DETAILED ACTION
Remarks
Claims 1-20 have been examined and rejected. This Office Action is responsive to the amendment filed on 02/13/2026, which has been entered in the above identified application.
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 .
Claims 1-20 are presented for examination.
Respond to Amendment
The amendment filed 02/13/2026 has been entered. Claims 1, 3, 4, 9, 12, 13 and 17 have been amended. Claims 1-20 are pending in the application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bardak et al (WO 2019164503 A1) hereafter Bardak, further in view of Alkan et al (US 20200159836 A1) hereafter Alkan, and further in view of Loke et al (US 20220253040 A1) hereafter Loke.
With respect to claim 1, Bardak teaches the method comprising:
receiving a training set including pairings of control loop data for respective control loops and templates, the control loop data being mapped to the templates in the pairings, the control loop data and control loops being identified in digitized design data, and the templates including software programs or logic and having been instantiated using the control loop data of the respective control loops (The machine learning algorithm is used to improve an automation of tasks related to generating a control program. A data extraction tool configured to extract data from project files in the initial training mode. The information extracted may include input, output, requirements, cost and other parameters. The knowledge about the control system may be manipulated during the design and engineering phase, such that the descriptive information may include functions, control variables, control loops and other parameters [par. 0004-0006]); and
causing the predicted template to be instantiated with the digitized new control loop data for implementation of a control loop in an engineering system (user seeks a best fit template for engineering a control application program used by a controller. The knowledge about the control system manipulated during the design and engineering phase maintained in a project, and description information for extraction may include explanation and justification of control logic, control loops, control structure, … the user may use a GUI to enter a query for a template to begin a new engineering project. Causing the predicted templates to be instantiated is similar to generating concrete code based on a general template and specific predicted parameters. Data pre-processing tool 120 converts the high-level descriptions and template information into a high-dimensional mapping. This high-dimensional mapping may be derived using an algorithm converting text to a vector, and it may employ a mapping algorithm such as the size of the code in an engineering template, the kinds of networks defined in the code, the number of I/O connections, etc. An example of 213A/B/C may be used in multiple engineering projects those extracted from the templates [par. 0005-0013 and FIG. 10]).
However, Bardak does not particularly teach training, using machine learning, a knowledge base, based on the training set; and receiving, by the knowledge base, a query having digitized new control loop data identified in new digitized design data extracted from pictorial or textual data of an engineering project.
In the same field of endeavor, Alkan teaches training, using machine learning, a knowledge base, based on the training set (a dialog system in a computing environment that has conflicting information relating to one or more queries may be detected between one or more users, such that responses may be provided to resolve the conflict according to the knowledge domain/knowledge base [par. 0004, 0018, 0020, 0076]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of providing responses to a conflicting information from a knowledge base as suggested by Alkan into the concept of providing a suitable template among ranking templates based on the queries as suggested by Bardak because both of these systems addressing the process of training a computing system using queries those associated with existing data in the knowledge base/knowledge domain. Doing so would be desirable because the system of Bardak would be more sufficient by providing a concept of a knowledge base in the world of Internet infrastructure that would simplify the sharing of information, such that the existing data in the knowledge base can be used to resolve the conflicting information in the queries (Alkan, [par. 0002-0005]).
However, the combination of Bardak and Alkan does not particularly disclose receiving, by the knowledge base, a query having digitized new control loop data identified in new digitized design data extracted from pictorial or textual data of an engineering project; and responding, by the knowledge base, to the query with a predicted template thatincludes a software program or logic to perform a control function, the predicted template being predicted using the digitized new control loop data for pairing with the digitized new control loop data.
In the same field of endeavor, Loke teaches receiving, by the knowledge base, a query having digitized new control loop data identified in new digitized design data extracted from pictorial or textual data of an engineering project (a piping and instrumentation diagram (P&ID) is used as a diagram to illustrate the piping of process flow together with installed equipment and instrumentation. A control engineer would read the P&ID and extract engineering data from there. The process includes generating a graphic drawing representing engineering data corresponding to the unit represented within the P&ID. The step of generating engineering data comprises a modified control logic drawing, modified graphic drawing and/or modified operation sequence(s) by substituting the identified placeholder tag names within any of the extracted control logic drawings [par. 0010-0011, 0022-0024, 0068-0070, 0079-0084]); and
responding, by the knowledge base, to the query with a predicted template thatincludes a software program or logic to perform a control function, the predicted template being predicted using the digitized new control loop data for pairing with the digitized new control loop data (The process includes generating a graphic drawing representing engineering data corresponding to the unit represented within the P&ID, wherein the control logic drawing and the graphic drawing are generated based on data parsed by the unit model convertor from the generated unit model. Engineering data is extracted from the P&ID into control logic drawings, graphic drawings and sequence libraries. Each template that includes the drawings is generated according to different underlying format or standard that each is read by a software program that is distinct from others [par. 0022-0024, 0068-0070, 0079-0084 and FIG. 13]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of a piping and instrumentation diagram that is used to illustrate the piping of process flow together with installed equipment and instrumentation as suggested by Loke into the combination Bardak and Alkan because all of these systems addressing the process of training a computing system using queries those associated with existing data in a particular database. Doing so would be desirable because the combination of Bardak and Alkan would be more sufficient by generating engineering data for process within a process control system and by generating a unit template that includes data corresponding to the generated control logic drawing, the generated graphic drawing and/or the generated operation sequences, wherein each generated drawing is generated based on a format that is different from the others (Loke, [par. 0021-0024]).
With respect to claim 2, the combination of Bardak, Alkan and Loke teaches wherein the knowledge base includes a machine learning model, and training the machine learning model includes training a classifier of the machine learning model (Alkan, a machine learning component may perform one or more machine learning operations including training a classifier to classify one or more queries to assist with providing one or more responses to resolve the conflicting information of the dialog system [par. 0024, 0081]).
With respect to claim 3, the combination of Bardak, Alkan and Loke teaches wherein the received digitized control loop data and the received templates are standardized and/or normalized (Bardak, each engineering template may be used more than once that includes one or more high level descriptions, such that the template can be used for multiple projects. A descriptive data may be any kind of identifying data that can be used to associate with the template to a project [par. 0010, 0011 and FIG. 2]), and further comprising:
extracting the new digitized design data from the pictorial or textual data of the engineering project (Loke, engineering data is extracted from the P&ID into control logic drawings, graphic drawings and sequence libraries. Each template that includes the drawings is generated according to different underlying format or standard that each is read by a software program that is distinct from others [par. 0022-0024, 0068-0070, 0079-0084]).
With respect to claim 4, the combination of Bardak, Alkan and Loke teaches further comprising:
reviewing the training set by submitting the pairings of the training set for a conflict review (Alkan, one or more persons may be engaged in a conversation conflicting information. The computing system may provide an automated agent to monitor and intervene in the conversation [par. 0016-0018]);
receiving review data based on the conflict review (Alkan, the agent monitors and reacts to the chat dialog between users. The agent may identify questions with no answer, questions with multiple answers and other types of conflicting information [par. 0016-018]); and
updating the control loop data in the pairings as a function of the conflict review data, wherein the knowledge base is trained using the updated pairings (Alkan, when responses of two or more users are in conflict, an answer may be provided using any data that is available using a knowledge base/ knowledge domain, user profiles, and previous conversation histories [par. 0018]).
With respect to claim 5, the combination of Bardak, Alkan and Loke teaches wherein the conflict review comprises:
identifying a conflict in which control loop data for first and second control loops for two different pairings of the training set are the same and are paired respectively with different templates (Alkan, the answer lookup component may generate a candidate answer from user “A”. A template may be generated based on keywords that are extract from the candidate answer “A”. This answer lookup component may be triggered once a question/conflict being detected [par. 0090-0095]);
reviewing the digitized design data to identify an additional attribute in the digitized design data of the respective first and second control loops that is different for the first control loop relative to the second control loop (Bardak, the machine learning component may receive an association for a template usage as feedback data to retrain the clustering for templates. The machine learning component then may treat the information received from the association as an additional training sample for updating the clustering for the templates [par. 0016]); and
including in the review data a new feature to be added to the control loop data for the first and second control loops that corresponds to the additional attribute so that the first and second control loops have different corresponding control loop data (Bardak, the feedback data once used to retrain the clustering for the templates may be treated as additional training sample for updating the clustering. Any subsequent projects with template selections may allow the machine learning component to refine the clustering for the given template via the feedback, or the first and second control loops may have different control loop data because of this added additional training sample [par. 0016]).
With respect to claim 6, the combination of Bardak, Alkan and Loke teaches further comprising adding the additional attribute to the control loop data of at least one of the first and second control loops (Bardak, the machine learning component may use the additional training sample to update the clustering for the templates, hence the future data of one of the future projects may have this additional attribute with a given template [par. 0013, 0016]).
With respect to claim 7, the combination of Bardak, Alkan and Loke teaches further comprising:
receiving feedback about pairings between digitized new control loop data and predicted templates output by the knowledge base in response to queries submitted to the knowledge base (Alkan, the machine learning component may perform and learn information based on the feedback collected from the users. The feedback may be stored in the knowledge base and the feedback data may be used to learn and resolve conflicting information in future [par. 0083, 0096]); and
updating the training set based on the feedback (Alkan, the machine learning component may collect the user feedback to train the classifier to classify the queries based on the feedback, and collect and use historical conversations of the dialog system to assist with providing responses to resolve future conflicting information [par. 0081, 0083, 0096]).
With respect to claim 8, the combination of Bardak, Alkan and Loke teaches wherein the knowledge base is further configured to adjust a confidence score associated with the predicted template based on the feedback (Bardak, the clustering for the templates may be based on probabilities of correctness, and ranking of templates may be based on the probabilities. The machine learning component may the association for a template usage in a present project as feedback data in order to retrain the clustering. The machine learning component may receive information related to one of the associations, the query, and may treat the information as additional training sample to update the clustering for the template [par. 0013-0018]).
With respect to claim 9, it is a machine learning system claim that corresponding to the method for building a knowledge base of claim 1. Therefore, it is rejected for the same reason as claimed in claim 1 above.
With respect to claim 10, it is a machine learning system claim that corresponding to the method for building a knowledge base of claim 2. Therefore, it is rejected for the same reason as claimed in claim 2 above.
With respect to claim 11, it is a machine learning system claim that corresponding to the method for building a knowledge base of claim 3. Therefore, it is rejected for the same reason as claimed in claim 3 above.
With respect to claim 12, it is a machine learning system claim that corresponding to the method for building a knowledge base of claim 4. Therefore, it is rejected for the same reason as claimed in claim 4 above.
With respect to claim 13, it is a machine learning system claim that corresponding to the method for building a knowledge base of claim 5. Therefore, it is rejected for the same reason as claimed in claim 5 above.
With respect to claim 14, it is a machine learning system claim that corresponding to the method for building a knowledge base of claim 6. Therefore, it is rejected for the same reason as claimed in claim 6 above.
With respect to claim 15, it is a machine learning system claim that corresponding to the method for building a knowledge base of claim 7. Therefore, it is rejected for the same reason as claimed in claim 7 above.
With respect to claim 16, it is a machine learning system claim that corresponding to the method for building a knowledge base of claim 8. Therefore, it is rejected for the same reason as claimed in claim 8 above.
With respect to claim 17, it is a non-transitory computer readable storage medium claim that corresponding to the method for building a knowledge base of claim 1. Therefore, it is rejected for the same reason as claimed in claim 1 above.
With respect to claim 18, it is a non-transitory computer readable storage medium claim that corresponding to the method for building a knowledge base of claim 2. Therefore, it is rejected for the same reason as claimed in claim 2 above.
With respect to claim 19, it is a non-transitory computer readable storage medium claim that corresponding to the method for building a knowledge base of claim 7. Therefore, it is rejected for the same reason as claimed in claim 7 above.
With respect to claim 20, it is a non-transitory computer readable storage medium claim that corresponding to the method for building a knowledge base of claim 8. Therefore, it is rejected for the same reason as claimed in claim 8 above.
Response to Arguments
The examiner respectfully acknowledges the applicant’s amendments to claims 1, 3, 4, 9, 12, 13 and 17.
Applicant’s amendment filed on 02/13/2026 regarding the rejections to claim 3 under 35 USC 112(b) have been considered and are consequently withdrawn.
Applicant’s arguments filed on 02/13/2026 regarding the rejections to claims 1-20 under 35 USC 103 have been fully considered and moot in view of new ground of rejection (see rejection above).
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this Office Action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP 706.07(a). Applicant is remined 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 filled 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Quoc Phung whose telephone number is (703) 756 1330. The examiner can normally be reached on Monday through Friday from 9am to 5pm PT.
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) athttp://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached on 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Q.L.P./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
Prosecution Insights