DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1-11 are objected to because of the following informalities:
"Device" should be "A device" [Claim 1, line 1];
"Method" should be "A method" [Claim 5, line 1];
"Device" should be "The device" [Claims 2-4, all line 1];
"Method" should be "The method" [Claims 6-11, all line 1];
"wherein step" or “wherein, after step” should be "wherein said step" or “wherein, after step” [Claims 7-11, lines 1 and/or 2].
Appropriate correction is required. Further, in an effort to practice compact prosecution, each of these limitations has been interpreted similarly as in the provided recommendation for each limitation, above.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Parker (US 2016/0341436).
Regarding claim 1, Parker discloses:
Device for generating light signals and/or audio signals inside environments comprising a plurality of sensors arranged for detecting one or more parameters of the environment and/or one or more parameters of one or more users present in the environment in which the device is located, one or more lighting elements and/or one or more elements for emitting an audio signal ([0027] measuring physiological data using sensors [0038]-[0039] light-sensing devices [0051] environmental sensors (e.g., motion, temperature...etc.), there being a control unit arranged for controlling the activation of the lighting elements and/or of the elements for emitting an audio signal, which control unit comprises at least one processing unit ([0042] An environmental device is a device that at least in part controls a controllable environmental condition. Examples of environmental devices, include, for example, lighting devices, ..., sound systems (e.g., speakers and noise cancelling apparatus). Of particular interest herein are lighting devices, which can be used for a variety of lighting applications, including, for example, ambient lighting, spot light and backlighting of display screens. In one embodiment, the lighting devices are configured to control the type and/or intensity of the light emitted [0051] information detected by the biometric device 106 1 and the environmental sensor 108 1 can be received by the control server 110 and used to control the environmental device 112 1) and at least one storage unit, characterized in that association rules are provided, which association rules are designed to associate the values of the detected parameters with a corresponding condition of the environment, a plurality of conditions of the environment being provided ([0047] receiving environmental data from an environment (e.g., location, temperature, barometric pressure, lighting conditions, spectral content of ambient light, etc.) and physiological data from a subject (e.g., pulse rate, blood-oxygen level, blood-sugar level, melatonin level, etc.), processing the data, and using a learning model to determine an optimal set of environmental parameters and settings to broadcast to a set of environmental devices that can affect the environmental conditions [0055] A simulation engine 214 can use the current data 231 and other data (e.g., target outcomes 233) to develop and adjust a learning model 232 stored in a store of model data 216. The output of the simulation engine 214 is received by a parameter synthesizer 218 to convert to parameters and settings specific to the instances of the environmental devices that have been identified for adjustment), the processing unit being configured to: - identify a specific condition of the environment on the basis of the detected parameters, - activate the lighting elements and/or the elements for emitting an audio signal on the basis of the specific condition of the environment ([0048] from inferences on the health state of a subject, an algorithm may prescribe a desired change to environmental conditions. For instance, if the circadian cycle of the subject is out-of-phase, a light therapy can be provided (increase of blue light and of light intensity to entrain the circadian cycle, or paucity of blue light and reduced intensity to reduce circadian stimulation). In another example, the temperature and humidity of the ambient air are tuned to affect the subject's body temperature. A closed-feedback loop may be used between health conditions and environmental conditions [0049] Various embodiments include controllable light sources so that the subject is exposed to a given amount of light with a given spectrum at a given time in order to influence his circadian cycle. For instance, if it has been determined that the subject has not received enough circadian-stimulating blue light in the first few hours of the day, he may be exposed to a large amount of blue light from a computer screen mid-morning to compensate for this. Conversely, if it has been determined that the user has received excessive amounts of circadian-stimulating blue light throughout the day, lights can be automatically dimmed or tuned to a spectrum containing less blue light (including a low-CCT spectrum, or a standard CCT spectrum with very little blue light and violet light instead to achieve a desired chromaticity) at night [0055]-[0057], [0058], [0059] When in the first position, learning model logic 421 is processed (e.g., by the simulation engine 214). The learning model logic 421 shows that if the biometric device 106 2 is within 10 feet of work area A 411 (e.g., as detected by environmental sensor 108 2) and the subject's melatonin level is greater than 500 (e.g., as detected by the biometric device 106 2), then the blue spectrum of environmental device 112 2 should be increased by 3.45 μW/cm2).
As per claim 2, claim 1 is incorporated, Parker further discloses:
wherein machine learning and artificial intelligence algorithms are loaded inside said control unit, the processing unit being configured to modify said association rules based on the operation of said algorithms ([0052] a learning model for determining desired environmental conditions based, in part, on a subject's environmental and physiological state can be developed (e.g., trained, simulated, optimized, etc.) at the control server 110 (see operation 122 of FIG. 1B)...The control server 110 can process (e.g., filter, adjust, translate, etc.) the received data (see operation 128) and validate and/or make adjustments to the learning model as needed (see operation 130 of FIG. 1B). Using the updated learning model and the current environmental and physiological data, an optimal set of environmental parameters and settings can be determined (see operation 132) and broadcast to the environmental device 112 1 and other devices (see message 134)).
As per claim 3, claim 1 is incorporated, Parker further discloses:
wherein said sensors comprise light sensors and/or acoustic sensors and/or motion sensors and/or temperature sensors and/or humidity sensors and/or vibration sensors and/or Weather sensors ([0027] measuring physiological data using sensors [0038]-[0039] light-sensing devices [0051] environmental sensors (e.g., motion, temperature...etc.).
As per claim 4, claim 1 is incorporated, Parker further discloses:
wherein said sensors comprise a communication unit configured to communicate with one or more user devices associated with the users present in the environment ([0051], Fig. 1A).
Regarding claim 5, Parker discloses:
Method for generating light signals and/or audio signals within an environment comprising the following steps: a) detection of one or more parameters of the environment and/or of one or more parameters of one or more users present in the environment ([0027] measuring physiological data using sensors [0038]-[0039] light-sensing devices [0051] environmental sensors (e.g., motion, temperature...etc.), b) processing of such parameters, c) generation of light signals and/or audio signals, characterized in that step b) involves the association of the values of the parameters detected in step a) to a specific condition of the environment, step c) being implemented on the basis of the specific condition of the environment associated (([0042] An environmental device is a device that at least in part controls a controllable environmental condition. Examples of environmental devices, include, for example, lighting devices, ..., sound systems (e.g., speakers and noise cancelling apparatus). Of particular interest herein are lighting devices, which can be used for a variety of lighting applications, including, for example, ambient lighting, spot light and backlighting of display screens. In one embodiment, the lighting devices are configured to control the type and/or intensity of the light emitted [0047] receiving environmental data from an environment (e.g., location, temperature, barometric pressure, lighting conditions, spectral content of ambient light, etc.) and physiological data from a subject (e.g., pulse rate, blood-oxygen level, blood-sugar level, melatonin level, etc.), processing the data, and using a learning model to determine an optimal set of environmental parameters and settings to broadcast to a set of environmental devices that can affect the environmental conditions [0048] from inferences on the health state of a subject, an algorithm may prescribe a desired change to environmental conditions. For instance, if the circadian cycle of the subject is out-of-phase, a light therapy can be provided (increase of blue light and of light intensity to entrain the circadian cycle, or paucity of blue light and reduced intensity to reduce circadian stimulation). In another example, the temperature and humidity of the ambient air are tuned to affect the subject's body temperature. A closed-feedback loop may be used between health conditions and environmental conditions [0049] Various embodiments include controllable light sources so that the subject is exposed to a given amount of light with a given spectrum at a given time in order to influence his circadian cycle. For instance, if it has been determined that the subject has not received enough circadian-stimulating blue light in the first few hours of the day, he may be exposed to a large amount of blue light from a computer screen mid-morning to compensate for this. Conversely, if it has been determined that the user has received excessive amounts of circadian-stimulating blue light throughout the day, lights can be automatically dimmed or tuned to a spectrum containing less blue light (including a low-CCT spectrum, or a standard CCT spectrum with very little blue light and violet light instead to achieve a desired chromaticity) at night [0051] information detected by the biometric device 106 1 and the environmental sensor 108 1 can be received by the control server 110 and used to control the environmental device 112 1 [0055] A simulation engine 214 can use the current data 231 and other data (e.g., target outcomes 233) to develop and adjust a learning model 232 stored in a store of model data 216. The output of the simulation engine 214 is received by a parameter synthesizer 218 to convert to parameters and settings specific to the instances of the environmental devices that have been identified for adjustment, [0056]-[0058], [0059] When in the first position, learning model logic 421 is processed (e.g., by the simulation engine 214). The learning model logic 421 shows that if the biometric device 106 2 is within 10 feet of work area A 411 (e.g., as detected by environmental sensor 108 2) and the subject's melatonin level is greater than 500 (e.g., as detected by the biometric device 106 2), then the blue spectrum of environmental device 112 2 should be increased by 3.45 μW/cm2).
As per claim 6, claim 5 is incorporated, Parker further discloses:
wherein step b) involves the generation of associative rules configured for associating the values of the detected parameters with a corresponding condition of the environment ([0059] When in the first position, learning model logic 421 is processed (e.g., by the simulation engine 214). The learning model logic 421 shows that if the biometric device 106 2 is within 10 feet of work area A 411 (e.g., as detected by environmental sensor 108 2) and the subject's melatonin level is greater than 500 (e.g., as detected by the biometric device 106 2), then the blue spectrum of environmental device 112 2 should be increased by 3.45 μW/cm2).
As per claim 7, claim 5 is incorporated, Parker further discloses:
wherein step b) and/or step c) comprises the execution of machine learning and/or artificial intelligence algorithms ([0052] a learning model for determining desired environmental conditions based, in part, on a subject's environmental and physiological state can be developed (e.g., trained, simulated, optimized, etc.) at the control server 110 (see operation 122 of FIG. 1B)...The control server 110 can process (e.g., filter, adjust, translate, etc.) the received data (see operation 128) and validate and/or make adjustments to the learning model as needed (see operation 130 of FIG. 1B). Using the updated learning model and the current environmental and physiological data, an optimal set of environmental parameters and settings can be determined (see operation 132) and broadcast to the environmental device 112 1 and other devices (see message 134)).
As per claim 8, claim 5 is incorporated, Parker further discloses:
wherein, after step c), a step for adjusting the association between the values of the parameters and a specific environmental condition is provided, which adjustment step involves repeating step a) ([0052] a learning model for determining desired environmental conditions based, in part, on a subject's environmental and physiological state can be developed (e.g., trained, simulated, optimized, etc.) at the control server 110 (see operation 122 of FIG. 1B)...The control server 110 can process (e.g., filter, adjust, translate, etc.) the received data (see operation 128) and validate and/or make adjustments to the learning model as needed (see operation 130 of FIG. 1B). Using the updated learning model and the current environmental and physiological data, an optimal set of environmental parameters and settings can be determined (see operation 132) and broadcast to the environmental device 112 1 and other devices (see message 134)).
As per claim 9, claim 5 is incorporated, Parker further discloses:
wherein step a) comprises the detection of light parameters and/or acoustic parameters and/or movement parameters and/or temperature parameters and/or humidity parameters /or vibration parameters, and/or weather parameters ([0027] measuring physiological data using sensors [0038]-[0039] light-sensing devices [0051] environmental sensors (e.g., motion, temperature...etc.).
As per claim 10, claim 5 is incorporated, Parker further discloses:
wherein step a) comprises the detection of biometric parameters associated with the users present in the environment ([0027] measuring physiological data using sensors, [0029], [0037] biometric measurements).
As per claim 11, claim 5 is incorporated, Parker further discloses:
wherein step c) is implemented in such a way as to adapt the light signals and/or the audio signals in real time so as to balance the parameters detected in step a) ([0047] The techniques can continually receive new data, and synthesize and broadcast new environmental settings in real time to enable systems for controlling environmental conditions affecting circadian biorhythms using real-time biometrics, [0048], [0052], [0056], [0057], [0058], [0059] When in the first position, learning model logic 421 is processed (e.g., by the simulation engine 214). The learning model logic 421 shows that if the biometric device 106 2 is within 10 feet of work area A 411 (e.g., as detected by environmental sensor 108 2) and the subject's melatonin level is greater than 500 (e.g., as detected by the biometric device 106 2), then the blue spectrum of environmental device 112 2 should be increased by 3.45 μW/cm2).
Pertinent Prior Art
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gilg (US 2024/0001228) discloses ai-prompt-triggering of end devices for immersive effects;
Gilg (US 2026/0059631) discloses generating or augmenting lighting effects based on contextual data;
Chen (US 2023/0124271) discloses dynamic lighting for an audio device;
Child (US 2017/0245344) discloses identity-based environment adjusting techniques.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM P BARTLETT whose telephone number is (469)295-9085. The examiner can normally be reached on M-Th 11:30-8:30, F 11-3.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached on 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIAM P BARTLETT/
Primary Examiner, Art Unit 2169
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