DETAILED ACTION
This Office Action is in response to Applicant's application filed on 28 June 2023. Currently, claims 1-20 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8, 11-20 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (system, computer program product and method). Claims 1-8, 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1 and 11 recite the abstract idea of receive user input and a model to determine solutions to inquiries regarding data centers and a second model configured to determine rankings for solutions to inquiries regarding data centers and receiving an inquiry associated with a data center based on the user input and determining solutions to the inquiry and determining a ranking for each of the solutions to the inquiry. Claim 16 recites the abstract idea of receiving input from a user comprising a request to design a data center having selected performance characteristics and determining using a model one or more designs for the data center to achieve the selected performance characteristics, wherein model is configured to determine solutions to inquiries regarding data centers and determining, for each design of the one or more designs, a preference rating for the respective design based on historical user preference data and receiving selection input from the user selecting a first design of the one or more designs and receiving navigation input from the user comprising navigation commands. The claims are directed to a type of designing data centers based on user inputs. Under prong 1 of Step 2A, these claims are considered abstract because the claims are organized human activities including commercial interactions such as business relations. Applicant’s claims are organized human activities including commercial interactions such as business relations because the claims show designing a data center (a business which can be considered human commercial activity) where the design is based on determinations of received data (organizing) . Under prong 2 of Step 2A, the judicial exception is not integrated into a practical application because the claims (the judicial exception and any additional elements individually or in combination such as a system for designing a data center, the system comprising: a display device configured to display a graphical user interface, a non-transitory storage device comprising computer executable program code; and a processor in communication with the display device, the input device, the first machine learning model, the second machine learning model, and the non-transitory storage device, wherein the computer-executable program code comprises instructions configured to cause the processor to perform operations, and cause the display device to display the solutions to the inquiry via the graphical user interface in an order corresponding to the rankings and using an input device, using a first machine learning model, using a second machine learning model, displaying, using a display device, the one or more designs in order of the preference rating for each design of the one or more designs, displaying, using the display device and in response to receiving the selection input, a user-navigable three-dimensional representation of the data center configured according to the first design, adjusting, in response to receiving the navigation input, the displayed user-navigable three-dimensional representation of the data center based on the navigation commands and product comprising a non-transitory computer-readable medium comprising code that, when executed by a first apparatus, causes the first apparatus to perform steps) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply 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 claims as a whole is more than a drafting effort designed to monopolize the exception. These limitations at best are merely implementing an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as a system for designing a data center, the system comprising: a display device configured to display a graphical user interface, a non-transitory storage device comprising computer executable program code; and a processor in communication with the display device, the input device, the first machine learning model, the second machine learning model, and the non-transitory storage device, wherein the computer-executable program code comprises instructions configured to cause the processor to perform operations, and cause the display device to display the solutions to the inquiry via the graphical user interface in an order corresponding to the rankings and using an input device, using a first machine learning model, using a second machine learning model, displaying, using a display device, the one or more designs in order of the preference rating for each design of the one or more designs, displaying, using the display device and in response to receiving the selection input, a user-navigable three-dimensional representation of the data center configured according to the first design, adjusting, in response to receiving the navigation input, the displayed user-navigable three-dimensional representation of the data center based on the navigation commands and product comprising a non-transitory computer-readable medium comprising code that, when executed by a first apparatus, causes the first apparatus to perform steps (as evidenced by para [0082]-[0084], [0095]-[0108] of applicant’s own specification) are well understood, routine and conventional in the field. Dependent claims 2-8, 12, 15, 17-20 also do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements either individually or in combination are merely an extension of the abstract idea itself by further showing receive selection of a first solution of the solutions and wherein the inquiry comprises a request to change a configuration of the data center and in determining the solutions to the inquiry, determine one or more processes for changing the configuration of the data center; in determining the rankings for the solutions to the inquiry, determine, for each process of the one or more processes, a number of steps required to perform the respective process and before determining the solutions to the inquiry: identify keywords in the inquiry; and determine, based on the identified keywords, an intent of the inquiry; and in determining the solutions to the inquiry, determine the solutions to the inquiry based on the identified keywords and the intent of the inquiry and in determining the solutions to the inquiry, determine configurations of equipment in the data center having the performance characteristics; in determining the rankings for the solutions to the inquiry, determine, for each configuration of the configurations, a preference rating for the respective configuration based on historical user preference data and a selection of a first configuration of the configurations and a second inquiry comprising (i) a size of the data center and (ii) a request for a materials list and associated floor plan implementing the first configuration and a plurality of materials lists and associated floor plans implementing the first configuration in the data center corresponding to the size; determine, using the second machine learning model and for each materials list and associated floor plan of the plurality of materials lists and associated floor plans, a preference rating for the respective materials list and associated floor plan and wherein the inquiry comprises at least one of a question about the data center or a command to change a configuration of the data center. Dependent claims 2, 4-5, 7-8, 13-15, 17-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as an input device, in response to receiving the selection of the first solution, the display device to display a virtual performance of the first solution in a three-dimensional representation of the data center and adjust the graphical user interface to include the one or more processes in the order based on the number of steps required to perform each process of the one or more processes and cause, in response to receiving the selection of the first process, the display device to display virtual performances of each step in the series of steps in a three-dimensional representation of the data center and causing the display device to display the solutions to the inquiry, cause the display device to display the configurations in the order based on the preference rating for each configuration of the configurations and using the first machine learning model, cause the display device to display the plurality of materials lists and associated floor plans in another order based on the preference rating for each respective materials list and associated floor plan and determine one or more processes for moving the hot aisle the user-specified distance in the direction within the data center; in determining the rankings for the solutions to the inquiry, determine, for each process of the one or more processes, a number of steps required to perform the respective process and a cost associated with the respective process; and adjust the graphical user interface to comprise the one or more processes in the order based on a combined rating based on the number of steps required to perform each process and the cost associated with each process (as evidenced by para [0082]-[0084], [0095]-[0108] of applicant’s own specification) are well understood, routine and conventional in the field.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, 11-13, 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Healey et al. (US 2015/0331977 A1) in view of Gavin et al. (US 2024/0401833 A1).
Claims 1 and 11:
Healey, as shown, discloses the following limitations of claims 1 and 11:
A system (and corresponding computer program product – Fig. 12, showing equivalent computing functionality and components) for designing a data center, the system comprising: a display device configured to display a graphical user interface (see para [0033], " At least some embodiments in accordance with the present invention relate to systems and processes through which a user may design and analyze data center configurations. These systems and processes may facilitate this design and analysis activity by allowing the user to create models of data center configurations from which performance metrics may be determined. Both the systems and the user may employ these performance metrics to determine alternative data center configurations that meet various design objectives. According to one embodiment, systems and methods described herein employ performance metrics to generate three-dimensional airflow paths in a data center. The cross sectional area of the airflow paths is proportional to the airflow between an inlet and an exhaust of combinations of cooling consumers and producers. The airflow can include airflow between rack to rack, cooler to cooler, and rack to cooler combinations. The airflow paths method provides visualization of airflow in a manner that is more organized and straightforward than the traditional methods.");
an input device configured to receive user input (see para [0011], "According to another aspect, a method for modeling airflow is disclosed. In one example, the method comprises receiving data related to equipment including at least two of a group comprising a cooling consumer and a cooling provider computing, by a computer system, at least one quantity of airflow between an inlet and an outlet associated with the equipment, generating, by the computer system, a representation of at least one airflow path between the inlet and the outlet having a cross-sectional area proportional to the at least one quantity of airflow, and displaying, by the computer system, the representation of the at least one airflow path in a cooling model." and see para [0041]-[0042] and Fig. 1);
a model configured to determine solutions to inquiries regarding data centers (see para [0041], " In at least one embodiment, information regarding a data center is received by the system 100 through the interface, and assessments and recommendations for the data center are provided to the user. ");
…configured to determine rankings for solutions to inquiries regarding data centers (see para [0064], " According to various examples, the data center design and management system 106 can determine the fractional quantities of airflow associated with the cooling producers, for example 306a-d, and 406a-e, and generate the airflow paths, for example 308a-d and 408a-e, using the method described below. The interface 104 can display the airflow paths as 3D continuous rectangular streams from the sources of airflow to the destinations of airflow. In addition, the interface 104 can display the HACIs and CACIs and their associated fractional quantities of airflow for each rack. The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency.”);
comprising: receive, from the input device, an inquiry associated with a data center based on the user input (see para [0011], "According to another aspect, a method for modeling airflow is disclosed. In one example, the method comprises receiving data related to equipment including at least two of a group comprising a cooling consumer and a cooling provider computing, by a computer system, at least one quantity of airflow between an inlet and an outlet associated with the equipment, generating, by the computer system, a representation of at least one airflow path between the inlet and the outlet having a cross-sectional area proportional to the at least one quantity of airflow, and displaying, by the computer system, the representation of the at least one airflow path in a cooling model." and see para [0041]-[0042] and Fig. 1);
determine…solutions to the inquiry (see para [0041], " In at least one embodiment, information regarding a data center is received by the system 100 through the interface, and assessments and recommendations for the data center are provided to the user. ");
determine… a ranking for each of the solutions to the inquiry (see para [0064], " According to various examples, the data center design and management system 106 can determine the fractional quantities of airflow associated with the cooling producers, for example 306a-d, and 406a-e, and generate the airflow paths, for example 308a-d and 408a-e, using the method described below. The interface 104 can display the airflow paths as 3D continuous rectangular streams from the sources of airflow to the destinations of airflow. In addition, the interface 104 can display the HACIs and CACIs and their associated fractional quantities of airflow for each rack. The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency.”); and
cause the display device to display the solutions to the inquiry via the graphical user interface in an order corresponding to the rankings (see para [0064], " According to various examples, the data center design and management system 106 can determine the fractional quantities of airflow associated with the cooling producers, for example 306a-d, and 406a-e, and generate the airflow paths, for example 308a-d and 408a-e, using the method described below. The interface 104 can display the airflow paths as 3D continuous rectangular streams from the sources of airflow to the destinations of airflow. In addition, the interface 104 can display the HACIs and CACIs and their associated fractional quantities of airflow for each rack. The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency.”)
Healey further shows for claim 11:
display, in response to receiving the selection of the first solution and using the display device, a virtual performance of the first solution in a three-dimensional representation of the data center (see para [0048], " The traditional way to visualize temperatures in a data center is via a two-dimensional (2D) plane or slice through a three-dimensional (3D) model of a data center. FIG. 2A shows a traditional method of visualizing temperatures as a slice through the model of a data center having two rows of equipment separated by a hot aisle. The temperatures are color-keyed to specific temperature values. Similarly, airflow can be displayed as velocity vector arrows that lie within a 2D plane as shown in FIG. 2B. In addition, airflow can be displayed in a 3D manner at each location in the model intersected by the 2D plane as shown in FIG. 2C. To display temperatures and airflows in a 3D representation of the data center, a model of the data center can be divided into a grid of cells. For each cell in the grid, the temperatures and airflows can be calculated or measured and then displayed in the 3D model. Grid-based approaches provide a high level of granularity for data center performance." and see para [0064], " According to various examples, the data center design and management system 106 can determine the fractional quantities of airflow associated with the cooling producers, for example 306a-d, and 406a-e, and generate the airflow paths, for example 308a-d and 408a-e, using the method described below. The interface 104 can display the airflow paths as 3D continuous rectangular streams from the sources of airflow to the destinations of airflow. In addition, the interface 104 can display the HACIs and CACIs and their associated fractional quantities of airflow for each rack. The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency.”)
Healey, however, does not specifically disclose a first and second machine learning model. In analogous art, Gavin discloses the following limitations:
a first machine learning model configured to determine solutions to inquiries regarding data centers (see para [0004], "One or more aspects relate to building management systems and methods that implement, for example, building equipment servicing, optimization, self-healing, etc. For example, a system can include at least one machine learning model configured using training data that includes at least one of unstructured data or structured data regarding items of equipment. The system can provide inputs, such as prompts, to the at least one machine learning model regarding an item of equipment, and generate, according to the inputs, responses regarding the item of equipment, such as responses for detecting a cause of an issue of the item of equipment, performing a service operation corresponding to the cause, or guiding a user through the service operation. The machine learning model can include various machine learning model architectures (e.g., networks, backbones, algorithms, etc.), including but not limited to language models, LLMs, attention-based neural networks, transformer-based neural networks, generative pretrained transformer (GPT) models, bidirectional encoder representations from transformers (BERT) models, encoder/decoder models, sequence to sequence models, autoencoder models, generative adversarial networks (GANs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), diffusion models (e.g., denoising diffusion probabilistic models (DDPMs)), or various combinations thereof." and see para [0095], "In some embodiments, the optimization engine 120 may analyze or process natural language text or prompts to generate one or more optimization improvements for the building. For example, the optimization engine 120 may receive a prompt that asks, “why is room A always hot in the morning?” and the optimization engine 120 may generate an optimization improvement for the building based on the prompt. The optimization engine 120 may retrieve or access building specs, building layouts, room layouts, geographical data, or BIM models to determine a location of the room A with the building. The optimization engine 120 may determine that room A includes multiple windows that receive several hours of sunlight each day. The optimization engine 120 may generate or update a control schedule for blinds associated with the windows to reduce an amount of heat that is produced as a result of the sunlight." where it would be obvious to one of ordinary skill in the art that such inquiries for buildings and rooms could be implemented for data centers and function the same way);
a second machine learning model configured to determine rankings for solutions to inquiries regarding data centers (see para [0082], "As described further herein with respect to applications 120, in some implementations, the model updater 108 can select the training data from the data of the data sources 112 to apply as the input based at least on a particular application of the plurality of applications 120 for which the second model 116 is to be used for. For example, the model updater 108 can select data from the parts data source 112 for the product recommendation generator application 120, or select various combinations of data from the data sources 112 (e.g., engineering data, operational data, and service data) for the service recommendation generator application 120. The model updater 108 can apply various combinations of data from various data sources 112 to facilitate configuring the second model 116 for one or more applications 120." and see para [0092]-[0093], "The applications 120 can at least one service recommendation generator application 120. The service recommendation generator application 120 can receive inputs such as a service request or information regarding the item of equipment to be serviced, and provide the inputs to the second model 116 to cause the second model 116 to generate outputs for presenting service recommendations, such as actions to perform to address the service request. In some implementations, the applications 120 can include a product recommendation generator application 120. The product recommendation generator application 120 can process inputs such as information regarding the item of equipment or the service request, using one or more second models 116 (e.g., models trained using parts data from the data sources 112), to determine a recommendation of a part or product to replace or otherwise use for repairing the item of equipment.");
a non-transitory storage device comprising computer executable program code; and a processor in communication with the display device, the input device, the first machine learning model, the second machine learning model, and the non-transitory storage device, wherein the computer-executable program code comprises instructions configured to cause the processor to perform operations (see para [0005]-[0008] and Figs 1-2);
determine, using the first machine learning model, solutions to the inquiry (see para [0095], "In some embodiments, the optimization engine 120 may analyze or process natural language text or prompts to generate one or more optimization improvements for the building. For example, the optimization engine 120 may receive a prompt that asks, “why is room A always hot in the morning?” and the optimization engine 120 may generate an optimization improvement for the building based on the prompt. The optimization engine 120 may retrieve or access building specs, building layouts, room layouts, geographical data, or BIM models to determine a location of the room A with the building. The optimization engine 120 may determine that room A includes multiple windows that receive several hours of sunlight each day. The optimization engine 120 may generate or update a control schedule for blinds associated with the windows to reduce an amount of heat that is produced as a result of the sunlight." where it would be obvious to one of ordinary skill in the art that such inquiries for buildings and rooms could be implemented for data centers and function the same way);
determine, using the second machine learning model, a ranking for each of the solutions to the inquiry (see para [0082], "As described further herein with respect to applications 120, in some implementations, the model updater 108 can select the training data from the data of the data sources 112 to apply as the input based at least on a particular application of the plurality of applications 120 for which the second model 116 is to be used for. For example, the model updater 108 can select data from the parts data source 112 for the product recommendation generator application 120, or select various combinations of data from the data sources 112 (e.g., engineering data, operational data, and service data) for the service recommendation generator application 120. The model updater 108 can apply various combinations of data from various data sources 112 to facilitate configuring the second model 116 for one or more applications 120." and see para [0092]-[0093], "The applications 120 can at least one service recommendation generator application 120. The service recommendation generator application 120 can receive inputs such as a service request or information regarding the item of equipment to be serviced, and provide the inputs to the second model 116 to cause the second model 116 to generate outputs for presenting service recommendations, such as actions to perform to address the service request. In some implementations, the applications 120 can include a product recommendation generator application 120. The product recommendation generator application 120 can process inputs such as information regarding the item of equipment or the service request, using one or more second models 116 (e.g., models trained using parts data from the data sources 112), to determine a recommendation of a part or product to replace or otherwise use for repairing the item of equipment.")
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Gavin with Healy because using multiple machine learning models can improve the efficiency in managing and processing building systems depending on complex systems (see Gavin, para [0001]-[0003]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the building management system with AI as taught by Gavin in the system of visualizing airflow of Healey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 2:
Further, Healey discloses the following limitations:
receive, from the input device, selection of a first solution of the solutions (see para [0056], "FIG. 3 illustrates one example of a data center room 300 including airflow paths in a hot-aisle that are generated using the airflow paths visualization method described below. The two rows of equipment 302 shown in FIG. 3 are separated by a hot aisle and include an equipment rack 304, and in-row coolers 306a-d and other equipment racks which are not labeled. A data center operator or designer, via the interface 104, can select a particular equipment rack within the data center cluster to display airflow paths into and out of the selected equipment rack. While FIG. 3 shows the airflows paths associated with selected equipment rack 304, the operator or designer may select another equipment rack in the data center and the interface 104 can display the associated airflow paths for the other equipment racks." and see para [0082], "In one example, the airflow paths described above can be generated by the computer system for one object belonging to a single cold or hot aisle cluster at a given time. In some examples, this ensures that the displayed airflow paths do not become unclear or too busy and further ensures that streams do not cross. In at least one embodiment, the user, via the interface 104, may be able to select one object at a time and the data center design and management system 106 can calculate the airflow paths for the selected object and display the airflow paths to the user via the interface. As the user selects another object, the data center design and management system 106 can calculate and display the airflow paths for the subsequently selected object."); and
cause, in response to receiving the selection of the first solution, the display device to display a virtual performance of the first solution in a three-dimensional representation of the data center (see para [0064], "According to various examples, the data center design and management system 106 can determine the fractional quantities of airflow associated with the cooling producers, for example 306a-d, and 406a-e, and generate the airflow paths, for example 308a-d and 408a-e, using the method described below. The interface 104 can display the airflow paths as 3D continuous rectangular streams from the sources of airflow to the destinations of airflow. In addition, the interface 104 can display the HACIs and CACIs and their associated fractional quantities of airflow for each rack. The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency.")
Claims 3-4 and 12:
Healey does not explicitly disclose wherein the inquiry comprises a request to change a configuration of the data center. In analogous art, Gavin discloses the following limitations:
wherein the inquiry comprises a request/question to change a configuration of the data center (see para [0122], "The model system 260 can include a model configuration processor 264. The model configuration processor 264 can incorporate features of the model updater 108 and/or the feedback trainer 128 described with reference to FIG. 1. For example, the model configuration processor 264 can apply training data (e.g., prompts 248 and corresponding completions) to the machine learning models 268 to configure (e.g., train, modify, update, fine-tune, etc.) the machine learning models 268. The training manager 244 can control training by the model configuration processor 264 based on model tuning parameters in the model tuning database 256, such as to control various hyperparameters for training. In various implementations, the system 200 can use the training management system 240 to configure the machine learning models 268 in a similar manner as described with reference to the second model 116 of FIG. 1, such as to train the machine learning models 268 using any of various data or combinations of data from the data repository 204.").
determining the solutions to the inquiry, determine one or more processes for changing the configuration of the data center (see para [0050], "The system can be used to automate interventions for equipment operation, servicing, fault detection and diagnostics (FDD), and alerting operations. For example, by being configured to perform operations such as root cause prediction, the system can monitor data regarding equipment to predict events associated with faults and trigger responses such as alerts, service scheduling, and initiating FDD or modifications to configuration of the equipment. The system can present to a technician or manager of the equipment a report regarding the intervention (e.g., action taken responsive to predicting a fault or root cause condition) and requesting feedback regarding the accuracy of the intervention, which can be used to update the machine learning models to more accurately generate interventions.");
in determining the rankings for the solutions to the inquiry, determine, for each process of the one or more processes, a number of steps required to perform the respective process (see para [0051],"In some implementations, the systems, methods, and features of the present disclosure may be utilized to optimize and/or self-heal building equipment. In some such implementations, an AI model, such as a generative AI model (e.g., generative LLM), may be used to perform multi-variable optimization such that the AI model generates parameters to optimize against multiple variables (e.g., cost, energy usage, carbon emissions or other sustainability goals, comfort metrics, air quality or other health metrics, etc.). In some implementations, the AI model may self-heal issues with building equipment, such as by determining a root cause of an issue and a solution to the root cause and automatically taking actions to implement the solution." where it is obvious to one of ordinary skill in the art that a cost can be considered to show amount of steps given broadest reasonable interpretation); and
adjust the graphical user interface to include the one or more processes in the order based on the number of steps required to perform each process of the one or more processes (see para [0179]-[0180], "In some implementations, the AI model may take as input user feedback regarding one or more goals, or a balance of one or more goals. For example in some implementations, a user interface may be provided allowing the user to identify an absolute or relative amount of emphasis to put on one or more goals, or prioritize between a plurality of goals. In some such implementations, the interface may include a slider, multi-dimensional coordinate system, alphanumeric weighting interface, or other interface allowing the user to indicate a priority between multiple goals, such as between cost, energy usage, carbon emissions or other sustainability metrics, occupant comfort or health metrics, air quality, and/or other factors. In some implementations, the user may be provided with a conversational chat interface through which the user, via text, audio, visual, etc. input can indicate priorities or other input guidance for the model in determining the output parameters. For example, in some implementations, the input may be an unstructured natural language input, such as input that does not conform to a predetermined query ontology or input that conforms to a plurality of different query ontologies. In some such implementations, the model may be a generative AI model such as a generative large language model (LLM) (e.g., a generative AI chat model). In some implementations, one or more simulation models (e.g., AI or other machine learning models) may generate output simulation data, and the output simulation data may be used to train the AI model (e.g., a generative AI model trained using the simulation output, alone or in combination with other data). In some implementations, the tuning/optimization could be performed by the AI model based on recognition of patterns of behavior. In some implementations, the AI model could utilize building equipment operating data, schedule data, data from users, or other types of data to identify an occupancy pattern for a particular space, such as that a space is unoccupied by its occupant, or by any occupants, during a particular timeframe, certain days (e.g., due to remote work or vacations), etc. and change the operating parameters in response (such as lowering or raising a thermostat setpoint or taking other actions to reduce energy usage, cost, etc.)." and see para [0186], "In some implementations, the AI model can generate or modify a graphical interface indicating operating levels of different equipment or spaces, such as spaces/equipment that are operating at a high level or near optimal operation (e.g., above a first threshold level of operation), spaces/equipment that are operating at a medium level (e.g., below the first threshold level of operation and above a second threshold level of operation), and at a low level (e.g., below the second threshold). In some implementations, the operation could be shown in different colors (e.g., green, yellow, and red, respectively) or using other indicators. In some implementations, the data could be shown in a building information model (BIM) view, such as a 2D BIM view or floorplan or a 3D BIM view), or could be represented in some other fashion. In some implementations, the AI model can indicate information about the cause of the suboptimal operation of particular spaces/equipment. In some implementations, the AI model can show potential solutions to the determined problems/causes. In some such implementations, the AI model can provide a button or other input allowing the user to select to take an action with respect to such potential solutions, such as approving changes to building parameters or initiating a work order to fix particular equipment. In some such implementations, the AI model can autonomously take action to implement the potential solutions and show the change in operations pre- and post-implementation of the solutions in the graphical view.")
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the building management system with AI as taught by Gavin in the system of visualizing airflow of Healey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 5:
Further, Healey discloses the following limitations:
receive, from the input device, selection of a first process of the one or more processes, wherein the first process comprises a series of steps (see para [0041], "In other embodiments, interface 104 may be implemented with specialized facilities that enable user 102 to design, in a drag and drop fashion, a model that includes a representation of the physical layout of a data center or any subset thereof. This layout may include representations of data center structural components as well as data center equipment. The features of interface 104 are discussed further below. In at least one embodiment, information regarding a data center is received by the system 100 through the interface, and assessments and recommendations for the data center are provided to the user. Further, in at least one embodiment, optimization processes may be performed to optimize cooling performance and energy usage of the data center. For example, the design interface 104 may display aspect of the determined cooling performance, such as the cooling metrics discussed above and the airflow paths discussed below." where what is dragged and dropped in a layout can be considered a step given broadest reasonable interpretation); and
cause, in response to receiving the selection of the first process, the display device to display virtual performances of each step in the series of steps in a three-dimensional representation of the data center (see para [0037], "Adjacent rows of equipment racks separated by a cool aisle may be referred to as a cool aisle cluster, and adjacent rows of equipment racks separated by a hot aisle may be referred to as a hot aisle cluster. Further, single rows of equipment may also be considered to form both a cold and a hot aisle cluster by themselves. A row of equipment racks may be part of multiple hot aisle clusters and multiple cool aisle clusters. In descriptions and claims herein, equipment in racks, or the racks themselves, may be referred to as cooling consumers, and in-row cooling units and/or computer room air conditioners (CRACs) may be referred to as cooling providers. In the referenced applications, tools are provided for analyzing the cooling performance of a cluster of racks in a data center. In these tools, multiple analyses may be performed on different layouts to attempt to optimize the cooling performance of the data center.").
Claim 6:
Healey does not explicitly disclose determine, based on the identified keywords, an intent of the inquiry. In analogous art, Gavin discloses the following limitations:
before determining the solutions to the inquiry: identify keywords in the inquiry (see para [0095], where it is obvious to one of ordinary skill in the art that an optimization response to a prompt is able to process the text to determine the key words of the prompt); and
determine, based on the identified keywords, an intent of the inquiry (see para [0096], "In some embodiments, the optimization engine 120 may detect intents or sentiments associated with user queries to provide responses to address the user queries."); and
in determining the solutions to the inquiry, determine the solutions to the inquiry based on the identified keywords and the intent of the inquiry (see para [0096], showing insights or responses to the user queries).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the building management system with AI as taught by Gavin in the system of visualizing airflow of Healey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 7:
Further, Healey discloses the following limitations:
wherein the inquiry comprises performance characteristics (see para [0033], " At least some embodiments in accordance with the present invention relate to systems and processes through which a user may design and analyze data center configurations. These systems and processes may facilitate this design and analysis activity by allowing the user to create models of data center configurations from which performance metrics may be determined. Both the systems and the user may employ these performance metrics to determine alternative data center configurations that meet various design objectives. According to one embodiment, systems and methods described herein employ performance metrics to generate three-dimensional airflow paths in a data center. The cross sectional area of the airflow paths is proportional to the airflow between an inlet and an exhaust of combinations of cooling consumers and producers. The airflow can include airflow between rack to rack, cooler to cooler, and rack to cooler combinations. The airflow paths method provides visualization of airflow in a manner that is more organized and straightforward than the traditional methods."), and
wherein the computer-executable program code comprises instructions configured to cause the processor to: in determining the solutions to the inquiry, determine configurations of equipment in the data center having the performance characteristics (see para [0033], " At least some embodiments in accordance with the present invention relate to systems and processes through which a user may design and analyze data center configurations. These systems and processes may facilitate this design and analysis activity by allowing the user to create models of data center configurations from which performance metrics may be determined. Both the systems and the user may employ these performance metrics to determine alternative data center configurations that meet various design objectives. According to one embodiment, systems and methods described herein employ performance metrics to generate three-dimensional airflow paths in a data center. The cross sectional area of the airflow paths is proportional to the airflow between an inlet and an exhaust of combinations of cooling consumers and producers. The airflow can include airflow between rack to rack, cooler to cooler, and rack to cooler combinations. The airflow paths method provides visualization of airflow in a manner that is more organized and straightforward than the traditional methods.");
Healey does not specifically disclose a preference rating. In analogous art, Gavin discloses the following limitations:
in determining the rankings for the solutions to the inquiry, determine, for each configuration of the configurations, a preference rating for the respective configuration based on historical user preference data (see para [0102], "The feedback repository 124 can include feedback received from users regarding output presented by the applications 120. For example, for at least a subset of outputs presented by the applications 120, the applications 120 can present one or more user input elements for receiving feedback regarding the outputs. The user input elements can include, for example, indications of binary feedback regarding the outputs (e.g., good/bad feedback; feedback indicating the outputs do or do not meet the user's criteria, such as criteria regarding technical accuracy or precision); indications of multiple levels of feedback (e.g., scoring the outputs on a predetermined scale, such as a 1-5 scale or 1-10 scale); freeform feedback (e.g., text or audio feedback); or various combinations thereof." and see para [0083], "In some implementations, the system 100 can perform at least one of conditioning, classifier-based guidance, or classifier-free guidance to configure the second model 116 using the data from the data sources 112. For example, the system 100 can use classifiers associated with the data, such as identifiers of the item of equipment, a type of the item of equipment, a type of entity operating the item of equipment, a site at which the item of equipment is provided, or a history of issues at the site, to condition the training of the second model 116. For example, the system 100 combine (e.g., concatenate) various such classifiers with the data for inputting to the second model 116 during training, for at least a subset of the data used to configure the second model 116, which can enable the second model 116 to be responsive to analogous information for runtime/inference time operations."); and
in causing the display device to display the solutions to the inquiry, cause the display device to display the configurations in the order based on the preference rating for each configuration of the configurations (see para [0171]-[0172], " At 825, the completion is presented via the application session. For example, the completion can be presented as any of text, speech, audio, image, and/or video data to represent the completion, such as to provide an answer to a query represented by the prompt regarding an item of equipment or building management system. The completion can be presented via iterative generation of completions responsive to iterative receipt of prompts. The completion can be present with a user input element indicative of a request for feedback regarding the completion, such as to enable the prompt and completion to be used for updating the machine learning models. At 830, the machine learning model(s) used to generate the completion can be updated according to at least one of the prompt, the completion, or the feedback. For example, a training data element for updating the model can include the prompt, the completion, and the feedback, such as to represent whether the completion appropriately satisfied a user's request for information regarding the item of equipment. The machine learning models can be updated according to indications of accuracy determined by operations of the system such as accuracy checking, or responsive to evaluation of completions by experts (e.g., responsive to selective presentation and/or batch presentation of prompts and completions to experts).").
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the building management system with AI as taught by Gavin in the system of visualizing airflow of Healey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 13:
Further, Healey discloses the following limitations:
wherein the inquiry comprises a request to add cooling to a rack in the data center, and wherein the non-transitory computer-readable medium comprises code that, when executed by the first apparatus, causes the first apparatus to: in determining the solutions to the inquiry, determine one or more processes for adding cooling to the rack in the data center (see para [0055], "The airflow visualization method can be used to illustrate airflow between any source of airflow and any sink of airflow. For example, the airflow paths can be shown for any combinations of rack/cooler and inlet/outlet airflow tracking including rack to cooler, cooler to rack, rack to rack, or cooler to cooler airflow paths. Coolers and racks may be referred as cooling producers or providers and cooling consumers interchangeably. In addition, inlets and outlets may be referred to as inlets or originators and exhausts or outlets. While the fractional quantities of airflow described below are based on airflow associated with a rack, it is appreciated that the airflow paths specifically for a cooler can also be determined and displayed." and see para [0064], " According to various examples, the data center design and management system 106 can determine the fractional quantities of airflow associated with the cooling producers, for example 306a-d, and 406a-e, and generate the airflow paths, for example 308a-d and 408a-e, using the method described below. The interface 104 can display the airflow paths as 3D continuous rectangular streams from the sources of airflow to the destinations of airflow. In addition, the interface 104 can display the HACIs and CACIs and their associated fractional quantities of airflow for each rack. The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency.”);
in determining the rankings for the solutions to the inquiry, determine, for each process of the one or more processes, a number of steps required to perform the respective process and a cost associated with the respective process (see para [0064], where paths can be considered steps given broadest reasonable interpretation and see para [0108], “In at least some embodiments described above, the design of a facility (such as a data center) and/or actual parameters are altered based on predicted airflow in the facility. The alterations may be implemented to improve the cooling performance and/or may be implemented to provide cost and/or power savings when the performance is found to be within predetermined specifications. For example, the location of equipment racks may be changed and/or the types of racks or rack configurations may be changed. Further, based on determined airflow values, a data management system in accordance with one embodiment may control one or more CRACs or in-row cooling devices to adjust the airflow, and in addition, one or more equipment racks can be controlled to reduce power if the airflow from cooling providers is not adequate to provide sufficient cooling.”); and
adjust the graphical user interface to comprise the one or more processes in the order based on a combined rating based on the number of steps required to perform each process and the cost associated with each process (see para [0064]-[0065], "The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency. As discussed above, visualizing fractional quantities can provide significant advantages over the prior 3D visualization techniques. According to some examples, a specially configured computer system, such as the data center design and management system 106 described above, implements a process to automatically generate airflow paths within modeled data center rooms. FIG. 5 illustrates an airflow path generation process 500 in accord with these examples. The method can be used for any combinations of source/sink, rack/cooler, or inlet/outlet airflow tracking including rack to cooler, cooler to rack, rack to rack, or cooler to cooler airflow paths. In addition, the method described below can be used in any application where the source and destination of airflow can be represented visually, for example general building HVAC system design and maintenance.")
Claim 15:
Further, Healey discloses the following limitations:
wherein the inquiry comprises a request to design the data center to have user-specified performance characteristics (see para [0033], " At least some embodiments in accordance with the present invention relate to systems and processes through which a user may design and analyze data center configurations. These systems and processes may facilitate this design and analysis activity by allowing the user to create models of data center configurations from which performance metrics may be determined. Both the systems and the user may employ these performance metrics to determine alternative data center configurations that meet various design objectives. According to one embodiment, systems and methods described herein employ performance metrics to generate three-dimensional airflow paths in a data center. The cross sectional area of the airflow paths is proportional to the airflow between an inlet and an exhaust of combinations of cooling consumers and producers. The airflow can include airflow between rack to rack, cooler to cooler, and rack to cooler combinations. The airflow paths method provides visualization of airflow in a manner that is more organized and straightforward than the traditional methods."), and wherein the non- transitory computer-readable medium comprises code that, when executed by the first apparatus, causes the first apparatus to: in determining the solutions to the inquiry, determine one or more designs for the data center to achieve the selected performance characteristics (see para [0033], " At least some embodiments in accordance with the present invention relate to systems and processes through which a user may design and analyze data center configurations. These systems and processes may facilitate this design and analysis activity by allowing the user to create models of data center configurations from which performance metrics may be determined. Both the systems and the user may employ these performance metrics to determine alternative data center configurations that meet various design objectives. According to one embodiment, systems and methods described herein employ performance metrics to generate three-dimensional airflow paths in a data center. The cross sectional area of the airflow paths is proportional to the airflow between an inlet and an exhaust of combinations of cooling consumers and producers. The airflow can include airflow between rack to rack, cooler to cooler, and rack to cooler combinations. The airflow paths method provides visualization of airflow in a manner that is more organized and straightforward than the traditional methods.");
in displaying the virtual performance of the first solution in a three-dimensional representation of the data center, display a user-navigable three-dimensional representation of the data center (see para [0048], " The traditional way to visualize temperatures in a data center is via a two-dimensional (2D) plane or slice through a three-dimensional (3D) model of a data center. FIG. 2A shows a traditional method of visualizing temperatures as a slice through the model of a data center having two rows of equipment separated by a hot aisle. The temperatures are color-keyed to specific temperature values. Similarly, airflow can be displayed as velocity vector arrows that lie within a 2D plane as shown in FIG. 2B. In addition, airflow can be displayed in a 3D manner at each location in the model intersected by the 2D plane as shown in FIG. 2C. To display temperatures and airflows in a 3D representation of the data center, a model of the data center can be divided into a grid of cells. For each cell in the grid, the temperatures and airflows can be calculated or measured and then displayed in the 3D model. Grid-based approaches provide a high level of granularity for data center performance." and see para [0064], " According to various examples, the data center design and management system 106 can determine the fractional quantities of airflow associated with the cooling producers, for example 306a-d, and 406a-e, and generate the airflow paths, for example 308a-d and 408a-e, using the method described below. The interface 104 can display the airflow paths as 3D continuous rectangular streams from the sources of airflow to the destinations of airflow. In addition, the interface 104 can display the HACIs and CACIs and their associated fractional quantities of airflow for each rack. The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency.”);
receive, using the input device, navigation input from the user comprising navigation commands (see para [0064], "The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency." and where the airflow paths can be considered navigation commands as well given broadest reasonable interpretation); and
adjust, in response to receiving the navigation input and using the display device, the displayed user-navigable three-dimensional representation of the data center based on the navigation commands (see para [0064]-[0065], " According to various examples, the data center design and management system 106 can determine the fractional quantities of airflow associated with the cooling producers, for example 306a-d, and 406a-e, and generate the airflow paths, for example 308a-d and 408a-e, using the method described below. The interface 104 can display the airflow paths as 3D continuous rectangular streams from the sources of airflow to the destinations of airflow. In addition, the interface 104 can display the HACIs and CACIs and their associated fractional quantities of airflow for each rack. The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency. As discussed above, visualizing fractional quantities can provide significant advantages over the prior 3D visualization techniques. According to some examples, a specially configured computer system, such as the data center design and management system 106 described above, implements a process to automatically generate airflow paths within modeled data center rooms. FIG. 5 illustrates an airflow path generation process 500 in accord with these examples. The method can be used for any combinations of source/sink, rack/cooler, or inlet/outlet airflow tracking including rack to cooler, cooler to rack, rack to rack, or cooler to cooler airflow paths. In addition, the method described below can be used in any application where the source and destination of airflow can be represented visually, for example general building HVAC system design and maintenance.").
Healey, however, does not specifically disclose a preference rating for the respective design based on historical user preference data. In analogous art, Gavin discloses the following limitations:
in determining the rankings for the solutions to the inquiry, determine, for each design of the one or more designs, a preference rating for the respective design based on historical user preference data (see para [0102], "The feedback repository 124 can include feedback received from users regarding output presented by the applications 120. For example, for at least a subset of outputs presented by the applications 120, the applications 120 can present one or more user input elements for receiving feedback regarding the outputs. The user input elements can include, for example, indications of binary feedback regarding the outputs (e.g., good/bad feedback; feedback indicating the outputs do or do not meet the user's criteria, such as criteria regarding technical accuracy or precision); indications of multiple levels of feedback (e.g., scoring the outputs on a predetermined scale, such as a 1-5 scale or 1-10 scale); freeform feedback (e.g., text or audio feedback); or various combinations thereof." and see para [0083], "In some implementations, the system 100 can perform at least one of conditioning, classifier-based guidance, or classifier-free guidance to configure the second model 116 using the data from the data sources 112. For example, the system 100 can use classifiers associated with the data, such as identifiers of the item of equipment, a type of the item of equipment, a type of entity operating the item of equipment, a site at which the item of equipment is provided, or a history of issues at the site, to condition the training of the second model 116. For example, the system 100 combine (e.g., concatenate) various such classifiers with the data for inputting to the second model 116 during training, for at least a subset of the data used to configure the second model 116, which can enable the second model 116 to be responsive to analogous information for runtime/inference time operations.");
adjust the graphical user interface to comprise the one or more designs in the order based on the preference rating for each design of the one or more designs (see para [0171]-[0172], " At 825, the completion is presented via the application session. For example, the completion can be presented as any of text, speech, audio, image, and/or video data to represent the completion, such as to provide an answer to a query represented by the prompt regarding an item of equipment or building management system. The completion can be presented via iterative generation of completions responsive to iterative receipt of prompts. The completion can be present with a user input element indicative of a request for feedback regarding the completion, such as to enable the prompt and completion to be used for updating the machine learning models. At 830, the machine learning model(s) used to generate the completion can be updated according to at least one of the prompt, the completion, or the feedback. For example, a training data element for updating the model can include the prompt, the completion, and the feedback, such as to represent whether the completion appropriately satisfied a user's request for information regarding the item of equipment. The machine learning models can be updated according to indications of accuracy determined by operations of the system such as accuracy checking, or responsive to evaluation of completions by experts (e.g., responsive to selective presentation and/or batch presentation of prompts and completions to experts).")
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the building management system with AI as taught by Gavin in the system of visualizing airflow of Healey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 16:
Healey, as shown, discloses the limitations of claim 16:
A method for designing a data center, the method comprising: receiving, using an input device, input from a user comprising a request to design a data center having selected performance characteristics (see para [0033], "At least some embodiments in accordance with the present invention relate to systems and processes through which a user may design and analyze data center configurations. These systems and processes may facilitate this design and analysis activity by allowing the user to create models of data center configurations from which performance metrics may be determined. Both the systems and the user may employ these performance metrics to determine alternative data center configurations that meet various design objectives. According to one embodiment, systems and methods described herein employ performance metrics to generate three-dimensional airflow paths in a data center. The cross sectional area of the airflow paths is proportional to the airflow between an inlet and an exhaust of combinations of cooling consumers and producers. The airflow can include airflow between rack to rack, cooler to cooler, and rack to cooler combinations. The airflow paths method provides visualization of airflow in a manner that is more organized and straightforward than the traditional methods.");
determining, using a first machine learning model, one or more designs for the data center to achieve the selected performance characteristics, wherein the first machine learning model is configured to determine solutions to inquiries regarding data centers (see para [0033], " At least some embodiments in accordance with the present invention relate to systems and processes through which a user may design and analyze data center configurations. These systems and processes may facilitate this design and analysis activity by allowing the user to create models of data center configurations from which performance metrics may be determined. Both the systems and the user may employ these performance metrics to determine alternative data center configurations that meet various design objectives. According to one embodiment, systems and methods described herein employ performance metrics to generate three-dimensional airflow paths in a data center. The cross sectional area of the airflow paths is proportional to the airflow between an inlet and an exhaust of combinations of cooling consumers and producers. The airflow can include airflow between rack to rack, cooler to cooler, and rack to cooler combinations. The airflow paths method provides visualization of airflow in a manner that is more organized and straightforward than the traditional methods.");
receiving, using the input device, selection input from the user selecting a first design of the one or more designs (see para [0056], " FIG. 3 illustrates one example of a data center room 300 including airflow paths in a hot-aisle that are generated using the airflow paths visualization method described below. The two rows of equipment 302 shown in FIG. 3 are separated by a hot aisle and include an equipment rack 304, and in-row coolers 306a-d and other equipment racks which are not labeled. A data center operator or designer, via the interface 104, can select a particular equipment rack within the data center cluster to display airflow paths into and out of the selected equipment rack. While FIG. 3 shows the airflows paths associated with selected equipment rack 304, the operator or designer may select another equipment rack in the data center and the interface 104 can display the associated airflow paths for the other equipment racks.");
displaying, using the display device and in response to receiving the selection input, a user-navigable three-dimensional representation of the data center configured according to the first design (see para [0048], " The traditional way to visualize temperatures in a data center is via a two-dimensional (2D) plane or slice through a three-dimensional (3D) model of a data center. FIG. 2A shows a traditional method of visualizing temperatures as a slice through the model of a data center having two rows of equipment separated by a hot aisle. The temperatures are color-keyed to specific temperature values. Similarly, airflow can be displayed as velocity vector arrows that lie within a 2D plane as shown in FIG. 2B. In addition, airflow can be displayed in a 3D manner at each location in the model intersected by the 2D plane as shown in FIG. 2C. To display temperatures and airflows in a 3D representation of the data center, a model of the data center can be divided into a grid of cells. For each cell in the grid, the temperatures and airflows can be calculated or measured and then displayed in the 3D model. Grid-based approaches provide a high level of granularity for data center performance." and see para [0064], " According to various examples, the data center design and management system 106 can determine the fractional quantities of airflow associated with the cooling producers, for example 306a-d, and 406a-e, and generate the airflow paths, for example 308a-d and 408a-e, using the method described below. The interface 104 can display the airflow paths as 3D continuous rectangular streams from the sources of airflow to the destinations of airflow. In addition, the interface 104 can display the HACIs and CACIs and their associated fractional quantities of airflow for each rack. The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency.”);
receiving, using the input device, navigation input from the user comprising navigation commands (see para [0064], "The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency." and where the airflow paths can be considered navigation commands as well given broadest reasonable interpretation); and
adjusting, in response to receiving the navigation input, the displayed user-navigable three-dimensional representation of the data center based on the navigation commands (see para [0064]-[0065], " According to various examples, the data center design and management system 106 can determine the fractional quantities of airflow associated with the cooling producers, for example 306a-d, and 406a-e, and generate the airflow paths, for example 308a-d and 408a-e, using the method described below. The interface 104 can display the airflow paths as 3D continuous rectangular streams from the sources of airflow to the destinations of airflow. In addition, the interface 104 can display the HACIs and CACIs and their associated fractional quantities of airflow for each rack. The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency. As discussed above, visualizing fractional quantities can provide significant advantages over the prior 3D visualization techniques. According to some examples, a specially configured computer system, such as the data center design and management system 106 described above, implements a process to automatically generate airflow paths within modeled data center rooms. FIG. 5 illustrates an airflow path generation process 500 in accord with these examples. The method can be used for any combinations of source/sink, rack/cooler, or inlet/outlet airflow tracking including rack to cooler, cooler to rack, rack to rack, or cooler to cooler airflow paths. In addition, the method described below can be used in any application where the source and destination of airflow can be represented visually, for example general building HVAC system design and maintenance.")
Healey, however, does not explicitly disclose a preference rating. In analogous art, Gavin discloses the following limitations:
determining, using a second machine learning model and for each design of the one or more designs, a preference rating for the respective design based on historical user preference data (see para [0102], "The feedback repository 124 can include feedback received from users regarding output presented by the applications 120. For example, for at least a subset of outputs presented by the applications 120, the applications 120 can present one or more user input elements for receiving feedback regarding the outputs. The user input elements can include, for example, indications of binary feedback regarding the outputs (e.g., good/bad feedback; feedback indicating the outputs do or do not meet the user's criteria, such as criteria regarding technical accuracy or precision); indications of multiple levels of feedback (e.g., scoring the outputs on a predetermined scale, such as a 1-5 scale or 1-10 scale); freeform feedback (e.g., text or audio feedback); or various combinations thereof." and see para [0083], "In some implementations, the system 100 can perform at least one of conditioning, classifier-based guidance, or classifier-free guidance to configure the second model 116 using the data from the data sources 112. For example, the system 100 can use classifiers associated with the data, such as identifiers of the item of equipment, a type of the item of equipment, a type of entity operating the item of equipment, a site at which the item of equipment is provided, or a history of issues at the site, to condition the training of the second model 116. For example, the system 100 combine (e.g., concatenate) various such classifiers with the data for inputting to the second model 116 during training, for at least a subset of the data used to configure the second model 116, which can enable the second model 116 to be responsive to analogous information for runtime/inference time operations.");
displaying, using a display device, the one or more designs in order of the preference rating for each design of the one or more designs (see para [0171]-[0172], " At 825, the completion is presented via the application session. For example, the completion can be presented as any of text, speech, audio, image, and/or video data to represent the completion, such as to provide an answer to a query represented by the prompt regarding an item of equipment or building management system. The completion can be presented via iterative generation of completions responsive to iterative receipt of prompts. The completion can be present with a user input element indicative of a request for feedback regarding the completion, such as to enable the prompt and completion to be used for updating the machine learning models. At 830, the machine learning model(s) used to generate the completion can be updated according to at least one of the prompt, the completion, or the feedback. For example, a training data element for updating the model can include the prompt, the completion, and the feedback, such as to represent whether the completion appropriately satisfied a user's request for information regarding the item of equipment. The machine learning models can be updated according to indications of accuracy determined by operations of the system such as accuracy checking, or responsive to evaluation of completions by experts (e.g., responsive to selective presentation and/or batch presentation of prompts and completions to experts).");
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the building management system with AI as taught by Gavin in the system of visualizing airflow of Healey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 17:
Further, Healey discloses the following limitations:
after receiving the selection input selecting the first design: receiving, using the input device, inquiry input from the user comprising a request for a materials list implementing the first design (see para [0042]-[0043], " As shown in FIG. 1, the data center design and management system 106 presents design interface 104 to the user 102. According to one embodiment, data center design and management system 106 may include the data center design and management system as disclosed in PCT/US08/63675. In this embodiment, the design interface 104 may incorporate functionality of the input module, the display module and the builder module included in PCT/US08/63675 and may use the database module to store and retrieve data. As illustrated, the data center design and management system 106 may exchange information with the data center database 110 via the network 108. This information may include any information needed to support the features and functions of data center design and management system 106. For example, in one embodiment, data center database 110 may include at least some portion of the data stored in the data center equipment database described in PCT/US08/63675. In another embodiment, this information may include any information needed to support interface 104, such as, among other data, the physical layout of one or more data center model configurations, the production and distribution characteristics of the cooling providers included in the model configurations, the consumption characteristics of the cooling consumers in the model configurations, and a listing of equipment racks and cooling providers to be included in a cluster." where equipment racks and cooling providers can be considered materials in a data center given broadest reasonable interpretation);
Healey, however, does not explicitly disclose a preference rating. In analogous art, Gavin discloses the following limitations:
determining, using the first machine learning model, a plurality of materials lists implementing the first design (see para [0189]-[0190], " In some implementations, the AI model may ingest data from and/or export data into a digital twin of the building and/or digital twins of entities of the building (spaces, people, equipment, events, etc.). For example, in some implementations, the AI model can be trained using a digital twin of the building as input and use the digital twin as context to identify relationships between equipment and determine relationships or other characteristics that impact the performance level or health of the building devices, and use such context to determine potential root causes and solutions to suboptimal operation or issues with building equipment. The construction and arrangement of the systems and methods as shown in the various exemplary embodiments are illustrative only. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements may be reversed or otherwise varied and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present disclosure.");
determining, using the second machine learning model and for each materials list of the plurality of materials lists, a preference rating for the respective materials list (see para [0102], "The feedback repository 124 can include feedback received from users regarding output presented by the applications 120. For example, for at least a subset of outputs presented by the applications 120, the applications 120 can present one or more user input elements for receiving feedback regarding the outputs. The user input elements can include, for example, indications of binary feedback regarding the outputs (e.g., good/bad feedback; feedback indicating the outputs do or do not meet the user's criteria, such as criteria regarding technical accuracy or precision); indications of multiple levels of feedback (e.g., scoring the outputs on a predetermined scale, such as a 1-5 scale or 1-10 scale); freeform feedback (e.g., text or audio feedback); or various combinations thereof." and see para [0083], "In some implementations, the system 100 can perform at least one of conditioning, classifier-based guidance, or classifier-free guidance to configure the second model 116 using the data from the data sources 112. For example, the system 100 can use classifiers associated with the data, such as identifiers of the item of equipment, a type of the item of equipment, a type of entity operating the item of equipment, a site at which the item of equipment is provided, or a history of issues at the site, to condition the training of the second model 116. For example, the system 100 combine (e.g., concatenate) various such classifiers with the data for inputting to the second model 116 during training, for at least a subset of the data used to configure the second model 116, which can enable the second model 116 to be responsive to analogous information for runtime/inference time operations." ); and
displaying, using the display device, the plurality of materials lists in order of the preference rating for each materials list of the plurality of materials lists (see para [0171]-[0172], " At 825, the completion is presented via the application session. For example, the completion can be presented as any of text, speech, audio, image, and/or video data to represent the completion, such as to provide an answer to a query represented by the prompt regarding an item of equipment or building management system. The completion can be presented via iterative generation of completions responsive to iterative receipt of prompts. The completion can be present with a user input element indicative of a request for feedback regarding the completion, such as to enable the prompt and completion to be used for updating the machine learning models. At 830, the machine learning model(s) used to generate the completion can be updated according to at least one of the prompt, the completion, or the feedback. For example, a training data element for updating the model can include the prompt, the completion, and the feedback, such as to represent whether the completion appropriately satisfied a user's request for information regarding the item of equipment. The machine learning models can be updated according to indications of accuracy determined by operations of the system such as accuracy checking, or responsive to evaluation of completions by experts (e.g., responsive to selective presentation and/or batch presentation of prompts and completions to experts).").
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the building management system with AI as taught by Gavin in the system of visualizing airflow of Healey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 18:
Further, Healey discloses the following limitations:
receiving, from the input device, an inquiry associated with the data center (see para [0011], "According to another aspect, a method for modeling airflow is disclosed. In one example, the method comprises receiving data related to equipment including at least two of a group comprising a cooling consumer and a cooling provider computing, by a computer system, at least one quantity of airflow between an inlet and an outlet associated with the equipment, generating, by the computer system, a representation of at least one airflow path between the inlet and the outlet having a cross-sectional area proportional to the at least one quantity of airflow, and displaying, by the computer system, the representation of the at least one airflow path in a cooling model." and see para [0041]-[0042] and Fig. 1);
determining…solutions to the inquiry (see para [0041], " In at least one embodiment, information regarding a data center is received by the system 100 through the interface, and assessments and recommendations for the data center are provided to the user. ");
determining...a ranking for each of the solutions to the inquiry (see para [0064], " According to various examples, the data center design and management system 106 can determine the fractional quantities of airflow associated with the cooling producers, for example 306a-d, and 406a-e, and generate the airflow paths, for example 308a-d and 408a-e, using the method described below. The interface 104 can display the airflow paths as 3D continuous rectangular streams from the sources of airflow to the destinations of airflow. In addition, the interface 104 can display the HACIs and CACIs and their associated fractional quantities of airflow for each rack. The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency.”); and causing the display device to display the solutions to the inquiry via a graphical user interface in an order corresponding to the rankings (see para [0064], " According to various examples, the data center design and management system 106 can determine the fractional quantities of airflow associated with the cooling producers, for example 306a-d, and 406a-e, and generate the airflow paths, for example 308a-d and 408a-e, using the method described below. The interface 104 can display the airflow paths as 3D continuous rectangular streams from the sources of airflow to the destinations of airflow. In addition, the interface 104 can display the HACIs and CACIs and their associated fractional quantities of airflow for each rack. The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency.”).
Healey, however, does not specifically disclose a first and second machine learning model. In analogous art, Gavin disclose the following limitations:
a first machine learning model configured to determine solutions to inquiries regarding data centers (see para [0004], "One or more aspects relate to building management systems and methods that implement, for example, building equipment servicing, optimization, self-healing, etc. For example, a system can include at least one machine learning model configured using training data that includes at least one of unstructured data or structured data regarding items of equipment. The system can provide inputs, such as prompts, to the at least one machine learning model regarding an item of equipment, and generate, according to the inputs, responses regarding the item of equipment, such as responses for detecting a cause of an issue of the item of equipment, performing a service operation corresponding to the cause, or guiding a user through the service operation. The machine learning model can include various machine learning model architectures (e.g., networks, backbones, algorithms, etc.), including but not limited to language models, LLMs, attention-based neural networks, transformer-based neural networks, generative pretrained transformer (GPT) models, bidirectional encoder representations from transformers (BERT) models, encoder/decoder models, sequence to sequence models, autoencoder models, generative adversarial networks (GANs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), diffusion models (e.g., denoising diffusion probabilistic models (DDPMs)), or various combinations thereof." and see para [0095], "In some embodiments, the optimization engine 120 may analyze or process natural language text or prompts to generate one or more optimization improvements for the building. For example, the optimization engine 120 may receive a prompt that asks, “why is room A always hot in the morning?” and the optimization engine 120 may generate an optimization improvement for the building based on the prompt. The optimization engine 120 may retrieve or access building specs, building layouts, room layouts, geographical data, or BIM models to determine a location of the room A with the building. The optimization engine 120 may determine that room A includes multiple windows that receive several hours of sunlight each day. The optimization engine 120 may generate or update a control schedule for blinds associated with the windows to reduce an amount of heat that is produced as a result of the sunlight." where it would be obvious to one of ordinary skill in the art that such inquiries for buildings and rooms could be implemented for data centers and function the same way);
a second machine learning model configured to determine rankings for solutions to inquiries regarding data centers (see para [0082], "As described further herein with respect to applications 120, in some implementations, the model updater 108 can select the training data from the data of the data sources 112 to apply as the input based at least on a particular application of the plurality of applications 120 for which the second model 116 is to be used for. For example, the model updater 108 can select data from the parts data source 112 for the product recommendation generator application 120, or select various combinations of data from the data sources 112 (e.g., engineering data, operational data, and service data) for the service recommendation generator application 120. The model updater 108 can apply various combinations of data from various data sources 112 to facilitate configuring the second model 116 for one or more applications 120." and see para [0092]-[0093], "The applications 120 can at least one service recommendation generator application 120. The service recommendation generator application 120 can receive inputs such as a service request or information regarding the item of equipment to be serviced, and provide the inputs to the second model 116 to cause the second model 116 to generate outputs for presenting service recommendations, such as actions to perform to address the service request. In some implementations, the applications 120 can include a product recommendation generator application 120. The product recommendation generator application 120 can process inputs such as information regarding the item of equipment or the service request, using one or more second models 116 (e.g., models trained using parts data from the data sources 112), to determine a recommendation of a part or product to replace or otherwise use for repairing the item of equipment.");
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the building management system with AI as taught by Gavin in the system of visualizing airflow of Healey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 19:
Further, Healey discloses the following limitations:
receiving, from the input device, selection of a first solution of the solutions (see para [0056], " FIG. 3 illustrates one example of a data center room 300 including airflow paths in a hot-aisle that are generated using the airflow paths visualization method described below. The two rows of equipment 302 shown in FIG. 3 are separated by a hot aisle and include an equipment rack 304, and in-row coolers 306a-d and other equipment racks which are not labeled. A data center operator or designer, via the interface 104, can select a particular equipment rack within the data center cluster to display airflow paths into and out of the selected equipment rack. While FIG. 3 shows the airflows paths associated with selected equipment rack 304, the operator or designer may select another equipment rack in the data center and the interface 104 can display the associated airflow paths for the other equipment racks."); and
causing, in response to receiving the selection of the first solution, the display device to display a virtual performance of the first solution in the user-navigable three-dimensional representation of the data center (see para [0048], " The traditional way to visualize temperatures in a data center is via a two-dimensional (2D) plane or slice through a three-dimensional (3D) model of a data center. FIG. 2A shows a traditional method of visualizing temperatures as a slice through the model of a data center having two rows of equipment separated by a hot aisle. The temperatures are color-keyed to specific temperature values. Similarly, airflow can be displayed as velocity vector arrows that lie within a 2D plane as shown in FIG. 2B. In addition, airflow can be displayed in a 3D manner at each location in the model intersected by the 2D plane as shown in FIG. 2C. To display temperatures and airflows in a 3D representation of the data center, a model of the data center can be divided into a grid of cells. For each cell in the grid, the temperatures and airflows can be calculated or measured and then displayed in the 3D model. Grid-based approaches provide a high level of granularity for data center performance." and see para [0064], " According to various examples, the data center design and management system 106 can determine the fractional quantities of airflow associated with the cooling producers, for example 306a-d, and 406a-e, and generate the airflow paths, for example 308a-d and 408a-e, using the method described below. The interface 104 can display the airflow paths as 3D continuous rectangular streams from the sources of airflow to the destinations of airflow. In addition, the interface 104 can display the HACIs and CACIs and their associated fractional quantities of airflow for each rack. The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency.”).
Claim 20:
Further, Healey discloses the following limitations:
in determining the solutions to the inquiry, determining configurations of equipment in the data center having the performance characteristics (see para [0033], "At least some embodiments in accordance with the present invention relate to systems and processes through which a user may design and analyze data center configurations. These systems and processes may facilitate this design and analysis activity by allowing the user to create models of data center configurations from which performance metrics may be determined. Both the systems and the user may employ these performance metrics to determine alternative data center configurations that meet various design objectives. According to one embodiment, systems and methods described herein employ performance metrics to generate three-dimensional airflow paths in a data center. The cross sectional area of the airflow paths is proportional to the airflow between an inlet and an exhaust of combinations of cooling consumers and producers. The airflow can include airflow between rack to rack, cooler to cooler, and rack to cooler combinations. The airflow paths method provides visualization of airflow in a manner that is more organized and straightforward than the traditional methods.");
Healey does not explicitly disclose a preference rating for the respective configuration based on historical user preference data. In analogous art, Gavin discloses the following limitations:
in determining the rankings for the solutions to the inquiry, determining, for each configuration of the configurations, a preference rating for the respective configuration based on historical user preference data (see para [0102], "The feedback repository 124 can include feedback received from users regarding output presented by the applications 120. For example, for at least a subset of outputs presented by the applications 120, the applications 120 can present one or more user input elements for receiving feedback regarding the outputs. The user input elements can include, for example, indications of binary feedback regarding the outputs (e.g., good/bad feedback; feedback indicating the outputs do or do not meet the user's criteria, such as criteria regarding technical accuracy or precision); indications of multiple levels of feedback (e.g., scoring the outputs on a predetermined scale, such as a 1-5 scale or 1-10 scale); freeform feedback (e.g., text or audio feedback); or various combinations thereof." and see para [0083], "In some implementations, the system 100 can perform at least one of conditioning, classifier-based guidance, or classifier-free guidance to configure the second model 116 using the data from the data sources 112. For example, the system 100 can use classifiers associated with the data, such as identifiers of the item of equipment, a type of the item of equipment, a type of entity operating the item of equipment, a site at which the item of equipment is provided, or a history of issues at the site, to condition the training of the second model 116. For example, the system 100 combine (e.g., concatenate) various such classifiers with the data for inputting to the second model 116 during training, for at least a subset of the data used to configure the second model 116, which can enable the second model 116 to be responsive to analogous information for runtime/inference time operations."); and
in causing the display device to display the solutions to the inquiry, causing the display device to display the configurations in the order based on the preference rating for each configuration of the configurations (see para [0171]-[0172], " At 825, the completion is presented via the application session. For example, the completion can be presented as any of text, speech, audio, image, and/or video data to represent the completion, such as to provide an answer to a query represented by the prompt regarding an item of equipment or building management system. The completion can be presented via iterative generation of completions responsive to iterative receipt of prompts. The completion can be present with a user input element indicative of a request for feedback regarding the completion, such as to enable the prompt and completion to be used for updating the machine learning models. At 830, the machine learning model(s) used to generate the completion can be updated according to at least one of the prompt, the completion, or the feedback. For example, a training data element for updating the model can include the prompt, the completion, and the feedback, such as to represent whether the completion appropriately satisfied a user's request for information regarding the item of equipment. The machine learning models can be updated according to indications of accuracy determined by operations of the system such as accuracy checking, or responsive to evaluation of completions by experts (e.g., responsive to selective presentation and/or batch presentation of prompts and completions to experts).").
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the building management system with AI as taught by Gavin in the system of visualizing airflow of Healey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Healey and Gavin, as applied above, and further in view of Yendigeri et al. (US 2023/0141408 A1) (hereinafter Yendigeri).
Claims 9-10:
Healey and Gavin do not specifically disclose providing retraining data to another system for retraining the first machine learning model. In analogous art, Yendigeri discloses the following limitations:
wherein the first machine learning model is a supervised model trained using rule data comprising rules for configuring data center, historical solution data comprising historical configurations of data centers developed for customers based on customer requirements, and historical action data comprising historical actions performed on data centers and historical outcomes of the historical actions (see para [0002], "Some implementations described herein relate to a method. The method may include receiving historical project data identifying experiences and/or work product from previous projects and client data identifying a client with a problem, and processing the historical project data and the client data, with one or more machine learning models, to generate recommendations for the problem of the client and confidence scores for the recommendations. The method may include processing the recommendations and the confidence scores, with a natural language generation model, to generate a solution to the problem of the client and content for the solution, and generating a digitized dynamic client solution to the problem based on the solution and the content for the solution. The method may include providing the digitized dynamic client solution to one or more user devices, and receiving feedback on the digitized dynamic client solution from the one or more user devices. The method may include generating a final digitized dynamic client solution based on the feedback on the digitized dynamic client solution, and performing one or more actions based on the final digitized dynamic client solution." and see para [0043]-[0046], showing using supervised learning model on data for determining recommendations where it would be obvious to one of ordinary skill in the art that it could be applied to the configuring data center data shown the Healey and Gavin combination); and
wherein the computer-executable program code comprises instructions configured to cause the processor to provide retraining data to another system for retraining the first machine learning model, wherein the retraining data comprises inquiries received from the input device and outcomes associated with the inquiries (see para [0035]-[0036], "In some implementations, performing the one or more actions includes the solution system retraining the one or more machine learning models and/or the natural language generation model based on the final digitized dynamic client solution. For example, the solution system may utilize the final digitized dynamic client solution as additional training data for retraining the one or more machine learning models and/or the natural language generation model, thereby increasing the quantity of training data available for training the one or more machine learning models and/or the natural language generation model. Accordingly, the solution system may conserve computing resources associated with identifying, obtaining, and/or generating historical data for training the one or more machine learning models and/or the natural language generation model relative to other systems for identifying, obtaining, and/or generating historical data for training machine learning models. In this way, the solution system utilizes machine learning and natural language generation models to generate a digitized dynamic client solution. The solution system may utilize artificial intelligence and machine learning to eliminate manual dependencies in the creation of a client solution by automatically leveraging thought leadership content and by analyzing various client-related factors. The solution system may process the client solution, with natural language generation models, to produce layouts, paragraphs, and appropriate imagery for the client solution. The solution system may automatically combine the client solution, the layouts, the paragraphs, and the appropriate imagery together to generate a digitized dynamic client solution that provides a tailor-made experience for a potential client. The solution system drastically reduces the turn-around time for producing the digitized dynamic client solution from days (e.g., ten to fifteen days) to hours (e.g., six to eight hours). This, in turn, conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to create a timely client solution, coordinating various teams of personnel to generate an untimely client solution, losing business opportunities with the client due to the untimely client solution, and/or the like.")
wherein the second machine learning model is a supervised model trained using historical action data comprising historical actions performed on data centers and historical outcomes of the historical actions (see para [0002], "Some implementations described herein relate to a method. The method may include receiving historical project data identifying experiences and/or work product from previous projects and client data identifying a client with a problem, and processing the historical project data and the client data, with one or more machine learning models, to generate recommendations for the problem of the client and confidence scores for the recommendations. The method may include processing the recommendations and the confidence scores, with a natural language generation model, to generate a solution to the problem of the client and content for the solution, and generating a digitized dynamic client solution to the problem based on the solution and the content for the solution. The method may include providing the digitized dynamic client solution to one or more user devices, and receiving feedback on the digitized dynamic client solution from the one or more user devices. The method may include generating a final digitized dynamic client solution based on the feedback on the digitized dynamic client solution, and performing one or more actions based on the final digitized dynamic client solution." and see para [0043]-[0046], showing using supervised learning model on data for determining recommendations where it would be obvious to one of ordinary skill in the art that it could be applied to the configuring data center data shown the Healey and Gavin combination ); and
wherein the computer-executable program code comprises instructions configured to cause the processor to provide retraining data to another system for retraining the second machine learning model, wherein the retraining data comprises inquiries received from the input device and outcomes associated with the inquiries (see para [0035]-[0036], "In some implementations, performing the one or more actions includes the solution system retraining the one or more machine learning models and/or the natural language generation model based on the final digitized dynamic client solution. For example, the solution system may utilize the final digitized dynamic client solution as additional training data for retraining the one or more machine learning models and/or the natural language generation model, thereby increasing the quantity of training data available for training the one or more machine learning models and/or the natural language generation model. Accordingly, the solution system may conserve computing resources associated with identifying, obtaining, and/or generating historical data for training the one or more machine learning models and/or the natural language generation model relative to other systems for identifying, obtaining, and/or generating historical data for training machine learning models. In this way, the solution system utilizes machine learning and natural language generation models to generate a digitized dynamic client solution. The solution system may utilize artificial intelligence and machine learning to eliminate manual dependencies in the creation of a client solution by automatically leveraging thought leadership content and by analyzing various client-related factors. The solution system may process the client solution, with natural language generation models, to produce layouts, paragraphs, and appropriate imagery for the client solution. The solution system may automatically combine the client solution, the layouts, the paragraphs, and the appropriate imagery together to generate a digitized dynamic client solution that provides a tailor-made experience for a potential client. The solution system drastically reduces the turn-around time for producing the digitized dynamic client solution from days (e.g., ten to fifteen days) to hours (e.g., six to eight hours). This, in turn, conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to create a timely client solution, coordinating various teams of personnel to generate an untimely client solution, losing business opportunities with the client due to the untimely client solution, and/or the like.")
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Yendigeri with Healy and Gavin because integrating retraining into the models enables more desirable solutions to clients (see Yendigeri, para [0001]-[0002]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for utilizing machine learning models to generate a digitized dynamic client solution as taught by Yendigeri in the Healey and Gavin combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 14 is rejected under 35 U.S.C. 103 as being unpatentable over Healey and Gavin, as applied above, and further in view of Brey et al. (US 2009/0234613 A1) (hereinafter Brey).
Claim 14:
Further, Healey discloses the following limitations:
in determining the rankings for the solutions to the inquiry, determine, for each process of the one or more processes, a number of steps required to perform the respective process and a cost associated with the respective process (see para [0064], where paths can be considered steps given broadest reasonable interpretation and see para [0108], “In at least some embodiments described above, the design of a facility (such as a data center) and/or actual parameters are altered based on predicted airflow in the facility. The alterations may be implemented to improve the cooling performance and/or may be implemented to provide cost and/or power savings when the performance is found to be within predetermined specifications. For example, the location of equipment racks may be changed and/or the types of racks or rack configurations may be changed. Further, based on determined airflow values, a data management system in accordance with one embodiment may control one or more CRACs or in-row cooling devices to adjust the airflow, and in addition, one or more equipment racks can be controlled to reduce power if the airflow from cooling providers is not adequate to provide sufficient cooling.”); and
adjust the graphical user interface to comprise the one or more processes in the order based on a combined rating based on the number of steps required to perform each process and the cost associated with each process (see para [0064]-[0065], "The data center designer or operator can view the airflow paths and the fractional quantities displayed via the interface 104 and make decisions such as placement of additional equipment in the data center such as additional cooling consumers or producers. The data center designer or operator can also arrange equipment in the data center based on the airflow paths, such as moving cooling consumers or cooling producers for added efficiency. As discussed above, visualizing fractional quantities can provide significant advantages over the prior 3D visualization techniques. According to some examples, a specially configured computer system, such as the data center design and management system 106 described above, implements a process to automatically generate airflow paths within modeled data center rooms. FIG. 5 illustrates an airflow path generation process 500 in accord with these examples. The method can be used for any combinations of source/sink, rack/cooler, or inlet/outlet airflow tracking including rack to cooler, cooler to rack, rack to rack, or cooler to cooler airflow paths. In addition, the method described below can be used in any application where the source and destination of airflow can be represented visually, for example general building HVAC system design and maintenance.").
Healey and Gavin do not specifically disclose determine one or more processes for moving the hot aisle the user-specified distance in the direction within the data center. In analogous art, Brey discloses the following limitations:
wherein the inquiry comprises a request to move a hot aisle of the data center a user-specified distance in a direction (see para [0021], " The layout may be analyzed in view of selected criteria to determine its degree of optimization relative to the selected criteria. For example, the software may compare the actual rack spacing and resulting aisle dimensions to predefined target values of these parameters. The software may further identify problems with the layout, such as overlapping hot- and cold-aisles. The software may score the layout based on its degree of optimization. Point penalties may be assessed for deviations from the target values and any detected layout problems. The software may store and/or display a representation of the layout, any detected layout problems, and the layout score on a workstation. This information enables datacenter personnel to improve the configuration of the datacenter. Such improvements may include repositioning racks to correct the datacenter layout. If a particularly egregious problem is detected, the software may further invoke an automated emergency response, such as by powering off or at least reducing the power state of components in the affected racks until corrective action has been taking by personnel." and see para [0041], " The system designer for racks and rack components may specify target values for selected layout parameters to guide the datacenter personnel in arranging the racks in the datacenter."), and
wherein the non-transitory computer-readable medium comprises code that, when executed by the first apparatus, causes the first apparatus to: in determining the solutions to the inquiry, determine one or more processes for moving the hot aisle the user-specified distance in the direction within the data center (see para [0031]-[0032], " Various layout parameters such as rack footprints and aisle dimensions may be computed from the determined reference planes (aisle boundaries) 41-46 and 81-86. For example, the distance D1 between the reference planes 41, 42 (coinciding with the depth dimension of the racks) and the width W of each rack 12 in row 12A determine the footprint of the racks 12 in row 12A. The length of the entire row 12A may be determined, for example, as the horizontal distance between the two furthest-spaced temperature sensors in a given reference plane. For example, W may be calculated as the horizontal distance between the furthest-spaced temperature sensors in intake plane 41, which distance may be computed as the square root of the sum of (x.sub.1-x.sub.2).sup.2 and (y.sub.1-y.sub.2).sup.2. The dimensions of aisles may be computed from the distance between adjacent racks. For example, the mean width HA1 ("Hot Aisle 1") of the shared hot aisle 26 between the racks 12A, 12B may be determined from the distance between the reference planes 42 and 43. The mean width CA1 ("Cold Aisle 1") of the shared cold aisle 21 between the racks 12B and 12C may be determined from the distance between the reference planes 44, 45. Additional layout parameters, such as an angle .theta. between the racks 12B, 12C, may also be determined from the computed reference planes 41-46. These additional layout parameters may also be computed by the software 16 at the workstation 14 and electronically displayed in a representative display window 50 on the display 18 of the workstation 14.")
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Brey with Healy and Gavin because moving a hot aisle enables more effective management of the air circulation in a data center (see Brey, para [0004]-[0006]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the method for analyzing the layout of computer equipment racks in a datacenter as taught by Brey in the Healey and Gavin combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Allowable Subject Matter
Claim 8 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kumar et al. (US 2024/0394422 A1), a system generating virtual representations of architecture, engineering, and construction (AEC) smart constructs where a controller that determines user intent based on an analysis of a user input and further determines project objective constraints based on an evaluation of project objectives and the knowledge units are computed based on a plurality of nodes and a plurality of interdependencies of a computational graph
Chen (WO 2022135347 A1), a system for data center 3D modeling by obtaining a basic 3D model of a data center according to geometric elements in a plan view of the data center; and filling, according to markers in the plan view, models corresponding to the markers in marked positions in the basic 3D model, and generating a 3D model of the data center, the markers having one-to-one correspondence to the models in a preset model library
Heslin "Implementing Data Center Cooling Best Practices", a guide to data center cooling best practices will help data center managers take greater advantage of the energy savings opportunities available while providing improved cooling of IT systems
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUJAY KONERU whose telephone number is 571-270-3409. The examiner can normally be reached on Monday-Friday, 9 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached on 571- 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SUJAY KONERU/
Primary Examiner, Art Unit 3624
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