CTNF 18/724,374 CTNF 84375 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wei Yaguang et al. (JP 2021-156565 A) and further in view of Nishimura, Tadafumi et al. (US 2022/0316741 A1) . Consider claims 1 and 16, Wei discloses a learning device that comprises: processor to simulate a thermal environment of an indoor space, Wei teaches, “an air conditioning control system 100 feeds control contents to an air conditioning device 30, and adjusts an environment condition in an object space 81. The air conditioning system 100 includes an acquisition portion 11, and a control content determination portion 13. The acquisition portion 11 acquires a target environment condition as the environment condition to be aimed. The control content determination portion 13 has a learning model for inputting the target environment condition, and outputting the determined control content as the control content to be fed to the air conditioning device 30 to make the object space 81 approach the target environment condition. The learning model is learned while applying the control content for learning as the control content to the air conditioning device 30, and the environment condition for learning as the environment condition in the object space 81, as a data set for learning. Figure 2” See Abstract. the thermal environment is predicted to result from air conditioning of the indoor space by an air conditioner, in a situation in which at least one of a state of a refrigeration cycle included in the air conditioner or a state of the indoor space is given, Wei teaches, “The control content 42 of the air conditioner 30 is output to the output layer OPL of ResNet. A vector having a dimension of (the number of air conditioning control parameters constituting the control content 42) × (the number of air conditioner 30s) is output to the output layer OPL of the CNN. Here, a three-dimensional vector is output to the output layer OPL by 3 (number of air conditioning control parameters) × 1 (number of units). The predicted value of temperature, the predicted value of air volume, and the predicted value of wind direction are output to the three nodes (circles) shown in the output layer OPL of FIG. 10 from above, respectively.” See ¶ 0032 generate a trained model aimed at inferring a control value of the air conditioner from the at least one of the state of the refrigeration cycle or the state of the indoor space, Wei teaches, “The air conditioning control system of the first aspect sends control contents to the air conditioner and adjusts the environmental state in the target space of the air conditioner operation by the air conditioner. The air conditioning control system includes an acquisition unit and a control content determination unit. The acquisition unit acquires the target environmental state, which is the target environmental state. The control content determination unit has a learning model. The learning model inputs the target environment state. The learning model outputs the decision control content, which is the control content to be sent to the air conditioner for bringing the target space closer to the target environmental state. The learning model is learned by using the learning control content, which is the control content for the air conditioner, and the learning environment state, which is the environment state in the target space, as a learning data set. In the air-conditioning control system of the first aspect, the air-conditioning control system determines the decision control content to be sent to the air conditioner in order to bring the target space closer to the target environmental state by the learning model. The learning model is a trained model. Therefore, the air conditioning control system can determine the decision control content for achieving the target environment state in more real time.” See ¶ 0003-0004. Wei fails to discloses performing reinforcement learning that adopts, as a reward, a value based on a temperature environment. Nonetheless, in an analogous art, Nishimura teaches, “an information processing apparatus, based on a data set including a combination of information indicating a situation, information on the comfort of a user, and information on power consumption when an air conditioner has been operated, performs reinforcement learning that uses, as a reward, a value determined based on a first index that increases as the comfort increases and a second index that increases as the power consumption decreases, and executes a process of determining an operation setting according to a situation when the air conditioner is operated, the comfort when the air conditioner is operated, and a condition related to the power consumption when the air conditioner is operated.” See ¶ 0006, can claim 2 . With respect to, wherein the processing circuitry simulates air quality of the indoor space as the thermal environment, and executes the reinforcement learning, Nishimura teaches, “The indoor air cleanliness may be, for example, measured with a sensor of the indoor unit of the air conditioner 20. The indoor air cleanliness may include, for example, information on carbon dioxide concentration, etc. By performing machine learning based on the indoor air cleanliness, for example, it is believed that when a user is likely to fall asleep because of a relatively high concentration of carbon dioxide, the set temperature of heating tolerated by the user can be inferred to be lower for a certain period of time to lower indoor temperature to reduce drowsiness.” See ¶ 0095. generates the trained model aimed at inferring a timing of ventilating the indoor space from the state of the indoor space, Nishimura teaches, “a single information processing apparatus 10 may create a single trained model based on information obtained from each of multiple air conditioners 20 and distribute the created single trained model to the air conditioners 20.” See ¶ 0136. It would have been obvious to one of ordinary skilled in the art at the time of invention (effective filing date for AIA application) to modify the invention of Wei and include reinforcement learning that adopts, as a reward, a value based on a temperature environment, as suggested by Nishimura, in an effort to incentivize the end-user conserve energy. Consider claims 2-5, Wei in view of Nishimura teaches, that a processor uses control content for learning, which is data comprising values such as temperature values which the air conditioning device may take as a setting value, to simulate the temperature environment predicted in a case where, in a situation where the state of the refrigeration cycle applies, the air conditioner conditions the indoor space. See Wee teaches, “the determination control content 42D is predicted by the learning model 43, the two screen displays may be slightly different. It is also necessary to update the environmental state (CFD) 41C of the simulation unit 14 to one corresponding to the determination control content 42D. Therefore, the air conditioning control device 10 performs the environmental state display process based on the determination control content 42D.” Nishimura teaches, “[t]he information processing apparatus 10 may predict future changes in power consumption based on time-series data on outside air temperature or the like obtained from the apparatus 20A, and if a predetermined condition is satisfied, recommend an energy-conserving operation setting to the user A in advance. This, for example, when electric power is expected to be tight on a mid-summer early afternoon or the like, makes it possible to recommend energy conservation during the morning or the like.” See ¶ 0208 It would have been obvious to one of ordinary skilled in the art at the time of invention (effective filing date for AIA application) to modify the invention of Wei by applying the features disclosed in Nishimura in order to use of air conditioner specifications in a simulation. Consider claims 6-9 Wei in view of Nishimura teaches, that the processor uses a simulation model for the temperature distribution in the indoor space, the simulation model being generated on the basis of coordinates of the indoor space, to simulate the temperature environment predicted in a case where, in a situation where the state of the indoor space applies, the air conditioner conditions the indoor space, and to generate an airflow control model for controlling the airflow in the indoor space, wherein the airflow control model is a model for estimating, from the state of the indoor space, a control value for the airflow in the indoor space. See the cited section above . 07-21-aia AIA Claim (s) 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Wei (JP 2021-156565 A), in view of Nishimura (US 2022/0316741 A1), Takehara Tsukasa et al. (JP 2018-71853 A) . Consider claims 10-11, Takehara teaches that processor generating training data representing a target value for the temperature environment is included, in which the reinforcement learning means uses the training data generated by the training data to perform the reinforcement learning, and thus generates the learned model, and in which the training data is data that represents, as the target value, a time series pattern for the temperature preferred by the user (an air conditioner operation by the user is not performed). Takehara teaches, “utilizing a learning function using past data, it becomes possible to automatically control an air conditioning system such that thermal comfort of each user falls within an allowable range” See abstract. Takehara teaches, “the learning information includes integrated information as shown in FIG. In such a case, the control information acquisition unit 232 scans two pieces of continuous integrated information (the integrated information is arranged in time series), the internal information of the two integrated information, the reception internal information, and the acquisition internal The apparatus setting information of the integrated information having the closest distance between the information may be acquired as control information. Here, the device setting information used as control information is device setting information of the integrated information of the previous time of the two pieces of integrated information. Here, when the integrated information of the previous time includes operation information, it is appropriate to use information obtained by overwriting the operation information on the device setting information as control information. In addition, as with the learning unit 12, any distance or similarity may be used for the distance between the two pairs of internal information.” See Embodiment 2. It would have been obvious to one of ordinary skilled in the art at the time of invention (effective filing date for AIA application) to modify the combination of Wei- Nishimura and include and generates the learned model, and in which the training data is data that represents, as the target value, a time series pattern for the temperature preferred by the user, as suggested by Takehara , in an effort to incentivize the end-user conserve energy. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Omer S. Khan whose telephone number is (571)270-5146. The examiner can normally be reached 10:00 am to 8:00 pm EST. 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 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, Brian A. Zimmerman can be reached at 571-272-3059. 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. /Omer S Khan/Primary Examiner, Art Unit 2686 Application/Control Number: 18/724,374 Page 2 Art Unit: 2686 Application/Control Number: 18/724,374 Page 3 Art Unit: 2686 Application/Control Number: 18/724,374 Page 4 Art Unit: 2686 Application/Control Number: 18/724,374 Page 5 Art Unit: 2686 Application/Control Number: 18/724,374 Page 6 Art Unit: 2686 Application/Control Number: 18/724,374 Page 7 Art Unit: 2686 Application/Control Number: 18/724,374 Page 8 Art Unit: 2686 Application/Control Number: 18/724,374 Page 9 Art Unit: 2686
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