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 .
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-2, 11-13, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nishimura (JP 2021-57008; note that US 2022/0316741 is utilized herein as an English translation) in view of Matsuura (US 2021/0140670).
As to claim 1, Nishimura teaches an air conditioning control system for performing air conditioning of a target space, the system comprising:
a processor (paragraph 63) including
a generation unit configured to generate a plurality of candidates for an operation condition of the air conditioner (Fig. 6, step S202);
a prediction unit configured to use a learning model in order to predict a comfort level of occupants based on the operation condition (paragraphs 122-123);
an evaluation unit configured to evaluate the environment state corresponding to the operation condition (paragraphs 124-125);
an extraction unit configured to extract the operation condition for which an evaluation by the evaluation unit satisfies a predetermined condition (Fig. 6, step S204); and
a control unit 40 configured to control the air-conditioning apparatus with the extracted operation condition (Fig. 1; paragraph 133).
Nishimura does not explicitly teach utilizing the prediction unit to predict an environment state of the target space as claimed. However, one of ordinary skill would recognize the advantage of determining a future environment state when predicting a future comfort level of occupants. Furthermore, Matsuura teaches using a prediction unit 25 to predict an environment state based on acquired data (paragraph 77). In light of this teaching it would have been obvious to a person having ordinary skill in the art, before the effective filing date, to modify Nishimura such that the prediction unit predicts an environment state of the target space as claimed in order to increase the accuracy of user comfort predictions.
As to claim 2, Nishimura teaches the processor including a learning unit configured to generate the learning model (paragraph 74).
As to claims 11 and 17, Nishimura teaches use of reinforcement learning (paragraph 6).
As to claim 12, Nishimura teaches generating operation condition candidates for a next operation based on the current operation condition (paragraphs 120-123).
As to claims 13 and 18, the control system of Nishimura is capable of operating to control an air conditioner that targets a space including an aisle between server racks.
Claims 3-10 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Nishimura and Matsuura as applied in the rejections above, and further in view of Ono (JP 2004-069273, see English translation included with IDS filed 2/21/2024).
As to claim 3, Nishimura does not explicitly teach that the processor utilizes a computational fluid dynamics simulation in the manner as claimed. However, Ono teaches that it is known to perform a CFD simulation to determining indoor conditioning distributions (see last three lines of page 4 and first paragraph of page 5). In light of this teaching it would have been obvious to a person having ordinary skill in the art, at the time of the invention, to modify the processor Nishimura to include a unit performing a computational fluid dynamics simulation and to predict the environment state using the simulation in order to improve the accuracy of the prediction for the entire space.
As to claim 4, the modified apparatus would be capable of performing the intended use of utilizing explanatory and objective variables in the manner as claimed.
As to claims 5-6, the modified apparatus teaches most of the limitations of the claims as discussed in the rejections of claims 3-4 above, but does not explicitly teach use of a second environment state having a lower simulation accuracy. However, such a numerical method is well understood in mathematics and engineering and thus one of ordinary skill in the art would have found it obvious to use such a method in order to improve the accuracy of the calculations.
As to claims 7-10, the modified apparatus teaches the limitations of the claims for the same reasons as discussed in the rejections of claims above.
As to claims 14-16, the claims are rejected for the same reasons as set forth in the rejections above.
Response to Arguments
Applicant’s arguments, see page 8, filed 12/15/2025, with respect to the objection to the specification and claim interpretation under 35 U.S.C. 112(f) have been fully considered and are persuasive. Said objection and 112(f) interpretation have been withdrawn.
Applicant’s arguments, see pages 9-12, filed with respect to the rejection(s) of claim(s) under 35 U.S.C. 102 & 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Matsuura (US 2021/0140670).
Conclusion
This correspondence is being made NON-FINAL to afford the applicant the opportunity to respond to the new ground(s) of rejection.
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/JONATHAN BRADFORD/ Primary Examiner, Art Unit 3763