Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This action is in response to the original filing on 09/06/2024. Claims 1-14 are pending and have been considered below.
Information Disclosure Statement
3. The information disclosure statement (IDS(s)) submitted on 09/06/2024 is/are 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
4. 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 1-12 and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 recites a “commissioning apparatus,” the claim does not recite any physical or tangible structure, such as processor, computer, or other hardware component. Rather, the body of the claim recites only functional operations performed using a machine learning model, a commissioning model, and a recommendation system, including detecting patterns, comparing data, determining modelling data, determining configuration data, and using the configuration data for commissioning. Accordingly, the recited “commissioning apparatus” is computer software per se and is not a “process,” a “machine,” a “manufacture” or a “composition of matter,” as defined in 35 U.S.C. 101.
Claims 2 - 12 are dependent upon claim 1, incorporate the deficiencies of claim 1 and do not add tangibility to the claimed subject matter, they are likewise rejected.
Claim 14 recites a “computer program product”, however the specification is silent as to the particular details of what the “computer program product” comprises. It is noted that the ordinary meaning of the “computer program product” recited in the claim encompasses signals, carrier waves or other transmission media which is non-statutory. Therefore, the “computer program product” is not limited to physical articles or objects which constitute a manufacture within the meaning of 35 USC 101 and enable any functionality of the instructions carried thereby to act as a computer component and realize their functionality. As such, the claim is not limited to statutory subject matter and is therefore non-statutory.
Claim Rejections – 35 USC § 103
5. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
6. Claims 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over Schmitt et al. (U.S. Patent Application Pub. No. US 20210223748 A1) in view of Yin et al. (U.S. Patent Application Pub. No. US 20220092227 A1).
Claim 1: Schmitt teaches a commissioning apparatus configured to (i.e. a method for providing an evaluation and control model for controlling target building automation devices of a target building automation system; para. [0017]):
detect, using a model (i.e. semantic matchmaking to compare different semantic descriptions; para. [0022-0024]), repeatable patterns of a physical space of a non-commissioned lighting system (i.e. in a typical office building many rooms have similarities regarding room-functionality and installed sensors and actuators—these similarities shall be used to auto-align sensor/actuator data with the model … This context of a model can be used to decide which model can be re-used on which other rooms … the pre-trained model can be re-used for many similar rooms. This reduces the configuration efforts and speeds up the initial phase of model-based brightness-control for all similar rooms; para. [0035, 0037, 0038]) by identifying similar portions in a commissioned lighting system (i.e. The present invention is based on the idea to use the description of the context, in which a trained model which is used for evaluating input data of building automation devices and controlling the associated building automation devices of a building automation system, to provide a kind of fingerprint of the model. If a context can be described using a semantic or functional abstraction, similarities between the training context and the target context can be made … FIG. 3, three different pre-trained source evaluation and control models which were trained before in three different source building automation systems 10, 20, 30 are available for transfer to the target building automation system 40, that is to be uploaded to the central control unit of the target building automation system 40; para. [0021, 0036, 0049]), wherein the non-commissioned lighting system is a lighting system where lighting controls are not activated (i.e. the present invention a model which is displayed in the list on the display screen may be automatically selected on basis of the matching score and uploaded to the target building automation system. Regardless of the selection process, the time-consuming task of (re-)training the model in the target building automation system can be avoided which leads to faster deployment and applicability of evaluation and control models to a given new context; para. [0030]) and the commissioned lighting system is a lighting system where the lighting controls are activated (i.e. The starting point here is a pre-trained source evaluation and control model which was trained in the context of a source building automation system; para. [0047]);
compare, using a commissioning model, the detected repeatable patterns with installation data of at least one commissioned lighting system (i.e. matching of the generated semantic based description of the context of the target building automation system and the semantic based description of the context in which the pre-trained source evaluation and control models were trained by using a semantic match-making concept; para. [0020, 0022, 0036, 0048]), wherein the at least one commissioned lighting system is different from the non-commissioned lighting system (i.e. FIG. 3, three different pre-trained source evaluation and control models which were trained before in three different source building automation systems 10, 20, 30 are available for transfer to the target building automation system 40, that is to be uploaded to the central control unit of the target building automation system 40; para. [0049]);
determine, using the commissioning model, modelling data representative of commissioning steps in portions similar to the detected repeatable patterns of the commissioned lighting system based on the result of the comparison (i.e. based on the matchmaking, the selection of a suitable pre-trained source evaluation and control model and mechanisms to handle differences in the context of the pre-trained source evaluation and control model and the context of the targeted building automation system can be integrated automatically … This context of a model can be used to decide which model can be re-used on which other rooms (e.g. same room-function, same position, same users, . . . ), which sensors are required and have to be considered (e.g. similar type and placement—and if not perfectly matching how the sensors can be adapted e.g. un-bias the brightness value), and how the data has to be preprocessed (e.g. using the same averaging function) in order to re-use the model; para. [0025, 0037, 0038, 0040]);
determine, using a recommendation system, configuration data representative of recommendations of control rules required for commissioning the non-commissioned lighting system, based on the modelling data (i.e. a recommender system can make use of the similarities to select and propose the pre-trained source evaluation and control model which suits best for a new target context; para. [0026, 0041, 0044]); and
using the determined configuration data for commissioning the non-commissioned lighting system (i.e. a model which is displayed in the list on the display screen may be automatically selected on basis of the matching score and uploaded to the target building automation system; para. [0027, 0030, 0040]).
Schmitt does not explicitly teach detect patterns using a machine learning model. However, Yin teaches detect, using a machine learning model (i.e. one or more trained similarity machine learning models (e.g., one or more trained neural networks) to determine a degree of similarity between the two buildings; para. [0097]), repeatable patterns of a physical space (i.e. After an adjacency graph and optionally an embedding vector is generated for a floor plan of a building, that generated information may be used by the FPSDM system as specified criteria to automatically determine one or more other similar floor plans of other buildings; para. [0015, 0016]) by identifying similar portions (i.e. an initial floor plan is identified, and corresponding adjacency information for the initial floor plan is generated and compared to generated and/or provided adjacency information for multiple other candidate floor plans in order to determine differences (e.g., using distance measures or other measures of similarities) between the initial floor plan's adjacency information and the adjacency information of some or all of the candidate floor plans. The one or more such similarity neural networks are part of the FPSDM system and are trained using unlabeled or labeled data to identify similar floor plans, such as via supervised learning using labeled data or instead via unsupervised clustering using unlabeled data. Additional details are included below regarding comparing adjacency graphs or other adjacency information generated for floor plans to determine similarities of the floor plans and/or of the associated buildings more generally, including with respect to the examples of FIGS. 2D-2K and their associated description.; para. [0018, 0035, 0100]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Schmitt to include the feature of Yin. One would have been motivated to make this modification because using ML models to automate similarity determination between building floor plans so that matching spaces can be identified more efficiently, more rapidly, and also more accurately.
Claim 2: Schmitt and Yin teach the apparatus of claim 1. Schmitt further teaches wherein the repeatable patterns comprise at least one of areas and objects in a floor or building or site plan of the non-commissioned lighting system (i.e. in a typical office building many rooms have similarities regarding room-functionality and installed sensors and actuators—these similarities shall be used to auto-align sensor/actuator data with the model. To evaluate these similarities on a technical level the aspects which are relevant to determine similarities have to made available on a technical level, the context; para. [0022, 0035, 0036]).
Yin further teaches wherein the repeatable patterns comprise at least one of areas and objects in a floor or building or site plan of the non-commissioned lighting system (i.e. Such a floor plan of a building may include a 2D (two-dimensional) representation of various information about the building (e.g., the rooms, doorways between rooms and other inter-room connections, exterior doorways, windows, etc.), and may be further associated with various types of supplemental or otherwise additional information … After an adjacency graph and optionally an embedding vector is generated for a floor plan of a building, that generated information may be used by the FPSDM system as specified criteria to automatically determine one or more other similar floor plans of other buildings; para. [0012, 0015, 0016]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Schmitt to include the feature of Yin. One would have been motivated to make this modification because using ML models to automate similarity determination between building floor plans so that matching spaces can be identified more efficiently, more rapidly, and also more accurately.
Claim 3: Schmitt and Yin teach the apparatus of claim 1. Schmitt further teaches wherein the apparatus is configured to use a model for detecting the repeatable patterns in a floor or building or site plan (i.e. in a typical office building many rooms have similarities regarding room-functionality and installed sensors and actuators—these similarities shall be used to auto-align sensor/actuator data with the model … This context of a model can be used to decide which model can be re-used on which other rooms … the pre-trained model can be re-used for many similar rooms. This reduces the configuration efforts and speeds up the initial phase of model-based brightness-control for all similar rooms; para. [0035, 0037, 0038]).
Schmitt does not explicitly teach a convolutional neural network as the machine learning model.
Yin further teaches wherein the apparatus is configured to use a convolutional neural network as the machine learning model for detecting the repeatable patterns in a floor or building or site plan (i.e. the described techniques include using machine learning to learn the attributes and/or other characteristics of adjacency graphs to encode in corresponding embedding vectors that are generated, such as the attributes and/or other characteristics that best enable subsequent automated identification of building floor plans having attributes satisfying target criteria, and with the embedding vectors that are used in at least some embodiments to identify target building floor plans being encoded based on such learned attributes or other characteristics … Various techniques exist for extending and re-defining convolutions in the graph domain; para. [0065, 0066]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Schmitt to include the feature of Yin. One would have been motivated to make this modification because using ML models to automate similarity determination between building floor plans so that matching spaces can be identified more efficiently, more rapidly, and also more accurately.
Claim 4: Schmitt and Yin teach the apparatus of claim 1. Schmitt further teaches wherein the apparatus is configured to predict commissioning steps based on at least one of, floor plan, distance of luminaire from user, relationship between areas of the physical space, and user preference (i.e. it may be necessary that the context in which the pre-trained source evaluation and control model was trained, includes user specific information such as identification information and personal preferences. The same concept may be used in order to determine if and how a pre-trained user-specific evaluation and control model can be applied to the sensors in the new room in order to control the actuators in the room; para. [0026, 0031, 0043]).
Claim 5: Schmitt and Yin teach the apparatus of claim 1. Schmitt further teaches wherein the apparatus is configured to determine the configuration data by taking into account commissioned features of a space corresponding to the detected repeatable pattern in the commissioned lighting system (i.e. in a typical office building many rooms have similarities regarding room-functionality and installed sensors and actuators—these similarities shall be used to auto-align sensor/actuator data with the model. To evaluate these similarities on a technical level the aspects which are relevant to determine similarities have to made available on a technical level, the context … in the targeted scenario the context of a model that was trained to control the brightness of a room can contain information about the sensors (type and placement of the brightness-sensors, type and placement of the presence-sensors, . . . ), the actuators (type and placement of the lights to be controlled, type and placement of the blinds, . . . ); para. [0035-0038]).
Claim 6: Schmitt and Yin teach the apparatus of claim 1. Schmitt further teaches wherein the apparatus is configured to commission the non-commissioned lighting system with regard to at least one of luminaires, sensors, controllers, gateways, software clients and software server (i.e. FIG. 3, three different pre-trained source evaluation and control models which were trained before in three different source building automation systems 10, 20, 30 are available for transfer to the target building automation system 40, that is to be uploaded to the central control unit of the target building automation system 40. The context A, B, C of each source building automation system 10, 20, 30 is indicated by means of the placement of sensors 1, lighting devices 2, windows 3 and a controller 4 which controls the sensors 1 and the lighting devices 2 and all other building automation devices, via electrical connection lines 5; para. [0049, 0050]).
Claim 7: Schmitt and Yin teach the apparatus of claim 1. Schmitt further teaches wherein the apparatus is configured to optimize design rules of lighting commissioning by equally applying a set of rules to identified similar portions and/or to optimize an effort for traversing the physical space for commissioning (i.e. in a typical office building many rooms have similarities regarding room-functionality and installed sensors and actuators—these similarities shall be used to auto-align sensor/actuator data with the model. To evaluate these similarities on a technical level the aspects which are relevant to determine similarities have to made available on a technical level, the context … This context of a model can be used to decide which model can be re-used on which other rooms (e.g. same room-function, same position, same users, . . . ), which sensors are required and have to be considered (e.g. similar type and placement); para. [0035-0038]).
Claim 8: Schmitt and Yin teach the apparatus of claim 1. Schmitt further teaches wherein the apparatus is configured to identify repeatable physical spaces to apply various lighting rules for a granular space and replicate application of lighting rules across the identified repeatable physical spaces (i.e. in a typical office building many rooms have similarities regarding room-functionality and installed sensors and actuators—these similarities shall be used to auto-align sensor/actuator data with the model. To evaluate these similarities on a technical level the aspects which are relevant to determine similarities have to made available on a technical level, the context … the pre-trained model can be re-used for many similar rooms. This reduces the configuration efforts and speeds up the initial phase of model-based brightness-control for all similar rooms; para. [0035-0038]).
Schmitt does not explicitly teach identify physical spaces at various granularity.
Yin further teaches wherein identify repeatable physical spaces at various granularity (i.e. a floor plan may have various information that is associated with individual rooms and/or with inter-room connections and/or with a corresponding building as a whole; para. [0015, 0087]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Schmitt to include the feature of Yin. One would have been motivated to make this modification because it allows floor plans of multi-room buildings and other structures to be identified and used more efficiently and rapidly and in manners not previously available.
Claim 9: Schmitt and Yin teach the apparatus of claim 1. Schmitt further teaches wherein the determined configuration data comprises at least one of a number of luminaires, a number of daylight sensors, a number of occupancy sensors, a position of luminaires and sensors, a type of dynamic lighting configuration, a number of groups of luminaires and their light intensity, and a number of subgroups of luminaires and their light intensity (i.e. In the targeted scenario the context of a model that was trained to control the brightness of a room can contain information about the sensors (type and placement of the brightness-sensors, type and placement of the presence-sensors, . . . ), the actuators (type and placement of the lights to be controlled, type and placement of the blinds, . . . ), the targeted function of the model (control the light based on presence and brightness), the function of the room (e.g. meeting-room, kitchen, or office), the preprocessing (e.g. separated averaging of the brightness sensors inside the room and outside the building), the room-orientation (e.g. south side of the building in the first floor), and potentially also the users who were considered for training; para. [0036]).
Claim 10: Schmitt and Yin teach the apparatus of claim 1. Schmitt further teaches wherein the apparatus is configured to identify the similar portions based on at least one of a size of a room, a type of a room, a height of a room, an exposed area of a room, an ambient light of a room, positions of commissioned luminaires and/or sensors of a room, numbers of commissioned luminaires and/or sensors of a room, and number of groups and/or subgroups of luminaires in a room (i.e. in a typical office building many rooms have similarities regarding room-functionality and installed sensors and actuators—these similarities shall be used to auto-align sensor/actuator data with the model … the function of the room (e.g. meeting-room, kitchen, or office); para. [0035-0037, 0044]).
Claim 11: Schmitt and Yin teach the apparatus of claim 1. Schmitt further teaches wherein the apparatus is configured to predict a dynamic light behavior of an area of a non-commissioned lighting system based on at least one of a sensor type, a type of light, a position of the area in a floor map, and user behavior (i.e. this training data contains a set of targeted resulting values (e.g. on/off state of a lamp) for certain situations which in turn are described by recorded sensor values within that situation (e.g. a brightness measurement) … In the targeted scenario the context of a model that was trained to control the brightness of a room can contain information about the sensors (type and placement of the brightness-sensors, type and placement of the presence-sensors, . . . ), the actuators (type and placement of the lights to be controlled, type and placement of the blinds, . . . ), the targeted function of the model (control the light based on presence and brightness), the function of the room (e.g. meeting-room, kitchen, or office),; para. [0005, 0036]).
Claim 12: Schmitt and Yin teach the apparatus of claim 1. Schmitt further teaches a first interface arranged for receiving commissioning data from at least one commissioned lighting system (i.e. retrieving the semantic based description of the context of each pre-trained source evaluation and control model of the plurality of pre-trained source evaluation and control models; para. [0009, 0017-0019]), and a second interface for transmitting the determined configuration data to the non-commissioned lighting system for commissioning the non-commissioned lighting system (i.e. FIG. 3, three different pre-trained source evaluation and control models which were trained before in three different source building automation systems 10, 20, 30 are available for transfer to the target building automation system 40, that is to be uploaded to the central control unit of the target building automation system 40. The context A, B, C of each source building automation system 10, 20, 30 is indicated by means of the placement of sensors 1, lighting devices 2, windows 3 and a controller 4 which controls the sensors 1 and the lighting devices 2 and all other building automation devices, via electrical connection lines 5; para. [0049]).
Claim 13 is similar in scope to Claim 1 and is rejected under a similar rationale.
Claim 14 is similar in scope to Claim 1 and is rejected under a similar rationale.
Schmitt teaches a computer program product (i.e. running the computer program; para. [0004]) comprising code means for producing the steps of claim 13 when run on a processor (i.e. the selected control model is directly transferred to the control software running on the mobile phone which is used to control the target building automation devices of the target building automation system; para. [0027]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Harrison et al. (Pub. No. US 20200060007 A1), A platform for design of a lighting installation generally includes an automated search engine for retrieving and storing a plurality of lighting objects in a lighting object library and a lighting design environment providing a visual representation of a lighting space containing lighting space objects and lighting objects.
Coombes et al. (Pub. No. US 9992838 B1), automatically identify, locate, and assign luminaires into groups such that lighting systems may be more efficiently configured, used, and maintained especially in large buildings, etc.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/TAN H TRAN/Primary Examiner, Art Unit 2141
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