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 .
Status of Claims
Claims 1-14 of U.S. Application No. 18/753,078 filed on 06/25/2024 were examined. Examiner filed a non-final office action on 10/17/2025.
Applicant filed remarks and amendments on 01/12/2026. Claims 1 and 6-8 were amended. Claims 1-14 are pending and presented for examination.
Response to Arguments
Regarding the claim rejections under 35 USC 102/103: Applicant's arguments filed 01/12/2026 with respect to Woods et al. (US20200031195A1) in view of Schweiger et al. (DE102020106073A1) and in further view of Ostrowski et al. (US20200094651A1) have been fully considered but they are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1-3 and 5-14 are rejected under 35 U.S.C. 103 as being unpatentable over Stevanovic et al. (US9963012B2) in view of Woods et al. (US20200031195A1), hereinafter referred to as Stevanovic and Woods respectively.
Regarding claims 1 and 14, Stevanovic discloses A method for creating an exportable individualized data set for controlling an air conditioner in a vehicle for a user using a control structure (“in response to identifying the specific user in a vehicle, autonomously launching an adaptation application in the vehicle; upon launching the adaptation application, automatically downloading a historical comfort setting profile for the specific user, the historical comfort setting profile including individual comfort setting records; autonomously updating the historical comfort setting profile with a comfort setting record corresponding to each instance of an automatic climate control setting, a manual climate control setting, or an alternate climate control setting while the specific user is in the vehicle; dynamically predicting an in-vehicle setting within respective predetermined time increments while the specific user is in the vehicle or in response to a user request, the predicted in-vehicle setting and the respective predetermined time increments based upon geographic location data points and a set of climate control related settings retrieved from the individual comfort setting records corresponding to the specific user … and causing a most recently predicted in-vehicle setting corresponding to the specific user to be displayed on a vehicle display while the specific user is in the vehicle.”[Col.16 ln 10-40] , wherein, in a repeated modeling loop:
revising the comfort model in the Al unit based on at least a second modeling data packet for the user (“at a second time, recognizing that the specific user is in a second vehicle containing the adaptation application, the second vehicle being different from the first vehicle; monitoring climate control settings every second while the specific user is in the second vehicle; upon recognizing, by the monitoring, another instance of an automatic climate control setting, a manual climate control setting, or an alternate climate control setting: collecting a second vehicle location data point, a time/date data point, and at least one climate control related setting associated with the second vehicle; and generating a second comfort setting record including the data points and the at least one climate control related setting; consolidating the first and second comfort setting records into a historical comfort setting profile for the specific user.” [Col.17 ln 42-60],
repeatedly revising the comfort model in the Al unit based on a plurality of additional modeling data packets for the user (“Upon receiving a comfort setting record, the server 14 matches the comfort setting record with a profile of the specific user, and updates the historical comfort setting profile of the specific user with the comfort setting record.” [Col.13 ln 45-50] … “the adaptation application 46 retrieves data from the historical comfort setting profile (which may have been recently updated if changes or adjustments have been made, or new data is reported), and runs the prediction model to update the predicted in-vehicle setting(s) for the next predetermined time increment. This process is repeated for each predetermined time increment that the specific user is in the vehicle 12, 12′.” [Col.11 ln 57-64])
wherein, in a repeated setting loop:
a setting data packet for the vehicle is acquired and sent to the trained comfort model (“dynamically predicting an in-vehicle setting within respective predetermined time increments while the specific user is in the vehicle … the predicted in-vehicle setting and the respective predetermined time increments based upon geographic location data points and a set of climate control related settings retrieved from the individual comfort setting records” [Col.16 ln 25-36] … “at the end of one predetermined time increment, the adaptation application 46 retrieves data from the historical comfort setting profile … and runs the prediction model to update the predicted in-vehicle setting(s) for the next predetermined time increment. This process is repeated for each predetermined time increment.” [Col.11 ln 55-63])
individualized settings for the air conditioner are predicted by the comfort model based on the data in the setting data packet (“dynamically predicting an in-vehicle setting within respective predetermined time increments … the predicted in-vehicle setting … based upon geographic location data points and a set of climate control related settings retrieved from the individual comfort setting records … An example of this is shown in FIG. 2, where the predicted in-vehicle air temperature is 65° C. and the predicted air conditioner status is on … these climate control settings have the highest probability of being implemented at the given time and at the vehicle’s then-current location, where the probability is based upon historical comfort setting records of the specific user.” [Col.16 ln 25-36]),
and the air conditioner output is controlled on the basis of the individualized settings (“The selected setting(s) is/are then transmitted to the appropriate module 72, 76, which commands the appropriate in-vehicle system (e.g., … HVAC system 70 …) to implement the setting. In response, the appropriate system autonomously changes a then-current setting to the selected in-vehicle setting or checks to see if the then-current setting matches the selected in-vehicle setting.” [Col.13 ln 27-34]).
Stevanovic does not explicitly teach at least one modeling data packet for the user is acquired,
a comfort model is trained in an Al unit with data from at least one modeling data packet for the user
However, Woods does teach at least one modeling data packet for the user is acquired (“the method may further comprise: collecting historical data describing environmental measurements and one or more states of the HVAC system associated with the vehicle or the user of the vehicle; and generating the personal inference model for the vehicle or the user of the vehicle based on the historical data.” [0006] see also [0042-0043]),
a comfort model is trained in an Al unit with data from at least one modeling data packet for the user (“the one or more inference models may be trained by using the data describing environmental measurements and one or more states of the HVAC system collected at a current time, lagged target values inferred at one or more previous times, and a current target value at the current time as training data.” [0007] see also [0042-0043]). Both Stevanovic and Woods teach methods for operating an air conditioning unit of a vehicle. However, Woods explicitly teaches at least one modeling data packet for the user is acquired, a comfort model is trained in an Al unit with data from at least one modeling data packet for the user.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the air conditioning control method of Stevanovic to also include at least one modeling data packet for the user is acquired, a comfort model is trained in an Al unit with data from at least one modeling data packet for the user, as taught by Woods, with a reasonable expectation of success. Doing so improves operating an air conditioning unit of a vehicle (With regard to this reasoning, see at least [Woods, [0006-0007] see also [0042-0043]).
Regarding claim 2, Stevanovic discloses The method according to claim 1, wherein the creation of the modeling data packet is initiated when user input is entered manually by the user to change the current individualized settings of the air conditioner, and/or the creation of the modeling data packet is initiated after a predefined time interval, if the user does not manually enter any user input to change the current individualized settings of the air conditioner within a predefined time interval (“when a user manually adjusts or changes a climate control setting” (for the manual-input trigger) and “The adaptation application 46 may also be programmed to collect the current state of the HVAC system 70 and vehicle 12 at predetermined/regular intervals (e.g., every 5 minutes when the user is in the vehicle 12, 12′). As such, the adaptation application 46 may collect data even when a particular climate control setting instance is not recognized.” [Col.7 ln 50-65]
Regarding claim 3, Stevanovic discloses The method according to claim 1, wherein the setting loop is executed at a predefined frequency, preferably between 0.5 and 10 hertz, particularly preferably 1 hertz (“The adaptation application 46 monitors the climate control activity every half second while the specific user is in the vehicle 12 or 12′.” [Col.7 ln 49-51]).
Regarding claim 5, Stevanovic discloses The method according to claim 1.
Stevanovic does not explicitly teach the comfort model is trained periodically at a predefined time, wherein all modeling data packets for the user are stored between successive times, and used to train the comfort model the data from the modeling data packets are weighted when training the comfort model
However, Woods does teach the comfort model is trained periodically at a predefined time, wherein all modeling data packets for the user are stored between successive times, and used to train the comfort model the data from the modeling data packets are weighted when training the comfort model (“The server 102 may be configured to generate and train a personal inference model for a vehicle system 104 or a user 110 of the vehicle system 104 based on the received information from the vehicle system 104 and/or the collected external data 164. In some embodiments, the computing device 122 may periodically send updated information from the sensors 124, the HVAC controller 126 and/or the user device to the server 102. Responsively, the server 102 may further update the personal inference model for the vehicle system 104 or the user 110 of the vehicle system 104 based on the periodically received updated information from the vehicle system 104 and/or the collected external data 164. The server 102 may send the trained and/or updated personal inference model back to the vehicle system 104 and the vehicle system 104 may use the personal inference model to predict a target value for the vehicle system 104 or for the user 110 of the vehicle system 104.” [0043]). Both Stevanovic and Woods teach methods for operating an air conditioning unit of a vehicle. However, Woods explicitly teaches the comfort model is trained periodically at a predefined time, wherein all modeling data packets for the user are stored between successive times, and used to train the comfort model the data from the modeling data packets are weighted when training the comfort model.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the air conditioning control method of Stevanovic to also include the comfort model is trained periodically at a predefined time, wherein all modeling data packets for the user are stored between successive times, and used to train the comfort model the data from the modeling data packets are weighted when training the comfort model, as taught by Woods, with a reasonable expectation of success. Doing so improves operating an air conditioning unit of a vehicle (With regard to this reasoning, see at least [Woods, [0043]).
Regarding claim 6, Stevanovic discloses The method according to claim 1.
Stevanovic does not explicitly teach the modeling loop is executed in the vehicle the comfort model is trained in the AI unit in the vehicle.
However, Woods does teach the modeling loop is executed in the vehicle and the comfort model is trained in the AI unit in the vehicle (See Fig. 3A “Based on the received information, the computing device 122 may generate and train a personal inference model for the vehicle system 104, or for a user 110 (e.g., a driver, a passenger, or any other occupants) of the vehicle 104 if the vehicle system 104 is equipped with components (not shown in FIG. 1A) to detect and identify a user 110 through technologies such as face recognition, bio-information detection, etc.” [0042] see also [0066]). Both Stevanovic and Woods teach methods for operating an air conditioning unit of a vehicle. However, Woods explicitly teaches wherein characterized in that the modeling loop is executed in the vehicle the comfort model is trained in the AI unit in the vehicle.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the air conditioning control method of Stevanovic to also include wherein characterized in that the modeling loop is executed in the vehicle the comfort model is trained in the AI unit in the vehicle, as taught by Woods, with a reasonable expectation of success. Doing so improves operating an air conditioning unit of a vehicle (With regard to this reasoning, see at least [Woods, [0042] see also [0066]]).
Regarding claim 7,
Stevanovic does not explicitly teach wherein at, in the modeling loop: the comfort model is stored in the vehicle after it has been trained and in the setting loop: the comfort model is executed in the vehicle and the individualized settings are predicted locally in the vehicle.
However, Woods does teach wherein at, in the modeling loop: the comfort model is stored in the vehicle after it has been trained (“In some embodiments, the data storage 130 or database 108 may store historical in-vehicle sensor data 152, HVAC state data 154, vehicle state data 156, user identity data 158, user status data 160, user demographic data 162, and/or external data 164 retrieved by the computing device 122 over a period of time or a course of lifetime of the vehicle 104.” [0039]);
and in the setting loop: the comfort model is executed in the vehicle and the individualized settings are predicted locally in the vehicle (See Fig. 3A “Based on the received information, the computing device 122 may generate and train a personal inference model for the vehicle system 104, or for a user 110 (e.g., a driver, a passenger, or any other occupants) of the vehicle 104 if the vehicle system 104 is equipped with components (not shown in FIG. 1A) to detect and identify a user 110 through technologies such as face recognition, bio-information detection, etc.” [0042] see also [0039, 0066]). Both Stevanovic and Woods teach methods for operating an air conditioning unit of a vehicle. However, Woods explicitly teaches wherein at, in the modeling loop: the comfort model is stored in the vehicle after it has been trained and in the setting loop: the comfort model is executed in the vehicle and the individualized settings are predicted locally in the vehicle.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the air conditioning control method of Stevanovic to also include wherein at, in the modeling loop: the comfort model is stored in the vehicle after it has been trained and in the setting loop: the comfort model is executed in the vehicle and the individualized settings are predicted locally in the vehicle, as taught by Woods, with a reasonable expectation of success. Doing so improves operating an air conditioning unit of a vehicle (With regard to this reasoning, see at least [Woods, [0042] see also [0039, 0066]]).
Regarding claim 8,
Stevanovic does not explicitly teach wherein the modeling loop is executed in the cloud, the modeling data packet acquired for the user is sent to the cloud, and the modeling data packet acquired for the user is sent to the cloud.
However, Woods does teach wherein the modeling loop is executed in the cloud (See Fig. 3A, Fig. 3B “These historical in-vehicle sensor data 152, HVAC state data 154, vehicle state data 156, user identity data 158, user status data 160, user demographic data 162, and/or external data 164 may be used by the computing device 122 or the server 102 as training data to generate one or more inference models to predict personalized target values for controlling the HVAC system” [0039]);
the modeling data packet acquired for the user is sent to the cloud (See Fig. 3A, Fig. 3B “The work flow 320 is the same as the work flow 300 as described with reference to FIG. 3A, except that in the work flow 320, another data storage 322 and a model storage 324 may be included in the server 102 in a cloud computing context.” [0068]);
the comfort model is trained in the AI unit in the cloud. (See Fig. 3A, Fig. 3B “Additionally, in the illustrated embodiments of work flow 320, the model training component 308 is included in the server 102.” [0068]). Both Stevanovic and Woods teach methods for operating an air conditioning unit of a vehicle. However, Woods explicitly teaches wherein the modeling loop is executed in the cloud, the modeling data packet acquired for the user is sent to the cloud, and the modeling data packet acquired for the user is sent to the cloud.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the air conditioning control method of Stevanovic to also include wherein the modeling loop is executed in the cloud, the modeling data packet acquired for the user is sent to the cloud, and the modeling data packet acquired for the user is sent to the cloud, as taught by Woods, with a reasonable expectation of success. Doing so improves operating an air conditioning unit of a vehicle (With regard to this reasoning, see at least [Woods, [0068]]).
Regarding claim 9, Stevanovic discloses if it is not possible to send the modeling data packet for the user to the cloud, the modeling data packet is stored in the vehicle (“The comfort setting record may be transmitted immediately after it is generated, or all comfort setting record(s) during a single driving event may be transmitted together at the end of the event (as signaled by the vehicle engine being powered down, turned off, etc.).”[Col.10 ln 3-7] and “The adaptation application 46 transmits the comfort setting record(s) to the server 14 for storage in the specific user’s historical comfort setting profile … During a vehicle data upload event, the communications module 34 transmits the comfort setting record(s) as packet data to the server 14.” [Col.9-10 ln 63-67 & ln 1-3] and “the data transmission system 38 may include a packet builder, which is programmed to make decisions about what packet to send (e.g., bandwidth, data to include, etc.) and to actually build a packet data message. In another example, the data transmission system 38 may include a wireless modem, which applies some type of encoding or modulation to convert the digital data so that it can communicate through a vocoder or speech codec incorporated in the cellular chipset/component 36. It is to be understood that any suitable encoding or modulation technique that provides an acceptable data rate and bit error may be used with the examples disclosed herein.” [Col.5 ln 1-15]),
Stevanovic does not explicitly teach as soon as it is possible to send the modeling data packet for the user to the cloud, the modeling data packet is sent to the cloud and deleted in the vehicle
However, Woods does teach as soon as it is possible to send the modeling data packet for the user to the cloud, the modeling data packet is sent to the cloud and deleted in the vehicle (“Similarly, the model storage 324 in the server 102 may store historical inference models, while the model storage 310 in the vehicle 104 may store more up-to-date inference models. Additionally, in the illustrated embodiments of work flow 320, the model training component 308 is included in the server 102. The one or more inference models including a personal inference model may be trained by the model training component 308 in the server 102, and transmitted to the vehicle system 104.” [0068] and “a server might transmit a requested code for an application program through the Internet, the ISP, the local network and the communication interface 718. The received code may be executed by processor 704 as it is received, and/or stored in storage device 710,” [0089]). Both Stevanovic and Woods teach methods for operating an air conditioning unit of a vehicle. However, Woods explicitly teaches as soon as it is possible to send the modeling data packet for the user to the cloud, the modeling data packet is sent to the cloud and deleted in the vehicle.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the air conditioning control method of Stevanovic to also include as soon as it is possible to send the modeling data packet for the user to the cloud, the modeling data packet is sent to the cloud and deleted in the vehicle, as taught by Woods, with a reasonable expectation of success. Doing so improves operating an air conditioning unit of a vehicle (With regard to this reasoning, see at least [Woods, [0068]]).
Regarding claim 10,
Stevanovic does not explicitly teach wherein all modeling data packets for the user are stored in the vehicle, and periodically sent at predefined times from the vehicle to the cloud , and/or the comfort model is stored in a model pool in the cloud , and/or the comfort model is stored in a model pool in the cloud and sent from the model pool to the vehicle , and stored in the vehicle , and/or the comfort model is stored in a model pool in the cloud , and the current comfort model is sent periodically, at predefined times, from the model pool to the vehicle , and/or the comfort model is stored in a model pool in the cloud and sent from the model pool in the cloud to the vehicle after it has been fully trained.
However, Woods does teach wherein all modeling data packets for the user are stored in the vehicle, and periodically sent at predefined times from the vehicle to the cloud , and/or the comfort model is stored in a model pool in the cloud , and/or the comfort model is stored in a model pool in the cloud and sent from the model pool to the vehicle , and stored in the vehicle , and/or the comfort model is stored in a model pool in the cloud , and the current comfort model is sent periodically, at predefined times, from the model pool to the vehicle , and/or the comfort model is stored in a model pool in the cloud and sent from the model pool in the cloud to the vehicle after it has been fully trained ( See Fig. 3B “Similarly, the model storage 324 in the server 102 may store historical inference models, while the model storage 310 in the vehicle 104 may store more up-to-date inference models. Additionally, in the illustrated embodiments of work flow 320, the model training component 308 is included in the server 102. The one or more inference models including a personal inference model may be trained by the model training component 308 in the server 102, and transmitted to the vehicle system 104.” [0068] see also [0037-0039, 0043]). Both Stevanovic and Woods teach methods for operating an air conditioning unit of a vehicle. However, Woods explicitly teaches wherein all modeling data packets for the user are stored in the vehicle, and periodically sent at predefined times from the vehicle to the cloud , and/or the comfort model is stored in a model pool in the cloud , and/or the comfort model is stored in a model pool in the cloud and sent from the model pool to the vehicle , and stored in the vehicle , and/or the comfort model is stored in a model pool in the cloud , and the current comfort model is sent periodically, at predefined times, from the model pool to the vehicle , and/or the comfort model is stored in a model pool in the cloud and sent from the model pool in the cloud to the vehicle after it has been fully trained.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the air conditioning control method of Stevanovic to also include wherein all modeling data packets for the user are stored in the vehicle, and periodically sent at predefined times from the vehicle to the cloud , and/or the comfort model is stored in a model pool in the cloud , and/or the comfort model is stored in a model pool in the cloud and sent from the model pool to the vehicle , and stored in the vehicle , and/or the comfort model is stored in a model pool in the cloud , and the current comfort model is sent periodically, at predefined times, from the model pool to the vehicle , and/or the comfort model is stored in a model pool in the cloud and sent from the model pool in the cloud to the vehicle after it has been fully trained, as taught by Woods, with a reasonable expectation of success. Doing so improves operating an air conditioning unit of a vehicle (With regard to this reasoning, see at least [Woods, [0037-0039, 0043] and [0068]]).
Regarding claim 11, Stevanovic discloses in the modeling loop: the comfort model is stored in a model pool in the cloud after it has been trained, the comfort model is sent from the model pool to the vehicle and then stored in the vehicle ,in the setting loop : the setting data packet is sent to the comfort model stored in the vehicle, the comfort model is executed in the vehicle and the individualized settings are predicted locally in the vehicle (“the adaptation application 46 utilizes the retrieved data as training data for a machine learning algorithm and for building a prediction model” [Col.11 ln 21-23] (model built/trained from cloud profile data) and “the adaptation application 46 retrieves data from the historical comfort setting profile … and runs the prediction model to update the predicted in-vehicle setting(s) … This process is repeated” [Col.11 ln 57-62]).
Regarding claim 12, Stevanovic discloses The method according to claim 8 ,
Stevanovic does not explicitly teach wherein , in the modeling loop : the comfort model is stored after it has been trained in a model pool in the cloud , in the setting loop : the setting data packet is sent from the vehicle to the comfort model stored in the model pool , the comfort model is executed in the cloud , and the individualized settings are predicted in the cloud and subsequently sent to the vehicle
However, Woods does teach wherein , in the modeling loop : the comfort model is stored after it has been trained in a model pool in the cloud , in the setting loop : the setting data packet is sent from the vehicle to the comfort model stored in the model pool , the comfort model is executed in the cloud , and the individualized settings are predicted in the cloud and subsequently sent to the vehicle (“ In some embodiments, the computing device 122 may periodically send updated information from the sensors 124, the HVAC controller 126 and/or the user device to the server 102. Responsively, the server 102 may further update the personal inference model for the vehicle system 104 or the user 110 of the vehicle system 104 based on the periodically received updated information from the vehicle system 104 and/or the collected external data 164. The server 102 may send the trained and/or updated personal inference model back to the vehicle system 104 and the vehicle system 104 may use the personal inference model to predict a target value for the vehicle system 104 or for the user 110 of the vehicle system 104.” [0043] see also [0068]). Both Stevanovic and Woods teach methods for operating an air conditioning unit of a vehicle. However, Woods explicitly teaches the comfort model is stored after it has been trained in a model pool in the cloud , in the setting loop : the setting data packet is sent from the vehicle to the comfort model stored in the model pool , the comfort model is executed in the cloud , and the individualized settings are predicted in the cloud and subsequently sent to the vehicle.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the air conditioning control method of Stevanovic to also include the comfort model is stored after it has been trained in a model pool in the cloud , in the setting loop : the setting data packet is sent from the vehicle to the comfort model stored in the model pool , the comfort model is executed in the cloud , and the individualized settings are predicted in the cloud and subsequently sent to the vehicle, as taught by Woods, with a reasonable expectation of success. Doing so improves operating an air conditioning unit of a vehicle (With regard to this reasoning, see at least [Woods, [0043] and [0068]]).
Regarding claim 13,
Stevanovic does not explicitly teach the comfort model is stored after it has been trained in a model pool in the cloud , the comfort model is sent from the model pool to the vehicle and stored in the vehicle , in the setting loop : if it is not possible to send the setting data packet for the vehicle to the cloud , the setting data packet is sent to the comfort model stored in the vehicle , the comfort model is executed in the vehicle ,and the individualized settings are predicted locally in the vehicle , and when it is possible to send the setting data packet for the vehicle to the cloud , the setting data packet is sent from the vehicle to the comfort model stored in the model pool , the comfort model is executed in the cloud , the individualized settings are predicted in the cloud , and subsequently sent to the vehicle.
However, Woods does teach the comfort model is stored after it has been trained in a model pool in the cloud , the comfort model is sent from the model pool to the vehicle and stored in the vehicle , in the setting loop : if it is not possible to send the setting data packet for the vehicle to the cloud , the setting data packet is sent to the comfort model stored in the vehicle , the comfort model is executed in the vehicle ,and the individualized settings are predicted locally in the vehicle , and when it is possible to send the setting data packet for the vehicle to the cloud , the setting data packet is sent from the vehicle to the comfort model stored in the model pool , the comfort model is executed in the cloud , the individualized settings are predicted in the cloud , and subsequently sent to the vehicle (“ In some embodiments, the computing device 122 may periodically send updated information from the sensors 124, the HVAC controller 126 and/or the user device to the server 102. Responsively, the server 102 may further update the personal inference model for the vehicle system 104 or the user 110 of the vehicle system 104 based on the periodically received updated information from the vehicle system 104 and/or the collected external data 164. The server 102 may send the trained and/or updated personal inference model back to the vehicle system 104 and the vehicle system 104 may use the personal inference model to predict a target value for the vehicle system 104 or for the user 110 of the vehicle system 104.” [0043] see also [0068]). Both Stevanovic and Woods teach methods for operating an air conditioning unit of a vehicle. However, Woods explicitly teaches the comfort model is stored after it has been trained in a model pool in the cloud , the comfort model is sent from the model pool to the vehicle and stored in the vehicle , in the setting loop : if it is not possible to send the setting data packet for the vehicle to the cloud , the setting data packet is sent to the comfort model stored in the vehicle , the comfort model is executed in the vehicle ,and the individualized settings are predicted locally in the vehicle , and when it is possible to send the setting data packet for the vehicle to the cloud , the setting data packet is sent from the vehicle to the comfort model stored in the model pool , the comfort model is executed in the cloud , the individualized settings are predicted in the cloud , and subsequently sent to the vehicle.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the air conditioning control method of Stevanovic to also include the comfort model is stored after it has been trained in a model pool in the cloud , the comfort model is sent from the model pool to the vehicle and stored in the vehicle , in the setting loop : if it is not possible to send the setting data packet for the vehicle to the cloud , the setting data packet is sent to the comfort model stored in the vehicle , the comfort model is executed in the vehicle ,and the individualized settings are predicted locally in the vehicle , and when it is possible to send the setting data packet for the vehicle to the cloud , the setting data packet is sent from the vehicle to the comfort model stored in the model pool , the comfort model is executed in the cloud , the individualized settings are predicted in the cloud , and subsequently sent to the vehicle, as taught by Woods, with a reasonable expectation of success. Doing so improves operating an air conditioning unit of a vehicle (With regard to this reasoning, see at least [Woods, [0043] and [0068]]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Stevanovic in view of Woods and in further view of Ostrowski et al. (US20200094651A1), hereinafter referred to as Stevanovic, Woods and Ostrowski respectively.
Regarding claim 4,
Stevanovic in view of Woods does not explicitly teach wherein the offset settings are created from data in the modeling data packet, stored in the vehicle, and used to set the air conditioner, and as soon as the offset data are incorporated in the comfort model, they are deleted.
However, Ostrowski does teach wherein the offset settings are created from data in the modeling data packet, stored in the vehicle, and used to set the air conditioner, and as soon as the offset data are incorporated in the comfort model, they are deleted (“One or both of the at least one predictive model and the at least one different predictive model may be updated according to the user manual climate control system override action input to provide one or both of an updated unique user climate control system actuation action and an updated unique user climate control system operating pattern. One or both of the updated unique user climate control system actuation action and the updated unique user climate control system operating pattern may be stored in memory.” [0006] and “The controller may be further configured to receive from the one or more sensors a user manual climate control system override action input and to update one or both of the at least one predictive model and the at least one different predictive model according to the user manual climate control system override action input to provide one or both of an updated unique user climate control system actuation action and an updated unique user climate control system operating pattern. The controller may further store one or both of the updated unique user climate control system actuation action and the updated unique user climate control system operating pattern.” [0013]). Both Stevanovic in view of Woods and Ostrowski teach methods for operating an air conditioning unit of a vehicle. However, Ostrowski explicitly teaches wherein the offset settings are created from data in the modeling data packet, stored in the vehicle, and used to set the air conditioner, and as soon as the offset data are incorporated in the comfort model, they are deleted.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the air conditioning control method of Stevanovic in view of Woods to also include wherein the offset settings are created from data in the modeling data packet, stored in the vehicle, and used to set the air conditioner, and as soon as the offset data are incorporated in the comfort model, they are deleted, as taught by Ostrowski, with a reasonable expectation of success. Doing so improves operating an air conditioning unit of a vehicle (With regard to this reasoning, see at least [Ostrowski, 0006 and 0013]).
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 AHMED ALKIRSH whose telephone number is (703) 756-4503. The examiner can normally be reached M-F 9:00 am-5:00 pm EST.
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/AA/Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668