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 ACTION
The action is in response to the Applicant’s communication filed on 05/10/2024.
Claims 1-20 are pending, where claims 1 and 14 are independent.
This application claims the priority benefit of the provisional application no. 63/466,203 filed on 05/12/2023 incorporated herein.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/20//2024 and 08/21/2024 have been filed after the filing date of the application. The submission is in-compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Multiple filed related applications
Applicants have filed multiple related applications. To date, some of the related applications have been allowed or under NOA or some are stand pending, yet to be examined. There are plurality of co-pending related Applications and double patenting issue is proper. See MPEP 804 and 1490 (VI) D:
Nonstatutory Double Patenting
37 CFR 1.78(b) provides that when two or more applications filed by the same applicant contain conflicting claims, elimination of such claims from all but one application may be required in the absence of good and sufficient reason for their retention during pendency in more than one application. Applicant is required to either cancel the conflicting claims from all but one application or maintain a clear line of demarcation between the applications. See MPEP § 822.]
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. See MPEP § 804 and 1490 (VI) D.
Claims 1 and 14 are provisionally rejected on the ground of double patenting over claims 1 and 19 of copending U.S. Patent application No. 18/784,440 (PGPub No. 2025/0036092 A1). This is a provisional double patenting rejection because the patentably indistinct claims have not in fact been patented.
The subject matter claimed in the instant application and copending application are claiming common/similar subject matter, as follows:
Instant Application No. 18/662,057
US Application 18/784,440 (PGPub No. 2025/0036092 A1)
Claim 1. A method, comprising: receiving, by one or more processors, a prompt indicative of an item of equipment; providing, by the one or more processors, the prompt as input to a machine learning system comprising a plurality of machine learning models, each machine learning model of the plurality of machine learning models configured based on training data corresponding to a respective role for the machine learning model and comprising natural language data regarding items of equipment; and
generating, by the one or more processors using a first machine learning model of the plurality of machine learning models and based on the respective role for the first machine learning model, a candidate output;
modifying, by the one or more processors using a second machine learning model of the plurality of machine learning models and based on the respective role for the second machine learning model, the candidate output to generate an output; and
causing presentation of the output, by the one or more processors, using at least one of a display device or an audio output device
1. A method, comprising:
detecting, by one or more processors, one or more inputs comprising (i) a data structure representing a layout of an environment in which to install one or more items of equipment and (ii) an identifier of the one or more items of equipment;
generating an output, by the one or more processors, using at least one machine learning model and based on the input, the at least one machine learning model configured using training data comprising unstructured data regarding the one or more items of equipment and sensor data regarding one or more examples of the item of equipment, the output comprising at least one of a location or a state for installation of the one or more items of equipment; and
presenting, by the one or more processors using at least one of a display device and an audio output device, the output.
Claims 2-20 are also provisionally obvious to the claims 1-20 of the U.S. Patent co-pending Application No. 18/784,440 (PGPub No. 2025/0036092 A1).
Although the conflicting claims are not identical, they are not patentably distinct from each other (as shown in the table for comparison) because they are substantially or conceptually similar to the limitations of the patent applications (as for example the limitation “generating, by the one or more processors using a first machine learning model of the plurality of machine learning models and based on the respective role for the first machine learning model, a candidate output” of the application is equivalent to the limitation “generating an output, by the one or more processors, using at least one machine learning model and based on the input, the at least one machine learning model configured using training data” of the co-pending application) in scope and they use the similar limitations and produce the similar/same end result of presenting the output using display device and/or audio output device.
It would be therefore obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made that to modify or to omit the additional elements of claims 1 and 19 of the co-pending application to arrive at the claims 1 and 14 of the instant application, would perform the similar functions as before.
This is a provisional obviousness-type nonstatutory double patenting rejection because the patentably indistinct claims have not yet been patented. See MPEP 804 and 1490 (VI) D:
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
a) Claim 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the best mode contemplated by the inventor or a joint inventor, or for pre-AIA the inventor(s) has not been disclosed. Evidence of concealment of the best mode is based upon the lack of the illustration of first and second machine learning model, to facilitate understanding of the invention.
Because, in the specification, the application presents the elements “plurality of machine learning models”, “first machine learning model”, “second machine learning model” and so on. But they are unclear to enable the invention without drawings, equations, description and so on to a person of ordinary skilled in the art.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1-20 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Okamoto, USPGPub No. 20240046157 A1 in view of Huber, et al. (USPGPub No. 20230145448 A1).
As to claim 1, Okamoto discloses A method, comprising: receiving, by one or more processors, a prompt indicative of an item of equipment; providing, by the one or more processors, the prompt as input to a machine learning system comprising a plurality of machine learning models, each machine learning model of the plurality of machine learning models configured based on training data corresponding to a respective role for the machine learning model and comprising natural language data regarding items of equipment (Okamoto [0002-14] “receiving input data and labels, and performing data validation to generate a configuration file - automatically optimizing hyper-parameters associated with the generated artificial intelligence model, and automatically generating an updated model based on optimized one or more input features and the optimize hyper-parameters - one or more input features are optimized by a genetic algorithm to optimize combinations of the one or more input features, and generate a list of the optimize input features - automatically optimizing the one or more input features performed in a first iterative loop that is performed until a first prescribed number of iterations has been met - performing the training and the evaluation - execution of one or more feature functions based on a data type” [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0086-230] see Fig. 1-40, receiving input data and labels, context data, performing data validation to generate configuration file, preprocess, postprocess, plurality of machine learning models, learning, model training obviously provides the limitations receiving prompt indicative as input to machine learning system comprising a plurality of machine learning models based on training data and natural language data); and
generating, by the one or more processors using a first machine learning model of the plurality of machine learning models and based on the respective role for the first machine learning model, a candidate output; modifying, by the one or more processors using a second machine learning model of the plurality of machine learning models and based on the respective role for the second machine learning model, the candidate output to generate an output; (Okamoto [0002-14] “generate a configuration file, and splitting the data to generate split data for training and evaluation, performing training and evaluation of the split data to determine an error level, and based on the error level, performing an action - modifying the configuration file and tuning the artificial intelligence model automatically, generating the artificial intelligence model based on the training, the evaluation and the tuning, and serving the model for production - tuning - automatically optimizing hyper-parameters associated with the generated artificial intelligence model, and automatically generating an updated model based on optimized one or more input features - automatically optimizing the one or more input features performed - until a first prescribed number of iterations has been met - automatically generating the updated model performed in a second iterative loop until a second prescribed number of iterations has been met - performing the training and the evaluation - one or more feature functions based on a data type” [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0052-230] see Fig. 1-40, computing device communicatively coupled to input/output device interface, performing data validation to generate configuration file, preprocess, postprocess, plurality of machine learning models, learning, model training, output to model for production; automatically optimizing input features performed until met the prescribed criteria, training, evaluation, testing and prediction for data as output obviously provides generating and modifying, a candidate using plurality of machine learning models, the candidate output to generate an output).
However, Huber discloses causing presentation of the output, by the one or more processors, using at least one of a display device or an audio output device (Huber [0001-23] “automated action comprises generating - resolving the predicted fault based on the predicted root cause - presenting recommendation on a user interface - indicating a level of accuracy of the recommendation and training - based on the predicted root cause and the input level of accuracy” [abstract] see Fig. 1-16, plurality of machine learning models, automated action, predicted fault, presenting recommendation on user interface obviously provides the causing presentation of the output using a display device or an audio output device as user interface).
Okamoto and Huber are analogous arts from the same field of endeavor and contain overlapping structural and functional similarities and both contain plurality of machine learning models, automated action and predicted output.
Therefore, at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above functionalities causing presentation of the output using a display device or an audio output device as user interface, as taught by Okamoto, and incorporating presenting recommendation and output to user interface, as taught by Huber.
As to claim 2, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The method of claim 1, further comprising: receiving, by the one or more processors, feedback regarding the output; and updating, by the one or more processors, the neural network according to the feedback, wherein the feedback corresponds to at least one of a score or input data regarding the output (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - until a first prescribed number of iterations has been met - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, computing device communicatively coupled to input/output device interface, preprocess, postprocess, plurality of machine learning models, neural networks, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides receiving feedback regarding the output and updating neural network according to the feedback).
As to claim 3, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The method of claim 1, further comprising: evaluating, by the one or more processors, an accuracy score of the output; and performing, by the one or more processors, at least one of (i) storing the output and a flag associated with the output and indicative of the accuracy score, (ii) modifying the output responsive to the accuracy score not satisfying an accuracy criterion, or (iii) updating at least one machine learning model of the plurality of machine learning models according to the output and the accuracy score (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, preprocess, postprocess, plurality of machine learning models, neural networks, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides the limitations).
As to claim 4, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The method of claim 1, further comprising: evaluating, by the one or more processors, the output using one or more bias criteria; and controlling, by the one or more processors, inclusion of the output in a database for training data for updating the machine learning system according to the evaluation (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, preprocess, postprocess, plurality of machine learning models, neural networks include biasing criteria, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides the limitations).
As to claim 5, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The method of claim 1, further comprising: determining, by the one or more processors, that at least one of the prompt or the output does not satisfy one or more criteria associated with the item of equipment, wherein the one or more criteria correspond to at least one of a predetermined threshold, a model, an algorithm, or a simulation regarding the item of equipment; and performing, by the one or more processors responsive to determining that the at least one of the prompt or the output does not satisfy the one or more criteria, at least one of (i) outputting an alert, (ii) modifying the output according to the one or more criteria, (iii) transmitting a request to a device associated with a user to verify or flag the at least one of the prompt or the output, or (iv) modifying the machine learning system based on at least one of the prompt, the output, or the one or more criteria (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, computing device communicatively coupled to input/output device interface, plurality of feature functions based on data type, preprocess, postprocess, plurality of machine learning models, neural networks include biasing criteria, SVM, DNN, RNN, CNN, output to model for production; automatically optimizing input features performed until met the prescribed criteria, training, evaluation, testing and prediction obviously provides the limitations).
As to claim 6, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The method of claim 1, further comprising: providing to the machine learning system for generation of the output, by the one or more processors, context data corresponding to the prompt, wherein the context data comprises at least one of data regarding the item of equipment received via a user interface or retrieved by the machine learning system from one or more unstructured data elements corresponding to the item of equipment (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, computing device communicatively coupled to input/output device interface, plurality of feature functions based on data type, preprocess, postprocess, plurality of machine learning models, neural networks include biasing criteria, SVM, DNN, RNN, CNN, output to model for production; automatically optimizing input features performed until met the prescribed criteria, training, evaluation, testing and prediction obviously provides the limitations).
As to claim 7, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The method of claim 1, further comprising: configuring, by the one or more processors using the machine learning system, a database query for data to retrieve to generate the output; and providing, by the one or more processors, the data to at least one of the first machine learning model or the second machine learning model for generation of at least one of the candidate output or the output (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, preprocess, postprocess, plurality of machine learning models, neural networks include biasing criteria, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides the limitations).
As to claim 8, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The method of claim 1, wherein generating, using the first machine learning model, the candidate output comprises at least one of (i) processing sensor data regarding the item of equipment or (ii) causing a function to perform a calculation based on the sensor data regarding the item of equipment and provide a result of the calculation to the first machine learning model, the first machine learning model to generate the candidate output as text data comprising the result (Huber [0001-23] “automated action comprises generating - resolving the predicted fault based on the predicted root cause - presenting recommendation on a user interface - indicating a level of accuracy of the recommendation and training - based on the predicted root cause and the input level of accuracy” [0045-95] “system 130 - include various sensors (e.g., temperature sensors, pressure sensors, etc.) configured to measure attributes of the supply airflow - receive input from sensors located within AHU 106 and/or within the building zone and may adjust the flow rate, temperature, or other attributes of the supply airflow through AHU 106 to achieve setpoint conditions for the building zone” [abstract] see Fig. 1-16, BMS, controller, plurality of sensors for equipment data, plurality of machine learning models, automated action, predicted fault, presenting recommendation on user interface obviously provides candidate output comprises at least one of (i) processing sensor data regarding the item of equipment).
As to claim 9, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The method of claim 1, further comprising: establishing, by the one or more processors, a communication session between a first device from which the prompt is received and a second device, the second device associated with a user meeting one or more expertise criteria regarding the item of equipment (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, computing device communicatively coupled to input/output device interface, plurality of feature functions based on data type, preprocess, postprocess, data analyses, data transformation, integrated models, plurality of machine learning models, neural networks, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides the limitations).
As to claim 10, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The method of claim 1, wherein the respective role of the first machine learning model is a drafter role, and the respective role of the second machine learning model is at least one of an editor role or a summarizer role (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, plurality of feature functions based on data type, preprocess, postprocess, data analyses, data transformation, plurality of machine learning models, neural networks, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides the limitations).
As to claim 11, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The method of claim 1, further comprising modifying the prompt, by a preprocessor according to one or more criteria for the input, prior to providing the prompt as input to the neural network (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, plurality of feature functions based on data type, preprocess, postprocess, data analyses, data transformation, plurality of machine learning models, neural networks, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides the limitations).
As to claim 12, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The method of claim 1, further comprising modifying the output, by a postprocessor according to one or more criteria for the output, prior to providing the output to an application session of a device from which the prompt is received (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, plurality of feature functions based on data type, preprocess, postprocess, data analyses, data transformation, plurality of machine learning models, neural networks, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides the limitations).
As to claim 13, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The method of claim 1, further comprising: causing, by the one or more processors, the machine learning model system to generate a query to at least one of a database, a simulation, or a model for a validation output corresponding to the prompt; and causing, by the one or more processors, the machine learning model system to at least one of (i) output a comparison of the validation output and the output or (ii) modify the output according to the validation output (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, plurality of feature functions based on data type, preprocess, postprocess, data analyses, data transformation, plurality of machine learning models, neural networks, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides the limitations).
As to the independent claim 14, the claims recite similar limitations as the independent claim 1 and rejected using same rational as stated above.
As to claim 15, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The system of claim 14, wherein the first machine learning model comprises at least one of a transformer or a denoising network, and the training data comprises at least one of text data, speech data, audio data, image data, or video data (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, plurality of feature functions based on data type, preprocess, postprocess, data analyses, data transformation, plurality of machine learning models, neural networks, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides the limitations).
As to claim 16, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The system of claim 14, wherein an edge device comprises at least one first processor of the one or more processors, the at least one first processor configured to process sensor data regarding the item of equipment to generate equipment data to provide to the machine learning system for the machine learning system to use as input to generate the output (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, preprocess, postprocess, plurality of machine learning models, neural networks include biasing criteria, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides the limitations).
As to claim 17, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The system of claim 14, wherein the neural network comprises at least one generative pre-trained transformer model updated by fine-tuning using the training data (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, preprocess, postprocess, plurality of machine learning models, neural networks include biasing criteria, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides the limitations).
As to claim 18, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The system of claim 14, wherein the one or more processors are configured to: generate a vector representative of the prompt; identify, by searching a vector database mapping vectors with data elements, a selected data element corresponding to the vector; and generate, by using the neural network, the completion based at least on the selected data element (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, plurality of feature functions based on data type, preprocess, postprocess, data analyses, data transformation, plurality of machine learning models, neural networks, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides the limitations).
As to claim 19, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses The system of claim 14, wherein the one or more processors are configured to input, to the first machine learning model, a query comprising a request to perform a text analysis operation on at least one of the prompt or the output (Okamoto [abstract] “optimizing machine learning model generation - obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices - generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment” [0002-14] “performing training and evaluation of the split data to determine an error level, and based on the error level - automatically generating an updated model based on optimized one or more input features - performing the training and the evaluation - one or more feature functions based on a data type” [0052-230] see Fig. 1-40, plurality of feature functions based on data type, preprocess, postprocess, data analyses, data transformation, plurality of machine learning models, neural networks, SVM, DNN, RNN, CNN, output to model for production; training, evaluation, testing and prediction obviously provides the limitations).
As to claim 20, the combination of Okamoto and Huber disclose all the limitations of the base claims as outlined above.
The combination further discloses. The system of claim 14, wherein the one or more processors are configured to generate a control signal for operation of the item of equipment based on the output (Huber [0001-23] “automated action comprises generating - resolving the predicted fault based on the predicted root cause - presenting recommendation on a user interface - indicating a level of accuracy of the recommendation and training - based on the predicted root cause and the input level of accuracy” see Fig. 1-16, plurality of machine learning models, automated action, predicted fault, presenting recommendation on user interface obviously provides generate control signal for operation of the item of equipment based on the output).
Citation of Pertinent Prior Art
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2141.02 VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, i.e., as a whole and 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record:
Zeiler, et al. USPGPub No. 2018/0089591 A1 discloses a service platform facilitates artificial intelligence model includes data collection, machine learning models and update plurality of machine learning models.
Reisser, et al. USPGPub No. 2023/0036702 A1 discloses a method of processing data receiving a set of global parameters for a plurality of machine learning models according to the set of global parameters to generate a machine learning model output with user feedback and performing optimization of plurality of machine learning models based on the machine learning output and the user feedback to generate locally updated machine learning model.
Le, et al. USP No. 12,120,073 B2 discloses a method for generating dynamic interface options using machine learning models generated in real time goals of user by interpreting multi-modal feature inputs.
Gupta, et al. USPGPub No. 2024/0036537 A1 discloses a building management system to control, monitor, and manage equipment in or around a building or building area for security system, a lighting system, a fire alerting system, any other system of managing building functions or devices.
Choi, et al. USPGPub No. 2025/0363361 A1 discloses a method for optimizing machine learning model generation obtaining learning data based on a plurality of features of the learning data and trained for selecting machine learning model having highest model accuracy for deployment.
Siebel, et al. USP No. 11,954,112 B2 discloses a system for big data analytics, data integration, processing, machine learning, to an enterprise Internet-of-Things (IoT) application development platform.
Veshchikov, et al. USP No. 11847545 B2 discloses a system for integrating machine learning models according to certain aspects to assimilate respective sets of output data from to create new data set related to train machine learning operations for a set of output data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Md Azad whose telephone @(571)272-0553 or email: md.azad@uspto.gov. The examiner can normally be reached on Mon-Thu 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached on (571)272-4105. 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 Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR for authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Md Azad/
Primary Examiner, Art Unit 2119
Prosecution Insights