DETAILED ACTION
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
(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.
Claims 1-3 and 12-14 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by
U.S. Patent Application Publication No. 2013/0325196 (Basson).
Claim 1:
The cited prior art describes an air conditioner control apparatus comprising: (Basson: see the network energy resource manager 109 and heating/cooling element 111 as illustrated in figure 1; see the computer system 12’ as illustrated in figure 4)
at least one memory configured to store instructions; and (Basson: see the memory 28’ as illustrated in figure 4)
at least one processor configured to execute the instructions to perform operations comprising: (Basson: see the processing unit 16’ as illustrated in figure 4)
processing an image information including an image in which a target space is captured; (Basson: see the camera 117 as illustrated in figure 1 and as described in paragraphs 0020, 0021, 0025; “For instance, it can use biometrics sensors to ascertain the degree of comfort of user 101, e.g., via camera 117 and associated analysis of user emotions, and/or via one or more sensors (such as that indicated at 103) placed on or about the body of user 101.” Paragraph 0021)
acquiring an appearance history information including a history of appearances of one or more persons being active in the target space; and (Basson: “By way of an illustrative and non-restrictive example of a general algorithm that can be employed in accordance with the process of FIG. 2, in accordance with at least one embodiment of the invention, a sparse regression method may be employed. As such, a matrix H can be formed with columns that contain vectors that represent clothing/dress sensor data from prior history. For each historical sensor data measurement, correspondence can be determined with categories or factors such as user satisfaction/comfort and whether or not the user may have been sick on an occasion. New data measurements are input as a vector Y, and a sparse vector X can then be found that satisfies Y=HX, where the dimension of vector X equals the number of columns in the matrix H. There can then be considered instances where there is a non-zero value of X in any of the columns. Most likely, a category that corresponds to non-zero entries in X yields a category to which just-measured sensor data in Y belongs, and this can assist in defining a user condition (based on Y).” paragraph 0027)
outputting a control information for controlling an air conditioner, based on the appearance history information. (Basson: “The estimation data are then sent to a regulation module 231, where regulation of a room/apartment heating/cooling system is undertaken. For example, if it is found that a user requires more heating, then the room/apartment heating system is activated to provide more heat at or near the user's location.” Paragraph 0025)
Claim 2:
The cited prior art describes the air conditioner control apparatus according to claim 1, wherein outputting the control information includes controlling the air conditioner, based on a result of performing statistical processing on the history of the appearances. (Basson: see the sparse regression method performed on the data to determine the user condition as described in paragraph 0027 and as illustrated in figure 2)
Claim 3:
The cited prior art describes the air conditioner control apparatus according to claim 2, wherein
the statistical processing includes statistical processing related to at least one of
an attribute of the target space or a region included in the target space,
an appearance of a person present in the target space or the region, and (Basson: “As such, a matrix H can be formed with columns that contain vectors that represent clothing/dress sensor data from prior history.” Paragraph 0027; see the sparse regression method performed on the data to determine the user condition as described in paragraph 0027 and as illustrated in figure 2)
an attribute of a person present in the target space or the region. (Basson: “For each historical sensor data measurement, correspondence can be determined with categories or factors such as user satisfaction/comfort and whether or not the user may have been sick on an occasion.” Paragraph 0027; see the sparse regression method performed on the data to determine the user condition as described in paragraph 0027 and as illustrated in figure 2)
Claim 12:
The cited prior art describes an air conditioner control system comprising: (Basson: see the network energy resource manager 109 and heating/cooling element 111 as illustrated in figure 1; see the computer system 12’ as illustrated in figure 4)
the air conditioner control apparatus according to claim 1; (Basson: see the network energy resource manager 109 and heating/cooling element 111 as illustrated in figure 1; see the computer system 12’ as illustrated in figure 4)
a capturing apparatus that captures the target space; and (Basson: see the camera 117 as illustrated in figure 1 and as described in paragraphs 0020, 0021, 0025; “For instance, it can use biometrics sensors to ascertain the degree of comfort of user 101, e.g., via camera 117 and associated analysis of user emotions, and/or via one or more sensors (such as that indicated at 103) placed on or about the body of user 101.” Paragraph 0021)
the air conditioner. (Basson: see the heating/cooling element 111 as illustrated in figure 1)
Claim 13:
Claim 13 is substantially similar to claim 1 and is rejected based on the same reasons and rationale.
13. An air conditioner control method comprising, by a computer:
processing an image information including an image in which a target space is captured;
acquiring an appearance history information including a history of appearances of one or more persons being active in the target space; and
outputting a control information for controlling an air conditioner, based on the appearance history information.
Claim 14:
Claim 14 is substantially similar to claim 1 and is rejected based on the same reasons and rationale.
14. A non-transitory computer readable storage medium storing a program causing a computer to execute:
processing an image information including an image in which a target space is captured;
acquiring an appearance history information including a history of appearances of one or more persons being active in the target space; and
outputting a control information for controlling an air conditioner, based on the appearance history information.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4-9 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2013/0325196 (Basson) in view of
U.S. Patent Application Publication No. 2015/0168003 (Stefanski).
Claim 4:
Basson does not explicitly describe a time change as described below. However, Stefanski teaches the time change as described below.
The cited prior art describes the air conditioner control apparatus according to claim 3, wherein
the statistical processing includes statistical processing related to a time change in at least one of (Stefanski: “Throughout learning mode operation, after each user input is received, the processor 112 of the thermostat 16 analyzes (block 162) the time, temperature, and occupant activity data associated with the current user input relative to the time, temperature, and occupant activity data associated with previous user inputs and attempt to identify trends. If the processor 112 identifies (block 164) a trend or correlation between the time, temperature, and occupant activity data associated with the current user input and the time, temperature, and occupant activity data associated with previous user inputs, the processor 112 may create (block 166) or modify a temperature setpoint of the temperature setpoint schedule based on the correlation.” Paragraph 0048)
an attribute of the target space or a region included in the target space, (Stefanski: see the use of temperature for trend analysis as described in paragraph 0048)
an appearance of a person present in the target space or the region, and
an attribute of a person present in the target space or the region. (Stefanski: see the occupant activity for trend analysis as described in paragraph 0048)
One of ordinary skill in the art would have recognized that applying the known technique of Basson, namely, an air conditioning control system, with the known techniques of Stefanski, namely, an air conditioner control system, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Basson to control an air conditioner based on analysis of occupant data and occupant historical data with the teachings of Stefanski to control an air conditioner using various types of data over time would have been recognized by those of ordinary skill in the art as resulting in an improved air conditioner control system. In other words, the combination of references teaches an air conditioner control system using various types of data based on the teachings of an air conditioner control system using occupant image and other data in Basson and the teachings of using data over time to control an air conditioning system in Stefanski.
Claim 5:
Basson does not explicitly describe a control pattern as described below. However, Stefanski teaches the control pattern as described below.
The cited prior art describes the air conditioner control apparatus according to claim 2, wherein outputting the control information includes controlling the air conditioner, based on a control pattern information including a control content of the air conditioner according to a result of the statistical processing. (Stefanski: see the schedule as illustrated in figure 5 and as described in paragraph 0050) (Basson: “The estimation data are then sent to a regulation module 231, where regulation of a room/apartment heating/cooling system is undertaken. For example, if it is found that a user requires more heating, then the room/apartment heating system is activated to provide more heat at or near the user's location.” Paragraph 0025)
Basson and Stefanski are combinable for the same rationale as set forth above with respect to claim 4.
Claim 6:
Basson does not explicitly describe a control pattern as described below. However, Stefanski teaches the control pattern as described below.
The cited prior art describes theair conditioner control apparatus according to claim 5, the instructions to perform operations further comprising creating the control pattern information, based on a result of performing statistical processing on a motion history of the air conditioner. (Stefanski: “As illustrated in FIG. 6, the thermostat data 188 may include different types of data. For the illustrated embodiment, the thermostat data 188 includes temperature program data 190, which may include temperature setpoint schedules (e.g., temperature setpoint schedule 170 illustrated in FIG. 5). In certain embodiments, the temperature program data 190 may also include other occupant temperature preferences including, for example, learned exceptions to the temperature setpoint schedule or temperature programs based on occupant activity. In addition to the temperature program data 190, in certain embodiments, the thermostat data 188 may also include occupant activity data 192 that is based on inputs received by each of the thermostats 16 from one or more sensors (e.g., sensors 12 and/or 122) and/or other data inputs 110 regarding occupancy and/or occupant activities within the respective structures. For example, as illustrated in FIG. 6, in certain embodiments the occupant activity data 192 of a thermostat 16 may include one or more of: a number of occupants in a structure, occupant locations within or without the structure, occupant activity levels, computer network traffic, or other suitable measures of occupancy and/or occupant activity. It may be appreciated that the occupant activity data 192 may generally correspond to the temperature program data 190. For example, in certain embodiments, each piece of occupant activity data 192 may correlate or correspond to a temperature setpoint of a temperature setpoint schedule. That is, each piece of occupant activity data may be collected at or near the time associated with a corresponding temperature setpoint.” Paragraph 0053)
Basson and Stefanski are combinable for the same rationale as set forth above with respect to claim 4.
Claim 7:
Basson does not explicitly describe a control pattern as described below. However, Stefanski teaches the control pattern as described below.
The cited prior art describes the air conditioner control apparatus according to claim 5, the instructions to perform operations further comprising creating, when setting of the air conditioner is manually changed, the control pattern information for the air conditioner on which the change is made, based on change information about the change. (Stefanski: “In certain embodiments, the temperature program data 190 may also include other occupant temperature preferences including, for example, learned exceptions to the temperature setpoint schedule or temperature programs based on occupant activity.” Paragraph 0053; “However, a particularly warm-natured occupant, for example, may not desire to maintain a warmer temperature in the structure 10 in advance of the storm, despite the output of the signature-based temperature model 196. As such, the occupant may interact with the thermostat 16 to request a cooler temperature than the output of the signature-based temperature model 196 dictates, which may be referred to herein as an "exception" to signature-based temperature program.” Paragraph 0084)
Basson and Stefanski are combinable for the same rationale as set forth above with respect to claim 4.
Claim 8:
Basson does not explicitly describe a schedule as described below. However, Stefanski teaches the schedule as described below.
The cited prior art describes the air conditioner control apparatus according to claim 1, wherein outputting the control information further includes controlling the air conditioner, based on schedule information including at least one of a use schedule of the target space and a schedule of a user of the target space. (Stefanski: see the use of schedule 190 as illustrated in figure 6; “Further, one or more of the "smart" appliances 132 of the structure 10 discussed above may be also coupled to the router 52, which may enable the thermostat 16 to determine information (e.g., modes of operation, operation schedules, access or usage schedules, maintenance schedules, and so forth) for these appliances that may be used to determine or predict occupancy and/or occupant activity within portions of the structure 10.” Paragraph 0043; “For example, in certain embodiments, an occupant may enable the thermostat 16 to access occupant schedule information from one or more data inputs 110. By specific example, an occupant may maintain an agenda or schedule on the computer 50, on the cellular phone 48, or using an online resource 124, and the occupant may further grant the thermostat 116 access to the occupant's schedule on one or more of these devices or resources.” Paragraph 0045)
Basson and Stefanski are combinable for the same rationale as set forth above with respect to claim 4.
Claim 9:
Basson does not explicitly describe a different space as described below. However, Stefanski teaches the different space as described below.
The cited prior art describes the air conditioner control apparatus according to claim 1, wherein outputting the control information includes outputting the control information for adjusting a space different from the target space, based on the appearance history information. (Stefanski: see the use of data throughout different structures in a neighborhood 186 as illustrated in figure 6 and as described in paragraphs 0052, 0053; “It may be appreciated that, in certain embodiments, the process 260 may include an additional grouping step. For example, before correlating the data in block 268, in certain embodiments, the processor (e.g., the one or more processors 182 of the thermostat service 180) may use at least a portion of the classification data received with each set of thermostat data 188 to divide the received sets of thermostat data 188 into groups. For example, in certain embodiments, the processor may use one or more pieces of classification data 194, such as location of the structure 10, model of thermostat 16, type of structure 10, type of HVAC system 20, temperature profile of the structure 10, or occupant information, to group the received sets of thermostat data 188. It may be appreciated that this additional grouping step may enable the processor to construct multiple signature-based temperature models 196 in block 270 (e.g., one for each group of the received sets of thermostat data 188), wherein each temperature signature-based temperature model 196 is generated or constructed based on a particular group of the received sets of thermostat data 188 and, therefore, may be better tuned to the temperature preferences of that particular group. For example, the processor may construct may construct more specialized signature-based temperature model 196 for a particular locations or geographic regions (e.g., San Francisco Bay Area, East Texas, New York City, a particular community), for particular types of structures (e.g., one-, two-, or three-story residential structures), for particular types of occupants (e.g., elderly, college-aged, family with kids, high-income level, mid-level income level), and so forth.” Paragraph 0078)
Basson and Stefanski are combinable for the same rationale as set forth above with respect to claim 4.
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2013/0325196 (Basson) in view of
U.S. Patent Application Publication No. 2011/0205366 (Enohara).
Claim 10:
Basson does not explicitly describe plural spaces as described below. However, Enohara teaches the plural spaces as described below.
The cited prior art describes the air conditioner control apparatus according to claim 1, wherein
the target space is plural, and (Enohara: see rooms as illustrated in figure 1 and as described in paragraphs 0019, 0020)
processing the image information includes processing a plurality of the pieces of image information each including an image in which the plurality of target spaces are captured, and (Enohara: see the camera devices 10, 10-1 . . . 10-n as illustrated in figures 1, 2 and as described in paragraphs 0019, 0020; “The camera devices 10-1 to 10-n are installed for each of interiors as control targets, and image the interiors serving as the control targets. The activity amount calculation device 20 acquires and analyzes video information formed by imaging the interiors by the camera devices 10-1 to 10-n, and thereby calculates activity amounts of persons in such rooms as imaging targets.” Paragraph 0020)
acquiring the appearance history information including a plurality of pieces of the appearance history information being a history of appearances of one or more persons being active in each of the plurality of target spaces. (Enohara: “The activity amount calculation device 20 acquires and analyzes video information formed by imaging the interiors by the camera devices 10-1 to 10-n, and thereby calculates activity amounts of persons in such rooms as imaging targets.” Paragraph 0020) (Basson: “By way of an illustrative and non-restrictive example of a general algorithm that can be employed in accordance with the process of FIG. 2, in accordance with at least one embodiment of the invention, a sparse regression method may be employed. As such, a matrix H can be formed with columns that contain vectors that represent clothing/dress sensor data from prior history. For each historical sensor data measurement, correspondence can be determined with categories or factors such as user satisfaction/comfort and whether or not the user may have been sick on an occasion. New data measurements are input as a vector Y, and a sparse vector X can then be found that satisfies Y=HX, where the dimension of vector X equals the number of columns in the matrix H. There can then be considered instances where there is a non-zero value of X in any of the columns. Most likely, a category that corresponds to non-zero entries in X yields a category to which just-measured sensor data in Y belongs, and this can assist in defining a user condition (based on Y).” paragraph 0027)
One of ordinary skill in the art would have recognized that applying the known technique of Basson, namely, an air conditioning control system, with the known techniques of Enohara, namely, an air conditioner control system, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Basson to control an air conditioner based on analysis of occupant data and occupant historical data with the teachings of Enohara to control an air conditioner in different spaces would have been recognized by those of ordinary skill in the art as resulting in an improved air conditioner control system. In other words, the combination of references teaches an air conditioner control system using various types of data in different spaces based on the teachings of an air conditioner control system using occupant image and other data in Basson and the teachings of controlling an air conditioning system across different rooms in Enohara.
Claim 11:
Basson does not explicitly describe plural spaces as described below. However, Enohara teaches the plural spaces as described below.
The cited prior art describes the air conditioner control apparatus according to claim 10, wherein outputting the control information includes outputting the control information for controlling at least one air conditioner associated with at least one of the plurality of target spaces. (Enohara: “The EMS 30 calculates air conditioning control parameters for each of the rooms based on the activity amounts of the persons present in the rooms, which are calculated by the activity amount calculation device 20. The LCS 40 transmits the air conditioning control parameters, which are calculated by the EMS 30, to the respective direct digital controllers (DDCs) 50-1 to 50-n corresponding thereto. The DDCs 50-1 to 50-n are air conditioner control units which control operations of the air conditioners of the rooms as the control targets based on the air conditioning control parameters transmitted thereto from the LCS 40. The air conditioners 60-1 to 60-n are installed for each of the rooms, and operate by the control of the DDCs 50-1 to 50-n connected thereto.” Paragraph 0020) (Basson: “The estimation data are then sent to a regulation module 231, where regulation of a room/apartment heating/cooling system is undertaken. For example, if it is found that a user requires more heating, then the room/apartment heating system is activated to provide more heat at or near the user's location.” Paragraph 0025)
Basson and Enohara are combinable for the same rationale as set forth above with respect to claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 2023/0066057 describes an air conditioning control system that detects use activity and position.
U.S. Patent Application Publication No. 2016/0363340 describes an air conditioner control system with thermal imaging of the air conditioned space including the occupant.
U.S. Patent Application Publication No. 2019/0309978 describes an air conditioning control system using an image of the occupant to control the air conditioner.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER E EVERETT whose telephone number is (571)272-2851. The examiner can normally be reached Monday-Friday 8:00 am to 5:00 pm (Pacific).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at 571-272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Christopher E. Everett/Primary Examiner, Art Unit 2117
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