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
This Office action is in reply to filing by applicant on 11/20/2025.
Claims 1, 2, and 5 were amended by Applicant.
Claims 6 – 11 were previously presented by Applicant.
Claims 12 – 19 are new.
Claims 3 and 4 remain as original.
The prior 35 USC 102 claim rejections set forth in the Non-Final rejection of 09/19/2025 as to claims 1 and 5 are no longer made in view of Applicant's arguments and amendments.
The prior 35 USC 103 claim rejections set forth in the Non-Final rejection of 09/19/2025 as to claims 2 – 4 and 6 - 11 are maintained in view of Applicant's arguments and amendments. Independent claims 1 and 5 are also rejected herein per 35 USC 103.
THIS ACTION IS MADE FINAL.
Response to Arguments
There are no new grounds of rejection herein as to any of the claims.
Applicant’s arguments as to 35 USC 102 are moot. Applicant’s arguments as to 35 USC 103 are also moot as the combination argued by Applicant, namely, Seok – Young in view of Rakshit, is no longer used by examiner in this action.
Appellant’s argument specifically addresses the amended claims herein respecting the newly added machine learning (claims of 11/20/2025), which machine learning related claims had not yet been addressed by examiner. As such, those several machine learning claims are specifically addressed and are otherwise responded to in the following 35 USC 103 section.
Generally as to obviousness, examiner submits that it is determined on the basis of the evidence as a whole and the relative persuasiveness of the arguments. See In re Oetiker, 977 F.2d 1443, 1445, 24 USPQ2d 1443, 1444 (Fed. Cir. 1992); In re Hedges, 783 F.2d 1038, 1039, 228 USPQ 685,686 (Fed. Cir. 1992); In re Piasecki, 745 F.2d 1468, 1472, 223 USPQ 785,788 (Fed. Cir. 1984); and In re Rinehart, 531 F.2d 1048, 1052, 189 USPQ 143,147 (CCPA 1976). Using this standard, examiner submits that the burden of presenting a prima facie case of obviousness was successfully established in the prior Office Action of 09/19/2025, and also respecting the pending amended claim set of 11/20/2025, as seen below.
Examiner recognizes that references cannot be arbitrarily altered or modified, and that there must be some reason why a person having ordinary skill in the relevant art would be motivated to make the proposed modifications. Although the motivation or suggestion to make modifications must be articulated, it is respectfully submitted that there is no requirement that the motivation to make modifications must be expressly articulated within the references themselves. References are evaluated by what they suggest to one versed in the art, rather than by their specific disclosures, In re Bozek, 163 USPQ 545 (CCPA 1969).
Examiner also notes that the motivation to combine the applied references is, where appropriate in the below detailed analysis pursuant to 35 USC 103, additionally accompanied by select passages from the respective references which specifically support that particular motivation. It is also respectfully submitted that motivation based on the logic and scientific reasoning of one ordinarily skilled in the art at the time of the invention, which evidence can also support a finding of obviousness, is otherwise provided in the detailed 35 USC 103 analysis of the claim set below. In re Nilssen, 851 F.2d 1401, 1403, 7 USPQ2d 1500, 1502 (Fed. Cir. 1988) (references do not have to explicitly suggest combining teachings); Ex parte Clapp, 227 USPQ 972 (Bd. Pat. App. & Inter. 1985) (examiner must present convincing line of reasoning supporting rejection); and Ex parte Levengood, 28 USPQ2d 1300 (Bd. Pat. App. & Inter. 1993) (reliance on logic and sound scientific reasoning).
Examiner recognizes that obviousness can only be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to a person of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988) and In re Jones, 958 F.2d 347.
Claim Rejections – 35 USC 103
In the event the determination of the status of the application as subject to AIA 35 USC 102 and 103 is incorrect, any correction of the statutory basis 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 USC 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 USC 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 – 11 are rejected pursuant to 35 USC 103 as being unpatentable over Seok-Young (US20200180396A1) in view of Maeng (US20210078382A1) and in further view of Ikuo (US20210221399A1).
Regarding claims 1 and 5:
Seok-Young discloses:
an air-conditioning control device comprising a processor comprising hardware, the processor being configured to execute: (“A vehicle is equipped with an air conditioner (heating ventilation and air conditioning, HVAC) capable of controlling the temperature of an indoor space by supplying cold air or warm air to the indoor space.”, [003]) and (“The controller 130 may include at least one memory storing a program for performing the above - described operations and operations which will be described below, and at least one processor for executing the stored program.”, [084]) and
estimating an emotion of an occupant in a vehicle interior when a spot air conditioning is performed on the occupant; and (“The controller may be configured to obtain, when the type of wind blowing at the predetermined time intervals changes, emotion information corresponding to each type of wind based on at least one of the image data or the bio-signal”, [012]) and (“The type of wind may be classified according to at least one of a temperature, strength, or direction of wind blowing to the inside of the vehicle.”, [010]) and (“The predetermined condition may include when a predetermined time elapses from when wind starts being blown through the air conditioner, or when a degree of positiveness of emotion information obtained after wind starts being blown through the air conditioner is equal to or greater than the threshold level.”, [013]);
controlling the spot air conditioning based on an estimation result. (“Embodiments of the disclosure relate to a vehicle that controls an air conditioner based on a passenger’s emotion and method for controlling a vehicle.”, [002]) and (“A controller is configured to control , when a predetermined condition is satisfied, the air conditioner to change a type of wind blowing at predetermined time intervals, to obtain emotion information corresponding to the changed type of wind based on at least one of the image data or the bio-signal, and to blow a type of wind for which a degree of positiveness of emotion information is equal to or greater than a threshold level.”, [008]);
Seok-Young does not expressly disclose, but Maeng teaches:
using a machine learning model, (“Disclosed is a vehicle air conditioning control method which operates a vehicle air conditioning control apparatus by executing an artificial intelligence (AI) algorithm and/or a machine learning algorithm “, [Abstract, published 03/18/2021]);
the machine learning model is trained to receive a numerical value indicating the estimated emotion of the occupant as input, and Examiner notes that the “receiving a numerical value” language of the above limitation is specifically addressed in the Ikuo reference which immediately follows, that said … (“At this time, the AI server 20 may train the AI network according to the machine learning algorithm … and may directly store the learning model or transmit the learning model to the AI devices 30 a to 30 e. At this time, the AI server 20 may receive input data from the AI device 30 a to 30 e, infer a result value from the received input data by using the learning model,”, [092]) and (“Therefore, the air conditioning adjuster 176 may control the wind direction adjusting motor 150-1 and the wind volume adjusting motor 150-2 based on the thermal comfort information of the human body of each passenger. For example, when the human body feels cold or hot, the air conditioning may be controlled to control the temperature, the humidity, the strength and the direction of the air flow to raise or lower the temperature. That is, the air conditioning adjuster 176 identifies a thermal comfort index calculated based on the thermal comfort information of each passenger and detects a passenger having a thermal comfort index which is equal to or lower than a reference value, among the passenger in the vehicle.”, [0262]), the AC utilizes machine learning through a comfort value read (comfort index read) when “the human body feels cold or hot” (an emotion), and then controls at least the wind direction of the AC;
to output an on-off timing of the spot air conditioning, a switching destination of a target of the spot air conditioning, a temperature of the spot air conditioning, or a wind direction of the spot air conditioning. (“Therefore, the air conditioning adjuster 176 may control the wind direction adjusting motor 150-1 and the wind volume adjusting motor 150-2 based on the thermal comfort information of the human body of each passenger. For example, when the human body feels cold or hot, the air conditioning may be controlled to control the temperature, the humidity, the strength and the direction of the air flow to raise or lower the temperature. That is, the air conditioning adjuster 176 identifies a thermal comfort index calculated based on the thermal comfort information of each passenger and detects a passenger having a thermal comfort index which is equal to or lower than a reference value, among the passenger in the vehicle.”, [0262]), the AC utilizes machine learning through a comfort value read (comfort index read) when “the human body feels cold or hot” , which is a specific human emotion regarding the generalized occupant emotion claimed), and then controls the wind direction of the AC;
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Seok-Young to incorporate the teachings of Maeng because Seok-Young would be more efficient, add passenger comfort, and/or the AC system would otherwise be more transparent to vehicle passengers if it adopted machine learning to automatically control operation of the AC, as done in Maeng. (“ the third deep neural network model may be a learning model trained to extract the changed value of the thermal comfort information of the human body based on at least one of vehicle internal environment sensing information, vehicle external environmental sensing information, and vehicle control sensing information and correct the thermal comfort information of the human body based on the changed value.”, [043]);
The combination of Seok-Young and Maeng do not expressly disclose, but Ikuo teaches:
a numerical value indicating the estimated emotion of the occupant (“A technique has been proposed that reflects driving properties of a driver, an occupant, or a passenger of a vehicle on traveling control of the vehicle to make the driver, the occupant, or the passenger less likely to have negative emotions such as anxiety or a sense of discomfort during the travel of the vehicle.”, [003]) and (“For example, the sensitivity analysis may employ a method of specifying a data item that significantly changes the emotion level to be outputted when being deleted from the data items to be outputted or when the numerical value of the data item is changed. … Accordingly, it is possible to set the control parameter that causes an occupant's emotion to become closer to the ideal emotion while preferentially setting the input value of the data item that gives a significant impact on the emotion of each individual occupant.”, [069]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Seok-Young to incorporate the teachings of Ikuo because Seok-Young would be more exact if it assigned numerical values to vehicle occupant emotion, then it could better compare and otherwise measure changes to such occupant emotion levels, as done Ikuo.”) (“For example, the sensitivity analysis may employ a method of specifying a data item that significantly changes the emotion level to be outputted when being deleted from the data items to be outputted or when the numerical value of the data item is changed.”, [069]).
Regarding claim 2:
The combination of Seok-Young, Maeng, and Ikuo disclose the limitations of claim 1:
Maeng further teaches:
wherein the estimating of the emotion of the occupant includes acquiring sensor data from a sensor configured to detect a state of the occupant, and
(“The vehicle air conditioning control method includes acquiring a thermal image in a vehicle using an image sensor, acquiring thermal comfort information of each passenger in the vehicle using the thermal image, and controlling air conditioning of the vehicle based on the thermal comfort information of each passenger.”, [Abstract], published 3/18/2021]);
estimating the emotion of the occupant from the acquired sensor data using the machine learning model. (“At this time, the AI server 20 may train the AI network according to the machine learning algorithm … and may directly store the learning model or transmit the learning model to the AI devices 30 a to 30 e. At this time, the AI server 20 may receive input data from the AI device 30 a to 30 e, infer a result value from the received input data by using the learning model,”, [092]) and (“For example, when the human body feels cold or hot, [an emotion] the air conditioning may be controlled to control the temperature, the humidity, the strength and the direction of the air flow to raise or lower the temperature. That is, the air conditioning adjuster 176 identifies a thermal comfort index calculated based on the thermal comfort information of each passenger and detects a passenger having a thermal comfort index which is equal to or lower than a reference value, among the passenger in the vehicle.”, [0262]), the AC utilizes machine learning to estimate and ameliorate comfort levels / emotions.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Seok-Young to incorporate the teachings of Maeng because Seok-Young would be more efficient, add passenger comfort, and/or the AC system would otherwise be less obtrusive to vehicle passengers, if it adopted machine learning to automatically control operation of the AC, as done in Maeng. (“ the third deep neural network model may be a learning model trained to extract the changed value of the thermal comfort information of the human body based on at least one of vehicle internal environment sensing information, vehicle external environmental sensing information, and vehicle control sensing information and correct the thermal comfort information of the human body based on the changed value.”, [043]).
Regarding claim 3:
The combination of Seok-Young, Maeng, and Ikuo disclose the limitations of claim 2:
Seok-Young further teaches:
wherein the sensor includes a camera installed in the vehicle interior, and the sensor data includes image data obtained by the camera. (“In accordance with an aspect of the disclosure, there is provided a vehicle including an air conditioner, a temperature sensor configured to measure an indoor temperature of the vehicle, a camera configured to obtain an image data of a passenger, and a bio-signal sensor configured to measure a bio-signal of the passenger.”, [008]).
Regarding claim 4:
The combination of Seok-Young, Maeng, and Ikuo disclose the limitations of claim 1:
Maeng further teaches:
wherein the estimating of the emotion of the occupant is repeatedly executed, and the controlling of the spot air conditioning based on the estimation result includes adjusting a parameter of the spot air-conditioning such that a repeatedly obtained estimation result of the emotion of the occupant varies in a direction of comfort from discomfort. Examiner interprets this claim to include the meaning that the ongoing monitoring of the emotion of the vehicle occupant used to control the air conditioner settings as above are simply adjusted towards comfort, continuously, going forward … (“The present disclosure relates to an apparatus and a method for controlling air conditioning of a vehicle, and more particularly, to an apparatus and a method for controlling air conditioning of a vehicle which individually control the air conditioning for every passenger, based on thermal comfort information of each passenger in the vehicle.”, [002]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Seok-Young to incorporate the teachings of Maeng because Seok-Young would be more efficient by continually monitoring the comfort of vehicle passengers as done in Maeng. (“An aspect of the present disclosure is directed to individually controlling the air conditioning corresponding to individual passengers based on thermal comfort information of individual passengers in the vehicle.”, [008]).
Regarding claim 6:
The combination of Seok-Young, Maeng, and Ikuo disclose the limitations of claim 3:
Seok-Young further teaches:
wherein the estimating of the emotion of the occupant is based on an expression of the occupant included in the image data. (“FIG. 4 shows correlation information between facial expressions and emotion factors according to an embodiment of the disclosure;”, [038]) and (“Embodiments of the disclosure relate to a vehicle that controls an air conditioner based on a passenger's emotion, and a method for controlling the vehicle.”, [002]) and (“In accordance with an aspect of the disclosure, there is provided a vehicle including an air conditioner, a temperature sensor configured to measure an indoor temperature of the vehicle, a camera configured to obtain an image data of a passenger,”, [008]).
Regarding claim 7:
The combination of Seok-Young, Maeng, and Ikuo disclose the limitations of claim 2:
Seok-Young further teaches:
wherein the sensor includes a biometric sensor installed in the vehicle interior, and the sensor data includes biometric data obtained by the biometric sensor. (“Therefore, it is an aspect of the disclosure to provide a vehicle for providing in-vehicle air conditioning in real time based on at least one of an indoor temperature, a passenger's bio-signal, or a passenger's emotion to induce the passenger's satisfaction, and a method for controlling the vehicle.”, [006]) and (“In accordance with an aspect of the disclosure, there is provided a vehicle including an air conditioner, a temperature sensor configured to measure an indoor temperature of the vehicle, a camera configured to obtain an image data of a passenger, and a bio-signal sensor configured to measure a bio-signal of the passenger.”, [008]).
Regarding claim 8:
The combination of Seok-Young, Maeng, and Ikuo disclose the limitations of claim 7:
Seok-Young further teaches:
wherein the estimating of the emotion of the occupant is based on a body temperature, a heartbeat, a pulse, a blood pressure, or an electroencephalogram of the occupant included in the biometric data. (“The bio-signal sensor may include at least one of a heart rate (HR) sensor configured to measure a heart rate of the passenger, a skin temperature sensor configured to measure a skin temperature of the passenger, a galvanic skin response (GSR) sensor configured to measure skin electrical conductivity depending on a sweat rate of the passenger, or a blood pressure measurement sensor configured to measure blood pressure of the passenger.”, [016]).
Regarding claim 9:
The combination of Seok-Young, Maeng, and Ikuo disclose the limitations of claim 2:
Seok-Young further teaches:
wherein the sensor includes a camera and a biometric sensor that are installed in the vehicle interior, and the sensor data includes image data obtained by the camera and biometric data obtained by the biometric sensor. (“Therefore, it is an aspect of the disclosure to provide a vehicle for providing in-vehicle air conditioning in real time based on at least one of an indoor temperature, a passenger's bio-signal, or a passenger's emotion to induce the passenger's satisfaction, and a method for controlling the vehicle.”, [006]) and (“In accordance with an aspect of the disclosure, there is provided a vehicle including an air conditioner, a temperature sensor configured to measure an indoor temperature of the vehicle, a camera configured to obtain an image data of a passenger, and a bio-signal sensor configured to measure a bio-signal of the passenger.”, [008, and see 021]).
Regarding claim 10:
The combination of Seok-Young, Maeng, and Ikuo disclose the limitations of claim 9:
Seok-Young further teaches:
wherein the estimating of the emotion of the occupant is based on an expression of the occupant included in the image data and on a body temperature, a heartbeat, a pulse, a blood pressure, or an electroencephalogram of the occupant included in the biometric data. (“FIG. 4 shows correlation information between facial expressions and emotion factors according to an embodiment of the disclosure;”, [038]) and (“Embodiments of the disclosure relate to a vehicle that controls an air conditioner based on a passenger's emotion, and a method for controlling the vehicle.”, [002]) and (“The bio-signal sensor may include at least one of a heart rate (HR) sensor configured to measure a heart rate of the passenger, a skin temperature sensor configured to measure a skin temperature of the passenger, a galvanic skin response (GSR) sensor configured to measure skin electrical conductivity depending on a sweat rate of the passenger, or a blood pressure measurement sensor configured to measure blood pressure of the passenger.”, [016]).
Regarding claim 11:
The combination of Seok-Young, Maeng, and Ikuo disclose the limitations of claim 4:
Seok-Young further teaches:
wherein the parameter of the spot air-conditioning includes a target of the spot air conditioning, an air volume, a temperature, or a wind direction. (“In accordance with another aspect of the disclosure, there is provided a method of controlling a vehicle, the vehicle including an air conditioner, a temperature sensor configured to measure an indoor temperature of the vehicle,”, [021]).
Allowable Subject Matter
Claims 12 – 19 would be allowable if rewritten or amended to at least be in an independent form. The following is a statement of reasons for the indication of allowable subject matter: Independently, while the claims' limitations most recently set forth herein may individually be disclosed by the prior art, the claims as a whole are not obvious because the examiner would have to improperly use their separate limitations as a road map to combine them.
CONCLUSION
THIS ACTION IS MADE FINAL. 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 extension fee 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.
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form 892.
Sobhany (US20200242421A1) - A sensor fusion system associated with a vehicle includes a sensor interface communicatively coupled to a plurality of sensors in the vehicle and a vehicle experience system. The sensor interface comprises an input receiving data from each of the plurality of sensors and an output configured to output fused vehicle data based on the data received from the plurality of sensors. The vehicle experience system is coupled to the output of the sensors interface to receive the fused vehicle data. The vehicle experience system includes one or more processors and a non-transitory computer readable storage medium storing instructions that when executed by one or more processors cause the one or more processors to control at least one parameter of the vehicle based on the fused vehicle data.
Youn (US20200180396A1) – The vehicle includes an air conditioner, a temperature sensor configured to measure an indoor temperature of the vehicle, a camera configured to obtain an image data of a passenger, and a bio-signal sensor configured to measure a bio-signal of the passenger. A controller is configured to control, when a predetermined condition is satisfied, the air conditioner to change a type of wind blowing at predetermined time intervals, to obtain emotion information corresponding to the changed type of wind based on at least one of the image data or the bio-signal, and to blow a type of wind for which a degree of positiveness of emotion information is equal to or greater than a threshold level.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW COBB whose telephone number is (571) 272-3850. The examiner can normally be reached 9 - 5, M - F.
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 call examiner Cobb as above, or 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, Peter Nolan, can be reached at (571) 270-7016. 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.
/MATTHEW COBB/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661