1 represents the common value corresponding to the same symbol; m1 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 m1; 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 5/29/2026 have been fully considered but they are not persuasive.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
As was discussed in the Non-Final Office Action mailed on 3/3/20226, “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.” As a result, amending the claims to positively recite that the models are being applied is insufficient to overcome the rejection.
Although the claim recites “apply a pre-trained maintenance model to carry out a behavioral analysis … to obtain a plurality of first behaviors and the historical maintenance behaviors” and “apply a pre-trained representation analytic model to acquire a representation array of each first maintenance representation” the claims and specification fail to provide sufficient disclosure regarding an improvement to how a models can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained and describing the information in the training data set, i.e. “different maintenance data and their corresponding maintenance behaviors” and “different maintenance representations and symbols and weighted matched with the different maintenance representations as samples”.
There is insufficient evidence from the specification to indicate that the use of the machine learning models involves anything other than the generic application of a known technique or that the claimed invention purports to improve the functioning of the computer itself or the machine learning models. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even training and applying the trained/pre-trained models is simply an application of a computer model, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being a “pre-trained maintenance model” and “pre-trained representation analytic model” are functional labels, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards collecting non-standardized information and standardizing information according to mathematical algorithms. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP § 2106.05(f), the training and pre-trained are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with models. Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Further, the Examiner asserts that the only technology that has been recited are the models which are only being applied to perform the steps of “carry out a behavioral analysis…to obtain” information and to “acquire a representation array” while the remaining steps have not been tied to any technology, let alone the models, to perform these steps, with the exception of utilizing/applying a generic database to perform the extra-solution activity of storing information. The applicant argues the burden that would be placed upon a human because of the amount of information and calculations being performed, however, nothing in the claimed invention precludes a human(s) performing the claimed steps. In response to applicant's argument that the references fail to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e., using technology to perform the entirety or most of the claimed invention or, alternatively, that none or most of the steps of the claimed method are being performed by a human(s)) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Finally, the type of information that is being managed is insufficient to transform an abstract idea into a non-abstract idea or to demonstrate that the invention is “significantly more” than the abstract idea. Similar to Electric Power Group, LLC v. Alstom S.A., the claimed invention is simply limiting the claims to a particular environment and is, without more, insufficient to transform them into a patent-eligible applications of the abstract idea at their core. The Examiner asserts that the claims are directed towards the type of information and selecting information for collection and analysis, which do nothing significant to differentiate a process from ordinary mental processes. The claims to not require a new source or type of information, or new techniques for analyzing it and, accordingly, “do not invoke any assertedly inventive programming”, but “merely require the selection and manipulation of information—to provide a “humanly comprehensible” amount of information useful for users.” The Examiner asserts that implying that technology is utilized and is, therefore, patent eligible because of the amount of manual labor or information is unpersuasive because no such technology, other than what has already been discussed above, has been positively claimed and the invention simply utilizing technology for the benefits that it provides, i.e. faster, more efficient, less prone to human error, and the like.
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.
Klocman (US Patent 12,646,123 B2); Desai (US PGPub 2026/0073336 A1) – which discuss systems and methods for managing and monitoring power facilities
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 6/9/2026
Prosecution Insights