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. Claim Objections Claims 1-13 are objected to because they include reference characters which are not enclosed within parentheses. Reference characters corresponding to elements recited in the detailed description of the drawings and used in conjunction with the recitation of the same element or group of elements in the claims should be enclosed within parentheses so as to avoid confusion with other numbers or characters which may appear in the claims. See MPEP § 608.01(m). Claim Interpretation The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “A first i nput unit 100 acquiring performance… information… ” “ a control unit 300 comprising a plurality of artificial intelligence learning model units outputting initial control values allowing the performance factor current state information…. ” “ an interpolation unit receiving the initial control values generated by the artificial intelligence learning model units … “ “ the interpolation unit 400 transmits the final control value to the air conditioning system ….”, in claim 1 . “ a target input step S100 acquiring, by a first input unit, performance factor target information … ” “ a state input step S200 acquiring, by a second input unit, performance factor current stat information …” “ an AI control step S300 outputting initial control values a final control step S400 generating... ” “ a final control step S400 generating, by an interpolation unit, a final control value …” “ an air conditioning control step S500 performing, by the interpolation unit, artificial intelligence air conditioning by transmitting …”, in claim 7. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 112 The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claim 1-10 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1-10 recite the limitations, “A first i nput unit ”, “ a control unit 300 ” , “ an interpolation unit ”, which have been interpreted under 35 U.S.C. 112(f) above. However since the specification fails to clearly describe the corresponding structures of these limitations as required under the statute, Applicant has failed to demonstrate full possession of the metes and bounds of the invention as claimed. The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claim 1- 10 FILLIN "Enter claim indentification information" \* MERGEFORMAT rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. At most applicant’s specification para. [0036], cite “As shown in FIG. 1, the artificial intelligence air conditioning control system using interpolation according to an embodiment of the present invention is preferably configured to include a first input unit 100, a second input unit 200, a control unit 300, and an interpolation unit 400, and each of these components may be implemented as a single processing means or included within respective processing means to perform respective operation”. It is unclear if a single processing means is hardware or software as the specification fails to describe the corresponding structure in sufficient detail to describe particular structure for the control units of the above limitations. Claim Rejections - 35 USC § 103 This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 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. Claim(s) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20210285671 “Du”, and further in light of U.S. Patent Application Publication No. 20080053128 “Takeda”, Claim 1: Du teaches an artificial intelligence air conditioning control system using interpolation, the system comprising: a first input unit 100 acquiring performance factor target information for air conditioning control through external input (i.e. para. [0068], “AHU controller 330 receives information from BMS controller 366 (e.g., commands, setpoints, operating boundaries, etc.) and provides information to BMS controller 366 (e.g., temperature measurements, valve or actuator positions, operating statuses, diagnostics, etc.)”, wherein the BRI for performance factor target information encompasses a temperature setpoint for air conditioning control of an air handling unit (AHU). Wherein it is noted that the is a separate block from the building management system (BMS) controller and thus the two subsystems are external to each other) ; a second input unit 200 acquiring performance factor current state information from a pre-linked air conditioning system (i.e. para. [0269], “step 2718 may include receiving a current measurement of the indoor air temperature and/or relative humidity in a space”, wherein it is noted that the measurements are gathered by various sensors (e.g., temperature sensors, humidity sensors, etc.) in the zone and/or the external space) ; a control unit 300 comprising a plurality of artificial intelligence learning model units 310 (i.e. para. [0273], “FIG. 29, an illustration of example embodiment of process 2700 using multiple neural networks… in particular a first neural network model 2911 based on the first sample 2901, a second neural network model 2912 based on the second sample 2912, a third neural network model 2913 based on the third sample 2903, and a fourth neural network model 2914 based on the fourth sample 2904. The underlying neural networks may have the same structure (same inputs, same layers, same outputs) but different parameters/weights resulting from differences between data in the samples 2901-2904) configured with different external environmental conditions (i.e. para. [0176], “The neural network model of illustration 9100 is shown to include an input layer 9102, a hidden layer(s) 9104, and an output layer 9106. Input layer 9102 is shown to include multiple input neurons. Each input neuron can be associated with a different variable representing a time of day, environmental conditions, etc. In particular, the input neurons are shown to receive values of a zone temperature T.sub.z , a zone relative humidity RH.sub.z , a time of day, an outdoor air temperature T.sub.oa , an outdoor relative humidity RH.sub.oa , and a season”, wherein input data for training the neural network may be seasons) outputting initial control values (i.e. para. [0207], “the neural network may be configured such that the output layer of the neural network directly provides minimum and maximum values for a range within which the occupants are predicted to be comfortable”, wherein the BRI for initial control values encompass a first output set of comfort ranges for temperature control) allowing the performance factor current state information from the second input unit 200 to track the performance factor target information from the first input unit 100 based on the configured external environmental conditions (i.e. para. [0172], “To determine the change in the temperature setpoint, one potential method is to use a separate neural network to classify a day based on outdoor air conditions”, wherein current temperature may be tracked and adjusted to meet an comfort temperature range based on configured outside seasonal temperatures) . While Du teaches an air conditioning control unit and attached input sensors for tracking current and target temperature information to a neural network that outputs an initial group control value for a minimum and maximum AC settings, Du may not explicitly teach , an interpolation unit 400 receiving the initial control values generated by the artificial intelligence learning model units 310 from the control unit 300 to generate an interpolation function and generating a final control value by applying a current external environmental condition input in real-time to the interpolation function, wherein the interpolation unit 400 transmits the final control value to the air conditioning system for artificial intelligence air conditioning. However, Takeda teaches an interpolation unit 400 receiving the initial control values generated by the artificial intelligence learning model units 310 from the control unit 300 to generate an interpolation function (i.e. para. [0009], “the control set temperature that corresponds to the environmental condition detected value is interpolated and calculated from the control set temperatures stored in the predetermined points of the plural areas. The interpolated and calculated control set temperature can be used to control the air conditioning in the vehicle compartment”, wherein the BRI for initial control values encompass control set temperature before interpolation. Wherein it is noted that the BRI for a generated interpolation function encompasses using the control set temperatures as input to a membership function based on based on fuzzy control theory, from the control set temperature TSETc stored in the center point 62a of the detection area) and generating a final control value by applying a current external environmental condition input in real-time to the interpolation function (i.e. para. [0078], In step S134, the control set temperature TSETc at the detection point of the environmental conditions is interpolated and calculated from the control set temperature TSETc stored in the center point of the detection area including the detection point of the environmental conditions on the map and a control set temperature TSETc stored in a center point of a surrounding area of the detection area) , wherein the interpolation unit 400 transmits the final control value to the air conditioning system for artificial intelligence [learning] air conditioning (i.e. para. [0066], In next step S140, a target temperature TAO of air to be blown off into the vehicle compartment is calculated based on the environmental condition signals read in step S120 and the control set temperature TSETc (i.e., the control set temperature) or the like after learning) . It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add an interpolation unit 400 receiving the initial control values generated by the intelligence learning model units 310 from the control unit 300 to generate an interpolation function and generating a final control value by applying a current external environmental condition input in real-time to the interpolation function, wherein the interpolation unit 400 transmits the final control value to the air conditioning system for intelligence air conditioning, to Du’s artificial intelligence air conditioning systems that output initial temperature control setpoints , with how initial temperature control setpoints may be further refined by a generated interpolation equation that accounts for environmental conditions and results in final temperature control values that are sent to an air conditioning system , as taught by Takeda . One would have been motivated to combine the interpolation and environmental considerations into the temperature control refinement of Takeda with neural network that generates air conditioning setpoints of Du , would have had a reasonable expectation of success as air-conditioned control can be easily provided with high accuracy for reflecting preference for the set temperature. Claim 2: Du and Takeda teach the artificial intelligence air conditioning control system using interpolation of claim 1. Du further teaches wherein the control unit 300 further comprises a learning processing unit 320 training the performance factor target information and performance factor current state information collected based on external environmental conditions of different predetermined temperatures in association with pre-executed air conditioning control using a predetermined AI algorithm (i.e. para. [0172], “constraint generator 9016 can feed conditions into the separate neural network to generate classifications to use in the primary neural network … a particular day may be labeled as {Hot, Extremely Dry} whereas another day may be labeled as {Extremely Cold, Dry} as based on data fed into the separate neural network. Said labels can be provided to the primary neural network to predict the setpoint changes for the associated days”, wherein the BRI for a predetermined AI algorithm encompasses a neural network trained on particular labeled data, such as hot and dry days) , generating external environment condition-specific artificial intelligence learning models (i.e. para. [0177], the neural network can be generated based on a simulation framework set up to populate an occupant temperature override dataset) , and transmitting the interfacial intelligence learning models to the artificial intelligence learning model unit 310 (i.e. para. [0223], process 1800 shows a multi-stage approach where the discomfort tolerance is first generated (independent of particular building conditions) and then used to determine the building conditions that correspond to the discomfort tolerance under given conditions (e.g., for a particular time of day, day of week, type of day, outside weather conditions, etc.). Providing these as separate stages/determinations can provided increased adaptability and conformability to changing preferences and conditions as compared to other approaches) . Claim 3: Du and Takeda teach the artificial intelligence air conditioning control system using interpolation of claim 2. Du further teaches wherein the learning processing unit 320 sets intervals for the collected performance factor target information associated with pre-executed air conditioning control within predetermined ranges and establishes a midpoint or a specific value for each interval as representative target information (i.e. para. [0144], Referring now to FIG. 9, a graph 8900 illustrating fixed minimum and maximum comfort bounds that a zone may operate based on under a current MPC algorithm is shown) , sets intervals for the collected performance factor current state information associated with pre-executed air conditioning control within predetermined ranges (i.e. para. [0182], , comfort constraints can be generated for use in MPC. In general, process 9300 can result in an upper bound and a lower bound being generated for temperature. It should be appreciated, however, that process 9300 can be performed to generate constraints for other conditions such as relative humidity) and establishes a midpoint or a specific value for each interval as the representative target information (i.e. para. [0175], “the constraints can be found by iterating over each hour and sweeping the zone temperature to find minimum and maximum bounds for each hour. More specifically, a zone temperature range can be chosen (e.g., 15° C. to 30° C., 20° C. to 31° C., etc.), and for each hour, the trained neural network can predict the change in setpoint for that hour at each of the zone temperatures in the chosen range”, wherein the BRI for a specific value encompasses a lower and upper bound) , generates training data by matching the representative target information and representative state information and control values corresponding to the representative target information and state information (i.e. para. [0161], “to provide for generation of sufficient training data, comfort controller 9000 can include a zone simulator 9012 to model the indoor conditions in a zone, given real outdoor air conditions”, wherein a simulator may generate training data that may be matched for certain temperature zone simulation conditions) and performs the training process of the training data using the predetermined Al algorithm, based on different predetermined temperature values of the external environmental conditions (i.e. para. [0162], “Using real past weather measurements for a particular site, zone simulator 9012 can produce indoor air conditions, including zone temperatures and zone humidity ratios, for a corresponding time period”, wherein AI algorithms for a particular time of day, day of week, type of day, outside weather conditions, etc may be trained using condition specific generated training data) . Claim 4: Du and Takeda teach the artificial intelligence air conditioning control system using interpolation of claim 3. Du further teaches wherein the artificial intelligence learning model unit 310 receives the external environmental conditions of different predetermined temperatures as reference learning models based on the training processing result from the learning processing unit 320 (i.e. para. [0176], “Each input neuron can be associated with a different variable representing a time of day, environmental conditions, etc. In particular, the input neurons are shown to receive values of a zone temperature T.sub.z , a zone relative humidity RH.sub.z , a time of day, an outdoor air temperature T.sub.oa , an outdoor relative humidity RH.sub.oa , and a season”, wherein different seasons or times of day have predetermined temperatures) and outputs the initial control values (i.e. para. [0182], a process 9300 for determining hourly comfort constraints is shown, according to some embodiments. By performing process 9300, comfort constraints can be generated for use in MPC. In general, process 9300 can result in an upper bound and a lower bound being generated for temperature) allowing for the performance factor current state information from the second input unit 200 to track the performance factor target information form the first input unit 100 by reflecting the external environmental conditions specific to each learning model (i.e. para. [0175], As such, constraint generator 9016 can provide the comfort constraints to the economic controller 610 and/or the tracking controller 612 via communications interface 9008, so that MPC can be performed subject to the comfort constraints via the economic controller 610 and the tracking controller 612) . . Claim 5: Du and Takeda teach the artificial intelligence air conditioning control system using interpolation of claim 4. Du further teaches wherein the artificial intelligence learning model unit 310 selects an interval corresponding to the performance factor target information from the first input unit 100 (i.e. para. [0171], Based on the fed inputs, the neural network can output a change in a temperature setpoint that corresponds to a particular hour and/or some other unit of time. Inputs to the neural network can include, for example, a time of day (e.g., a particular hour), season of the year, indoor air temperature, indoor humidity, information on the outdoor air conditions, etc.) and an interval corresponding to the performance factor current state information from the second input unit 200 (i.e. para. [0066], “AHU controller 330 operates valves 346 and 352 via actuators 354-356 to modulate an amount of heating or cooling provided to supply air 310 (e.g., to achieve a setpoint temperature for supply air 310 or to maintain the temperature of supply air 310 within a setpoint temperature range)) by reflecting the ranges set by the learning processing unit 320, and outputs the initial control values by applying specific result information to each learning model (i.e. para. [0066], “The positions of valves 346 and 352 affect the amount of heating or cooling provided to supply air 310 by cooling coil 334 or heating coil 336 and may correlate with the amount of energy consumed to achieve a desired supply air temperature. AHU 330 may control the temperature of supply air 310”, wherein the setpoint range of desired temperature factor target information may reflect a setpoint Minium and maximum for an AI algorithm trained on a predetermined environmental condition such as a season or time of day) Claim 6: Du and Takeda teach the artificial intelligence air conditioning control system using interpolation of claim 5. Takeda further teaches wherein the interpolation unit 400 generates an interpolation function for the initial control values using a predetermined interpolation algorithm (i.e. para. [0086], “, a control set temperature TSETc at the detection point 63 is interpolated and calculated to be 25.525.degree. C., using the membership functions shown in FIGS. 8A and 8B for the outside air temperature TAM and the solar radiation amount TS by the following formula (1)”, wherein the BRI for a predetermined equation encompasses the predetermined inputs to the following formula) and applies a current external environmental condition input in real-time (i.e. para. [0056], “An outside air temperature sensor 40 serves as an outside air temperature detecting unit for detecting the temperature of air outside the vehicle compartment (outside air temperature) TAM”, wherein an outside air temperature sensor may detect outside air in real time) to the interpolation function to generate the final control value (i.e. para. [0133], In step S234, the control set temperature TSETc corresponding to the detection point 63 of the environmental conditions detected by the step S131 is read from the control set temperatures TSETc stored on the map) . Claim 7: Claim 7 is the method claim reciting similar limitations to claim 1 and is rejected for similar reasons. Claim 8: Claim 8 is the method claim reciting similar limitations to claim 2 and is rejected for similar reasons. Claim 9: Claim 9 is the method claim reciting similar limitations to claim 3 and is rejected for similar reasons. Claim 10: Claim 10 is the method claim reciting similar limitations to claim 5 and is rejected for similar reasons. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. U.S. Patent Application Publication 20170274573 “ Takii ” teaches in para. [0028], The temperature control device of the invention includes a reference energization rate calculation unit that creates, by means of the PID control, a first energization rate y1 based on a first temperature difference e1 between the actually measured temperature Ta and the target temperature Ts; an interpolation calculation unit that creates a second energization rate y2 based on a second temperature difference e2 between an ambient temperature Te of an environment in which the heating target is disposed and the target temperature Ts; a comparison calculation unit that obtains a corrected energization Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT DAVID H TAN whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-7433 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 7:30-4:30 . 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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. /D.T./ Examiner, Art Unit 2145 /CESAR B PAULA/ Supervisory Patent Examiner, Art Unit 2145