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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/1/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-7 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Martinez et al. [Martinez] (US PGPub 2022/0026086).
As to claim 1
Martinez discloses an air-conditioning controller (thermostat device 102, see Fig. 1) comprising:
a control board (electronic circuit board 150, see Fig. 1) configured to control an air-conditioning apparatus (HVAC system; see paragraph 0035, lines 11) (see paragraph 0035, lines 8-11);
an enclosure (housing 160, see Fig. 1) housing the control board (see Fig. 1); and
a plurality of temperature sensors (sensors 130, 132; see Fig. 1), wherein
the control board includes at least one heat-generating component (components; see paragraph 0036, line 1/processing circuitry 104, memory 105, fan relay 106, heat relay 108, cool relay 110, voltage measurement circuitry 109; see Fig. 1), the at least one heat-generating component being a heat source that raises a temperature in the enclosure when powered (see paragraph 0042, lines 10-16),
the plurality of temperature sensors are located at a plurality of respective positions (areas/locations) in the enclosure and heat from the at least one heat-generating component causes a difference in temperature between the plurality of positions (see paragraph 0041, lines 8-20 and paragraph 0043, lines 16-21), and
the control board stores a trained model (temperature compensation model 120, see Fig. 1) for room temperature estimation generated by machine learning (machine learning) and is configured to estimate a temperature of a room installed with the air-conditioning controller (room as where thermostat device 102 is located; see paragraph 0043, line 16) using temperature data from each of the plurality of temperature sensors and the trained model (see paragraph 0050, lines 1-13 and paragraph 0052, lines 1-7).
As to claim 2
Martinez discloses the air-conditioning controller according to claim 1, wherein
the plurality of temperature sensors include a first temperature sensor (temperature sensor 130, see Fig. 1) and a second temperature sensor (temperature sensor 132, see Fig. 1), and
a distance (placed near; see paragraph 0041, line 13) from the at least one heat-generating component to the first temperature sensor is shorter than a distance (placed in more isolated location; see paragraph 0041, line 17) from the at least one heat-generating component to the second temperature sensor (see paragraph 0041, lines 8-20).
As to claim 3
Martinez discloses the air-conditioning controller according to claim 1, wherein
the at least one heat-generating component includes a plurality of heat-generating components, and
the plurality of temperature sensors include a first temperature sensor located in a predetermined region in which the plurality of heat-generating components are located and a second temperature sensor located in a region other than the predetermined region (see paragraph 0041, lines 8-20).
As to claim 4
Martinez discloses the air-conditioning controller according to claim 2, wherein
the first temperature sensor is located at a position to which the heat is to be transferred from the at least one heat-generating component through the control board (see paragraph 0041, lines 8-20 and paragraph 0042, lines 10-19).
As to claim 5
Martinez discloses the air-conditioning controller according to claim 2, wherein
the second temperature sensor is located on the control board and partially separated from the at least one heat-generating component on the control board (see paragraph 0041, lines 8-20 and paragraph 0042, lines 10-19).
As to claim 6
Martinez discloses the air-conditioning controller according to claim 1, wherein
the trained model is generated by the machine learning based on training data based on the temperature data from each of the plurality of temperature sensors and label data based on room temperature data (see paragraph 050, lines 1-13).
As to claim 7
Martinez discloses the air-conditioning controller according to claim 6, wherein
the training data is moving average data of the temperature data from each of the plurality of temperature sensors (see paragraph 0112, lines 4-8).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael J. Brown whose telephone number is (571)272-5932. The examiner can normally be reached Monday-Thursday from 5:30am-4:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at (571)272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Brown/
Primary Examiner, Art Unit 2115
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