DETAILED ACTION
This action is responsive to the preliminary amendment filed 12/17/2024.
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 .
Status of the Claims
Claims 1-15 are rejected under 35 U.S.C. 103.
Claim Rejections - 35 USC § 103
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.
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 1-3, 6-10, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over PLITKINS (US 2015/0168002 A1) in view of OCK (US 2018/0283723 A1).
Regarding Claim 1, PLITKINS teaches an air conditioner comprising: an input interface; a memory; and a processor configured to: (¶ 52, Fig. 3: An AC system includes a thermostat 16 with memory 114, input interface 118, and processor 112.)
identify whether the air conditioner is operating in an artificial intelligence control operation state (¶ 58, Fig. 4 step 152, Fig. 11, ¶ 74-75, Fig. 11 step 194: A “learning mode” or an “exception learning mode” is activated, either manually by a user or automatically based on certain conditions. There is therefore an identification that the AC is operating in an artificial intelligence control operation state.)
in accordance with a setting pattern comprising a plurality of setting values, (¶ 59-61: The AI control operation state includes a learned setpoint schedule, i.e. a setting pattern, that is determined based on identified trends in time, temperature, and occupant activity data every time a user makes a change to the thermostat. The setting pattern includes at least temperature and time values. An example is shown in Fig. 5, which may be an initial pattern learned after the process illustrated in Fig. 4.)
with which a… model is trained; (Fig. 4. 11, 13, ¶ 61: “as the thermostat 16 is trained according to the temperature preferences of the occupant during learning mode, the thermostat 16 may generate the temperature setpoint schedule 170 and may control operation of the HVAC system”. The thermostat is trained and retrained using the setting values using the processes illustrated in Figures 4, 11, and 13.)
based on identifying that the air conditioner is operating in the artificial intelligence control operation state, acquire, via the input interface, a plurality of control histories related to changes in at least one of the plurality of setting values; (¶ 58: “While learning mode is activated, the processor 112 may receive (block 154) user input at a particular time requesting a particular temperature. In response, the processor 112 of the thermostat 16 provides control signals (block 156) the HVAC system 20 to adjust a temperature of structure 10 according to the occupant temperature preference. Further, the processor 112 stores (block 158) both the temperature and the time associated with the user input in the memory 114 for trend analysis.” Also see ¶ 60, 73, 75, 88: While the AC system is operating in the AI control operation state, namely the “learning mode” or “exception learning mode”, a plurality of temperature setpoint changes are acquired, which are control histories related to changes in at least one of the plurality of setting values.)
and store, in the memory, a changed setting value and environment information at a time of changing the setting value, based on a last control history among the plurality of control histories. (¶ 58, 60, 73-75, 88, Fig. 4 step 158, Fig. 11 step 196, Fig. 13 step 246: Changed setting values and environment information, such as the current or predicted activity of the user, at a time of changing the setting value is stored in memory while the thermostat is operating in a learning mode.
Also see ¶ 74: “while activated, the exception learning mode of the thermostat 16 may enable the processor 112 to store in the memory 114 relevant information regarding exceptions to the temperature setpoint schedule 170. Subsequently, in certain embodiments, the processor 112 may permanently modify the temperature setpoint schedule 170 immediately (e.g., throughout the time exception learning mode is activated) or upon deactivation of the exception learning mode.” A setpoint schedule, such as the schedule illustrated in Fig. 5, is stored in memory. While in the “exception learning mode”, a change to a setpoint that conflicts with the previous settings in the schedule are stored as exceptions. These exceptions are the “last control history among the plurality of control histories”. The stored setpoint schedule is therefore changed based on this last, or final, user change to the setpoint. This process reads on the storing of the changed setting value and environment information “based on a last control history” and reads on the discussion of this claim limitation in Paragraphs 132-133 of the instant application. Also see ¶ 76: Other conditions for changing the stored setpoint schedule, such as a number of times the user changed the setting at the particular time, averaging the new setting with the old setting, or prioritizing the newest setting change, also reads on storing the changed setting value “based on a last control history among the plurality of control histories”.)
While PLITKINS teaches that a thermostat is trained based on the changes a user makes to a temperature setpoint at particular times (Figs. 4, 11, 13). PLITKINS does not explicitly teach with which a machine learning model is trained.
However, OCK, which is similarly directed to an air conditioner having an AI operation setting, teaches with which a machine learning model is trained. (Fig. 1B data learning server DS, Figs. 7A-B, ¶ 188-203: A learning model is generated using machine learning techniques. The learning model is used to determine a setpoint temperature using a linear regression comprising a plurality of variables, including current temperature, outdoor temperature, and outdoor humidity, and learned weights.)
Before the effective filing data of the invention, it would have been obvious to a one of ordinary skill in the art to modify the thermostat having a learning mode that continuously updates and stores the latest setpoint changes made by a user to a schedule taught by PLITKINS by training a machine learning model to determine the setpoint temperatures as taught by OCK. Since the references are similarly directed to air conditioning systems that learn the preferences of a user, the combination would have yielded predictable results. This would have amounted to using a particular type of algorithm, which a person of ordinary skill in the art would have reasonably considered, for training the thermostat during the learning mode. As taught by OCK (¶ 26-27), determining recommended temperatures based on machine learning models, including continuously updating the model based on user’s temperature setting history, would enable the most ideal temperature to be provided to the user, improving the convenience for the user.
Claim 8 is directed to a method and Claim 15 is directed to a non-transitory computer-readable medium, but they otherwise recite the same limitations as claim 1. Claim 8 and Claim 15 are therefore rejected for the same reasoning discussed above.
Regarding Claim 2, PLITKINS in view of OCK further teaches further comprising: a communication interface configured to communicate (PLITKINS, ¶ 52, Fig. 3 116; OCK, ¶ 119-121)
with a server; wherein the processor is further configured to: transmit the changed setting value and the environment information at the time of changing the setting value, based on the last control history among the plurality of control histories, to the server (OCK, ¶ 71-72, 119-121, Fig. 1B: Changes made to the air conditioner settings made by the user are transmitted to a data learning server, which is where the model is trained.)
In addition to the reasons to combine discussed in the rejection of claim 1, it would have been further obvious for the changed setting values to be transmitted to a server, as taught by OCK. Implementing server communication would allow multiple data learning models to be learned and updated, as taught by OCK (¶ 121-123), and storing of such information on a server would reduce the storage requirements needed of the local air conditioning system.
Claim 9 recites the same limitations as claim 2 and is rejected for the same reasoning.
Regarding Claim 3, PLITKINS in view of OCK further teaches wherein the setting pattern is a pattern generated by applying a weight to at least one piece of information of a room temperature, a manipulation time, or a time during which an air conditioning operation is performed. (PLITKINS, ¶ 72, claim 12: A setting pattern is generated based on a weight applied to a manipulation time.
OCK, ¶ 172: Weights are assigned based on timing of data related to air conditioning settings. ¶ 191, 198: The recommended setpoints are determined based on weights learned by a machine learning model, including at least a weight for current room temperature.)
The same reasons to combine discussed in the rejection of claim 1 apply to claim 3.
Claim 10 recites the same limitations as claim 3 and is rejected for the same reasoning.
Regarding Claim 6, PLITKINS in view of OCK further teaches wherein environment information comprises at least one of a room temperature, humidity, weather, date, day, time, a number of times of operation, an operation time, a present state of a user, or information on a user located in a room. (PLITKINS, ¶ 60-61, 75-77, Fig. 5: Environment information used to learn user setting patterns includes day, time, number of times of operation, an operation time, user activity levels (i.e. present state/ information of a user).)
Claim 13 recites the same limitations as claim 6 and is rejected for the same reasoning.
Regarding Claim 7, PLITKINS in view of OCK further teaches wherein the processor is configured to: identify the artificial intelligence control operation state as being discontinued, based on receiving, via the input interface, an input to discontinue the artificial intelligence control operation. (PLITKINS, Fig. 11 step 198, ¶ 76: A user input is received to deactivate the exception learning mode, which discontinues the AI control operation state of the thermostat.)
Claim 14 recites the same limitations as claim 7 and is rejected for the same reasoning.
Claims 4-5 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over PLITKINS (US 2015/0168002 A1) in view of OCK (US 2018/0283723 A1) and further in view of GILLETTE (US 2018/0266718 A1).
Regarding Claim 4, PLITKINS in view of OCK teaches all the limitations of claim 1, on which claim 4 depends.
While OCK teaches generating the recommended sub-setting individually for a user and teaches through training of the machine learning model based on environmental information (¶ 72-88), PLITKINS in view of OCK does not teach wherein the setting pattern includes a pattern individually generated for a user present in a room… based on information indicating whether the user is present in the room.
However, GILLETTE, which is directed to thermostat settings based on user presence and priority, teaches wherein the setting pattern includes a pattern individually generated for a user present in a room… based on information indicating whether the user is present in the room. (¶ 239: “mode manager 3830 receives an indication of the presence of a user in a particular zone via geo-fencing (e.g., based on a received reported location of a mobile device being within a particular defined area). Mode manager 3830 can be configured to operate various zones in various zone modes 3802 based on the location of the user.” A zone mode, i.e. setting pattern, is determined based on the presence of a user in the room.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify the environmental information used to train a learning model and generate a setting pattern of an air conditioning device taught by PLITKINS in view of OCK to include presence information of a user in a room controlled by the air conditioner as taught by GILLETTE. Since the references teach adjusting the setpoint of an air conditioner based on user preferences, the combination would have yielded predictable results and would have amounted to transmitting the user presence information as the environment information, such as to the learning server taught by OCK. Furthermore, such an implementation would be beneficial to the user by improving their level of comfort according to their preferences, especially while performing a variety of tasks as suggested by GILLETTE (¶ 231-233).
Claim 11 recites the same limitations as claim 4 and is rejected for the same reasoning.
Regarding Claim 5, PLITKINS in view of OCK and GILLETTE further teaches further comprising: a communication interface configured to communicate with a server; (PLITKINS, ¶ 52, Fig. 3 116; OCK, ¶ 119-121, GILLETTE, ¶ 128)
wherein the processor is configured to: store identification information of a terminal device of the user in the memory, (GILLETTE, Fig. 7 memory including occupancy detector and identifier 706 and 708. GILLETTE, ¶ 122-124, thermostat receives identification information through NFC. Also see ¶ 110, which discusses a master user. Such a designation would correspond to identification information that is stored.)
control the communication interface to, based on the setting value changing, receive identification information and location information of the terminal device, confirm the user based on the stored identification information and the received identification information, (GILLETTE, ¶ 95, 108, 122-125, 239-240, 249-250: In particular ¶ 122-124: a user presence is identified and confirmed based on the identification information and location information of the terminal device. Authentication of the detected user using their terminal device is further taught in ¶ 117, 130, and 133.)
The combination of PLITKINS, OCK, and GILLETTE teaches confirm whether the confirmed user is present in the room based on the received location information, and control the communication interface to transmit information indicating whether the confirmed user is present in the room to the server. (GILLETTE teaches confirmation that a user is present in a room as discussed in ¶ 108, 239-240. This would correspond to environment information, which PLITKINS and OCK teach using for learning of a setting pattern. OCK teaches transmission of the environment information to the learning server in ¶ 71.)
In addition to the motivation to combine discussed in the rejection of claim 4, GILLETE teaches identification and confirmation of a particular user would be advantageous in multiple occupancy homes with users that have varying settings (¶ 126). Combined with the learning models of PLITKINS and OCK, it would have been obvious to use this environmental information in order to determine the recommended setpoint temperatures for the air conditioner.
Claim 12 recites the same limitations as claim 5 and is rejected for the same reasoning.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Fadell (US 2014/0316581 A1) teaches different learning or training modes for a smart thermostat. (¶ 140)
Knatchbull (US 2021/0025613 A1) teaches recording of historical setting changes and environment information corresponding to different users to determine a comfort setting for each user. (¶ 28, 34, Fig. 3)
Venkatesh (US 2021/0199326 A1) teaches determining the occupancy of a user based on location of a mobile terminal and predicting an occupancy and setpoint schedule. (Fig. 3, 11, ¶ 36, 61)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAMI RAFAT OKASHA whose telephone number is (571)272-0675. The examiner can normally be reached M-F 10-6 EST.
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/RAMI R OKASHA/Primary Examiner, Art Unit 2118
