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 .
Response to Arguments
Applicant's arguments filed 03/25/2026 have been fully considered but they are not persuasive.
Applicant argues that Dixon does not the use of varying the intensity of the emitted light to obtain different effects in the reflected light. The examiner disagrees, and asserts that the amended claims as written are disclosed by Dixon. Specifically, Dixon discloses varying the light intensity not least in [0064] and further discloses distinguishing between material types (and thus, wall types) based on material reflectivity not least in [0261]. See the rejections below for a detailed analysis of the claims.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 13, 14, 19, and 28 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Dixon (US 2016/0366348 A1).
Regarding Claim 1, Dixon discloses an apparatus comprising:
a light source to emit light ([0236]: “the illuminators 856 are light emitting diodes (LEDs). In some implementations, the illuminators 856 are semiconductor lasers or other semiconductor light sources.”; Figure 12, elements 856-n), wherein an intensity of emitted light by the light source is variable ([0064]: “The illuminators are configured to operate in a first mode to provide illumination using all of the illuminators… The process reconfigures the plurality of illuminators to operate in a second mode, where each of a plurality of subsets of the plurality of illuminators provides illumination separately from other subsets of the plurality of illuminators. The process sequentially activates each of the subsets of the illuminators to illuminate a scene and receives reflected illumination from the illuminated scene…” Illuminating the scene with a subset of the total illuminators necessarily involves varying the intensity of emitted light from the light source.);
a light source controller to control the light source, wherein the light source controller is to change the intensity of the light emitted by the light source ([0106]: “One step in some implementations is to control the illuminators individually or in small groups rather than turning them all on or off together.”; [0213]: “an illumination module 860, which controls the illuminators 856. In some implementations, the illumination module 860 identifies low-light conditions and turns on illuminators as needed. In some implementations, the illumination module controls the illuminators 856 individually.”), wherein the intensity of the light source is changed to obtain different effects from reflected light off a wall ([0243], [0245] discloses using different illumination groups to receive distinct intensities based on illumination; [0064] discloses that the changes in illumination can be achieved by switching between all illuminators and subsets of illuminators) , wherein the effects are dependent on the intensity and a type of wall ([0261]: “Certain materials have reflectivities that are intermediate between a specular surface and a surface with highly diffused reflections. In some implementations, these materials are identified by a range of expected image intensity from reflecting the IR light.” Changes in incident intensity therefore result in differential changes in reflected light based on the material, which can be used to identify the material.), and wherein the light source generates control data to record the intensity of the light emitted by the light source ([0054]: “In some implementations, the respective expected IR light intensity is based on characteristics of the IR illuminators. In some implementations, these characteristics include one or more of: illuminator lux; orientation of the IR illuminators relative to the array of image sensors; and location of the IR illuminators relative to the array of image sensors.”);
a low resolution sensor to measure the reflected light off the wall to generate light data from a reflection of the light off the wall ([0235]: “During illumination, light rays are scattered by and reflect off of object surfaces in the scene (e.g., walls, furniture, humans, etc.). Reflected light rays are then detected by the sensor array 852, which captures an image of the scene (e.g., and IR image or an RGB image).” The examiner notes that in [0017] of the instant application low resolution is described as not being able to distinguish or identify people or faces. Dixon makes no mention of these characteristics.);
a memory storage unit ([0203]: “The memory 806, or alternatively the non-volatile memory within the memory 806, comprises a non-transitory computer readable storage medium.”) to store the light data ([0203]: “In some implementations, the database stores captured images 872, including IR images 864 and/or RGB images 866. In some implementations, the image capture module 862 stores captured IR images 864 and RGB images 866 temporarily (e.g., in volatile memory) before being stored more permanently in the database 870.”) and corresponding control data (e.g., [0203]: “In some implementations, the database 870 stores lookup tables 874, which are used by the depth mapping module to generate depth maps 876.”); and
an image processing engine to locate ([0344]: “The process 2500 constructs (2516) a depth map of a scene using the plurality of IR images.”) and to classify the wall based on the light data and the control data ([0348]: “The process 2500 then analyzes the plane to determine whether the surface is likely to be a floor, a ceiling, or a wall.”), wherein the wall is classified based on the control data from the light source controller ([0319]: “In some implementations, the expected IR light intensity at the respective pixel is (2236) based on other characteristics of the IR illuminators of the camera system as well. For example, in some implementations, the characteristics include (2238) the lux of the IR illuminators 856.”) and the light data in response to changes in intensity of the emitted light ([0243], [0245] discloses using different illumination groups to receive distinct intensities based on illumination; [0064] discloses that the changes in illumination can be achieved by switching between all illuminators and subsets of illuminators; [0261]: “Certain materials have reflectivities that are intermediate between a specular surface and a surface with highly diffused reflections. In some implementations, these materials are identified by a range of expected image intensity from reflecting the IR light.”; Together these disclose the limitation of classifying walls based on the characteristics of reflected light under varying degrees of illumination).
Regarding Claim 13, Dixon discloses a lighting controller comprising:
a light source to emit light ([0236]: “the illuminators 856 are light emitting diodes (LEDs). In some implementations, the illuminators 856 are semiconductor lasers or other semiconductor light sources.”; Figure 12, elements 856-n), wherein an intensity of the emitted light by the light source is variable ([0064]: “The illuminators are configured to operate in a first mode to provide illumination using all of the illuminators… The process reconfigures the plurality of illuminators to operate in a second mode, where each of a plurality of subsets of the plurality of illuminators provides illumination separately from other subsets of the plurality of illuminators. The process sequentially activates each of the subsets of the illuminators to illuminate a scene and receives reflected illumination from the illuminated scene…” Illuminating the scene with a subset of the total illuminators necessarily involves varying the intensity of emitted light from the light source.);
a light source controller to control the light source, wherein the light source controller is to change the intensity of the light emitted by the light source ([0106]: “One step in some implementations is to control the illuminators individually or in small groups rather than turning them all on or off together.”; [0213]: “an illumination module 860, which controls the illuminators 856. In some implementations, the illumination module 860 identifies low-light conditions and turns on illuminators as needed. In some implementations, the illumination module controls the illuminators 856 individually.”), to obtain different effects from reflected light off a wall ([0243], [0245] discloses using different illumination groups to receive distinct intensities based on illumination; [0064] discloses that the changes in illumination can be achieved by switching between all illuminators and subsets of illuminators), wherein the effects are dependent on the intensity and a type of wall ([0261]: “Certain materials have reflectivities that are intermediate between a specular surface and a surface with highly diffused reflections. In some implementations, these materials are identified by a range of expected image intensity from reflecting the IR light.” Changes in incident intensity therefore result in differential changes in reflected light based on the material, which can be used to identify the material.), and wherein the light source generates control data to record the intensity of the light emitted by the light source ([0054]: “In some implementations, the respective expected IR light intensity is based on characteristics of the IR illuminators. In some implementations, these characteristics include one or more of: illuminator lux; orientation of the IR illuminators relative to the array of image sensors; and location of the IR illuminators relative to the array of image sensors.”);
a low resolution sensor to measure the reflected light off the wall to generate light data from a reflection the light off the wall ([0235]: “During illumination, light rays are scattered by and reflect off of object surfaces in the scene (e.g., walls, furniture, humans, etc.). Reflected light rays are then detected by the sensor array 852, which captures an image of the scene (e.g., and IR image or an RGB image).” The examiner notes that in [0017] of the instant application low resolution is described as not being able to distinguish or identify people or faces. Dixon makes no mention of these characteristics.);
a memory storage unit ([0203]: “The memory 806, or alternatively the non-volatile memory within the memory 806, comprises a non-transitory computer readable storage medium.”) to store the light data ([0203]: “In some implementations, the database stores captured images 872, including IR images 864 and/or RGB images 866. In some implementations, the image capture module 862 stores captured IR images 864 and RGB images 866 temporarily (e.g., in volatile memory) before being stored more permanently in the database 870.”) and corresponding control data (e.g., [0203]: “In some implementations, the database 870 stores lookup tables 874, which are used by the depth mapping module to generate depth maps 876.”); and
an image processing engine to locate ([0344]: “The process 2500 constructs (2516) a depth map of a scene using the plurality of IR images.”) and to classify the wall based on the light data and the control data ([0348]: “The process 2500 then analyzes the plane to determine whether the surface is likely to be a floor, a ceiling, or a wall.”), wherein the wall is classified based on the control data from the light source controller ([0319]: “In some implementations, the expected IR light intensity at the respective pixel is (2236) based on other characteristics of the IR illuminators of the camera system as well. For example, in some implementations, the characteristics include (2238) the lux of the IR illuminators 856.”) and the light data in response to changes in intensity of the emitted light ([0243], [0245] discloses using different illumination groups to receive distinct intensities based on illumination; [0064] discloses that the changes in illumination can be achieved by switching between all illuminators and subsets of illuminators; [0261]: “Certain materials have reflectivities that are intermediate between a specular surface and a surface with highly diffused reflections. In some implementations, these materials are identified by a range of expected image intensity from reflecting the IR light.”; Together these disclose the limitation of classifying walls based on the characteristics of reflected light under varying degrees of illumination); and
a communications interface to transmit a control signal ([0221]: “The communication buses 912 may include circuitry (sometimes called a chipset) that interconnects and controls communications between system components.”) to a plurality of lighting devices ([0245]: “For each depth 1302, the illuminators 856 of the camera 118 are simulated to activate in accordance with a pre-defined illumination pattern. An illumination pattern specifies the grouping of illuminators 856 (if any), specifies the order the groups of illuminators are activated, and may specify other parameters related to the operation of the illuminators.”, wherein each lighting device is to be bounded by the wall ([0388]: “For example, some implementations determine whether the camera is inside or outside (e.g., based on the presence of a ceiling). When a camera is inside, some implementations determine whether the room is a small room or a large room.” This admits a setup in which all the cameras are indoors and therefore bounded by a wall. The examiner has interpreted this limitation in light of [0027] of the specification of the instant application, which seems to indicate that the cameras are indoors. Note that the cameras of Dixon include lighting devices.).
Regarding Claim 14, which depends from rejected Claim 13, Dixon further discloses wherein communications interface is to receive an identifier from each lighting device of the plurality of lighting devices ([0394]: “In some instances, the notification is sent as an email. As indicated in FIG. 31C, the email message body indicates that the camera has moved, and identifies the zone.”; Thus devices in the network can be identified by the zone or room they are in.).
Regarding Claim 19, which depends from rejected Claim 13, Dixon further discloses further comprising a graphical user interface to receive input from a user ([0180]: “The user interface 710 also includes one or more input devices 714, including user interface components that facilitate user input such as a keyboard, a mouse, a voice-command input unit or microphone, a touch screen display, a touch-sensitive input pad, a gesture capturing camera, or other input buttons or controls.”), wherein the input is to generate the control signal ([0191]: “a camera control module 732 for generating control commands for modifying an operating mode of the one or more video sources in accordance with user input;”).
Regarding Claim 28, Dixon discloses a method comprising:
emitting light from a light source ([0236]: “the illuminators 856 are light emitting diodes (LEDs). In some implementations, the illuminators 856 are semiconductor lasers or other semiconductor light sources.”; Figure 12, elements 856-n) onto a wall ([0038]: “In accordance with some implementations, a process identifies large planar surfaces in scenes, such as floors, walls, and ceilings.”);
controlling the light source to change an intensity of the light emitted by the light source ([0106]: “One step in some implementations is to control the illuminators individually or in small groups rather than turning them all on or off together.”; [0213]: “an illumination module 860, which controls the illuminators 856. In some implementations, the illumination module 860 identifies low-light conditions and turns on illuminators as needed. In some implementations, the illumination module controls the illuminators 856 individually.”) to obtain different effects from reflected light off a wall ([0243], [0245] discloses using different illumination groups to receive distinct intensities based on illumination; [0064] discloses that the changes in illumination can be achieved by switching between all illuminators and subsets of illuminators), wherein the effects are dependent on the intensity and a type of wall ([0261]: “Certain materials have reflectivities that are intermediate between a specular surface and a surface with highly diffused reflections. In some implementations, these materials are identified by a range of expected image intensity from reflecting the IR light.” Changes in incident intensity therefore result in differential changes in reflected light based on the material, which can be used to identify the material.), and wherein the light source generates control data to record the intensity of the light emitted by the light source ([0054]: “In some implementations, the respective expected IR light intensity is based on characteristics of the IR illuminators. In some implementations, these characteristics include one or more of: illuminator lux; orientation of the IR illuminators relative to the array of image sensors; and location of the IR illuminators relative to the array of image sensors.”);
measuring light data from a reflection of the light off the wall with a low resolution sensor ([0235]: “During illumination, light rays are scattered by and reflect off of object surfaces in the scene (e.g., walls, furniture, humans, etc.). Reflected light rays are then detected by the sensor array 852, which captures an image of the scene (e.g., and IR image or an RGB image).” The examiner notes that in [0017] of the instant application low resolution is described as not being able to distinguish or identify people or faces. Dixon makes no mention of these characteristics.);
storing the light data ([0203]: “In some implementations, the database stores captured images 872, including IR images 864 and/or RGB images 866. In some implementations, the image capture module 862 stores captured IR images 864 and RGB images 866 temporarily (e.g., in volatile memory) before being stored more permanently in the database 870.”) and corresponding control data (e.g., [0203]: “In some implementations, the database 870 stores lookup tables 874, which are used by the depth mapping module to generate depth maps 876.”);
locating the wall based on the light data ([0344]: “The process 2500 constructs (2516) a depth map of a scene using the plurality of IR images.”); and
classifying the wall based on the light data and the control data using an image processing engine ([0348]: “The process 2500 then analyzes the plane to determine whether the surface is likely to be a floor, a ceiling, or a wall.”), wherein the wall is classified based on the control data from the light source controller ([0319]: “In some implementations, the expected IR light intensity at the respective pixel is (2236) based on other characteristics of the IR illuminators of the camera system as well. For example, in some implementations, the characteristics include (2238) the lux of the IR illuminators 856.”) and the light data in response to changes in intensity of the emitted light ([0243], [0245] discloses using different illumination groups to receive distinct intensities based on illumination; [0064] discloses that the changes in illumination can be achieved by switching between all illuminators and subsets of illuminators; [0261]: “Certain materials have reflectivities that are intermediate between a specular surface and a surface with highly diffused reflections. In some implementations, these materials are identified by a range of expected image intensity from reflecting the IR light.”; Together these disclose the limitation of classifying walls based on the characteristics of reflected light under varying degrees of illumination)
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.
Claim(s) 2-5, 29, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Dixon in view of Segev (US 2021/0073449 A1).
Regarding Claim 2, which depends from rejected Claim 1, Dixon does not teach and Segev does teach wherein the image processing engine is to use machine learning to classify the wall based on the light data and the control data ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Segev to use machine learning in the image processing engine to aid in classifying the wall data ([0485]: “In some embodiments generatively analyzing the room may include use of machine learning. Embodiments consistent with the present disclosure may include using artificial intelligence (i.e., machine learning models) for identifying wall contours, rooms, windows, walls, doors, door sills, architectural features, semantic features, many other possible features as well as predicting energy consumption and equipment types and locations.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Segev to use machine learning in wall classification into the apparatus of Dixon. Machine learning models can be trained on large amounts of real world data, allowing for more robust and accurate classifications of target objects.
Regarding Claim 3, which depends from rejected Claim 2, Dixon further discloses wherein the light data measured by the low resolution sensor includes an intensity distribution dependent on the intensity of the light emitted by the light source ([0235]: “During illumination, light rays are scattered by and reflect off of object surfaces in the scene (e.g., walls, furniture, humans, etc.). Reflected light rays are then detected by the sensor array 852, which captures an image of the scene (e.g., and IR image or an RGB image). The captured image digitally measures the intensity of the reflected IR light for each of the pixels in the sensor array 852.”), wherein the intensity distribution is associated with a type of wall ([261]: “ these materials are identified by a range of expected image intensity from reflecting the IR light.).
Regarding Claim 4, which depends from Claim 3, Dixon does not teach and Segev does teach wherein the machine learning is to assign a confidence value to the type of the wall ([0342]: “In some embodiments, a confidence rating may be associating with a given classification.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Segev to associate a confidence rating with the classifications provided by the machine learning algorithms. A worker skilled in the art would know that confidence values are common outputs of machine learning algorithms, and would find their incorporation in to the apparatus to yield predictable results.
Regarding Claim 5, which depends from rejected Claim 4, Dixon further discloses wherein the type of the wall is one of opaque, translucent, transparent, exterior, or doorway. ([0391]: “For example, the dark region 1822 in FIG. 18D appears to be a window, but it is also part of a door. In some implementations, the designations of “door” and “window” are compatible, so both are included. In some implementations, when there are two or more designations (which are potentially incompatible), each of the designations has an associated probability.” Thus the apparatus can detector doors and windows in addition to opaque walls.)
Regarding Claim 29, which depends from rejected Claim 28, Dixon does not teach and Segev does teach wherein classifying the control data applies machine learning to classify the wall into a type ([0485]: “In some embodiments generatively analyzing the room may include use of machine learning. Embodiments consistent with the present disclosure may include using artificial intelligence (i.e., machine learning models) for identifying wall contours, rooms, windows, walls, doors, door sills, architectural features, semantic features, many other possible features as well as predicting energy consumption and equipment types and locations.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Segev to use machine learning in wall classification into the apparatus of Dixon. Machine learning models can be trained on large amounts of real world data, allowing for more robust and accurate classifications of target objects. For example, by training on real world data, such models can effectively handle object recognition under a variety of lighting scenarios.
Regarding Claim 30, which depends from rejected Claim 29, Dixon does not teach and Segev does teach wherein further comprising assigning a confidence value to the type selected ([0342]: “In some embodiments, a confidence rating may be associating with a given classification.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Segev to associate a confidence rating with the classifications provided by the machine learning algorithms. A worker skilled in the art would have known that confidence values are common outputs of machine learning algorithms, and would find their incorporation in to the apparatus to yield predictable results.
Claims 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Dixon in view of Tiwari (WO 2016/154326 A1).
Regarding Claim 6, which depends from rejected Claim 1, Dixon further discloses comprising a communications interface, wherein the light source controller is to communicate with an external device via the communications interface ([0127]: “In some implementations, the smart home environment 100 includes one or more network-connected cameras 118 that are configured to provide video monitoring and security in the smart home environment 100.”; [0128]: “The smart home environment 100 may also include communication with devices outside of the physical home but within a proximate geographical range of the home.”)
Dixon does not teach and Tiwari does teach wherein this communication is done so as to coordinate the light source to reduce interference with the external device ([0228]: “This feature can be used at commissioning time to reduce wireless interference by configuring repeaters to only re-transmit signals from the transmitters that provide highest RF connectivity level and are present in the same area.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Shin into the apparatus of Dixon to make receiver-transmitter connections only to the transmitter that provides the highest connectivity level. Shin notes that “This feature can be used at commissioning time to reduce wireless interference”, which is beneficial to the overall functioning of the apparatus. Packet loss due to a poor connection can result in poor data transmission and retrieval, which could yield low quality performance in the apparatus.
Regarding Claim 7, which depends from rejected Claim 6, Dixon further discloses a grouping engine to associate the apparatus with a plurality of lighting devices autonomously ([0126]: “In some implementations, when plugged in, an appliance may announce itself to the smart home network, such as by indicating what type of appliance it is, and it may automatically integrate with the controls of the smart home. Such communication by the appliance to the smart home may be facilitated by either a wired or wireless communication protocol.” [0127] discloses that cameras (and by extension their illuminators) are associated with the network and other devices as well).
Regarding Claim 8, which depends from rejected Claim 7, Dixon further discloses wherein the plurality of lighting devices is to be controlled by a lighting controller ([0156]: “The I/O interface to one or more video sources 520 facilitates communications with one or more video sources 522 (e.g., groups of one or more cameras 118 and associated controller devices).”).
Regarding Claim 9, which depends from rejected Claim 8, Dixon further discloses further comprising a motion sensor, wherein the motion sensor is to detect a motion ([0200]: “In some implementations, the camera 118 includes one or more optional sensors 854, such as a proximity sensor, a motion detector, an accelerometer, or a gyroscope.”), wherein the motion to be communicated to the external device to confirm a location of the wall ([0323]: “The movement of the first object triggers the building of the depth map.” In subsequent steps, the depth map is used to locate and classify features in the scene, such as walls.).
Regarding Claim 10, which depends from rejected Claim 6, Dixon further discloses a daylight sensor, wherein the daylight sensor is to measure ambient light, wherein the ambient light is to be communicated to the external device to confirm a location of the wall ([0380]: “In general, an additional IR image is taken with none of the illuminators active in order to determine the ambient light.”; [0383]: “In some implementations, the depth mapping module 878 computes an active IR brightness image 2916, which represents only reflections of light from the active IR illuminators, and not the environmental ambient light. In some implementations, this is performed by subtracting the baseline intensity values (when no illuminators are on) from each of the other images.” These data can then be used to generate a depth map which can confirm a wall location.).
Claims 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Dixon in view of Picco (US 2011/0178650 A1).
Regarding Claim 15, which depends from rejected Claim 14, Dixon does not teach and Picco does teach wherein the identifier is used to group the plurality of lighting devices ([0069]: “Each device 21 is "assigned" a unique address from the server 12 via the controlling master interface 14 and input interface 16. If a particular zone or area requires several sensors/devices 21 to adequately cover the square footage or shape thereof, that grouping information is passed on to and "grouped" at the lighting control server 12.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Picco to group the lighting devices by an identifier into the apparatus of Dixon. Picco notes in [0069] that this grouping “allows the sensors/devices 21 in a large or oddly shaped room to act and/or be treated as one common sensor/device 21.” Grouping in the manner is advantageous since people generally interact with their dwellings on a room by room basis, thereby allowing more natural and intuitive control of the lighting levels.
Regarding Claim 16, which depends from rejected Claim 15, Dixon does not teach and Picco does teach comprising a grouping engine to divide the plurality of lighting devices into a subset of lighting devices. ([0069]: “Each device 21 is "assigned" a unique address from the server 12 via the controlling master interface 14 and input interface 16. If a particular zone or area requires several sensors/devices 21 to adequately cover the square footage or shape thereof, that grouping information is passed on to and "grouped" at the lighting control server 12.”; Putting the plurality of lighting devices into groups inherently divides them into subsets. The lighting control server is identified with the ‘grouping engine’ of the instant application.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Picco to group the lighting devices by an identifier into the apparatus of Dixon. Picco notes in [0069] that this grouping “allows the sensors/devices 21 in a large or oddly shaped room to act and/or be treated as one common sensor/device 21.” Grouping in the manner is advantageous since people generally interact with their dwellings on a room by room basis, thereby allowing more natural and intuitive control of the lighting levels.
Regarding Claim 17, which depends from rejected Claim 16, Dixon does not teach and Picco does teach wherein the control signal is to control the subset of lighting devices ([0128]: “The next step involves polling the room/area/group periodically for the actual maximum ambient light levels. For example, this may occur in response to a command generated by a field device (e.g., motion sensor, light switch, etc.) to turn on lights in a room (or group) (block 200).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Picco into the lighting controller of Dixon. Picco notes in [0194] that this type of control “allows for an automatic response to changing environmental conditions (e.g., season changing).” The advantage of this is increased convenience for end users, as well as potential savings as light levels are adjusted during seasonal changes allowing for decreased electrical usage as the days become longer.
Regarding Claim 18, which depends from rejected Claim 17, Dixon does not teach and Picco does teach wherein the grouping engine is to divide the plurality of lighting devices into a subset of lighting devices automatically based on the identifier ([0069]: “If a particular zone or area requires several sensors/devices 21 to adequately cover the square footage or shape thereof, that grouping information is passed on to and "grouped" at the lighting control server 12.” The grouping in Picco occurs at the lighting control server 12, and is therefore happening automatically).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Picco to automate the grouping process into the lighting controller of Dixon. Automating the process results in a faster and more convenient experience for the end user, and also allows for easier updates to the system in the case of grouping changes.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN WADE CLOUSER whose telephone number is (571)272-0378. The examiner can normally be reached M-F 7:30 - 5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ISAM ALSOMIRI can be reached at (571) 272-6970. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/B.W.C./ Examiner, Art Unit 3645
/ISAM A ALSOMIRI/ Supervisory Patent Examiner, Art Unit 3645
Prosecution Insights