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 .
DETAILED ACTION
Claims 1-14 are pending. Claims 1-14 have been examined and rejected.
Claim Rejections - 35 USC § 102
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.
Claims 1-2, 5-8, and 10-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al. (CN113743603).
Examiner’s Notes: Zhang teaches the first data sample set being actual measurement value and second data sample set being simulation value determined by the simulation model, so the named order as taught by Zhang is reverse of that in the claim. Throughout the below rejection, the Examiner will change and write accordingly without complex explanation.
As per claim 1, Zhang teaches an information processing device comprising:
one or more processors configured to: (p. 5 ¶ 9-11)
perform, by using a model for performing a simulation of operations of a plurality of electronic devices, the simulation, and output a plurality of pieces of first data representing outputs by the plurality of electronic devices (p. 5 ¶ 2 from the bottom, p. 8 ¶ 1-2 from the bottom, p. 9 ¶ 1-2; Zhang teaches performing simulation on a simulation model comprising of a plurality of electronic device such as sensors, controller; Zhang teaches the first data sample set being to actual measurement value and second data sample set being simulation value determined by the simulation model, so the named order as taught by Zhang is opposite to that in the claim); and
estimate, based on the first data and a plurality of pieces of second data representing outputs obtained by operating the plurality of electronic devices, mapping data representing a correspondence between the plurality of electronic devices that output the first data and the plurality of electronic devices that output the second data (p. 8 ¶ 1 from the bottom, p. 9 ¶ 1-2; Zhang teaches a second data sample set being to actual measurement value of the plurality of electronic devices; Zhang, on p. 9 last paragraph, p. 10 ¶ 1-4; Zhang teaches determining the second data sample set according, corresponding to the simulation outputs, to the first data sample set, corresponding to the actual measured outputs, and the preset simulation model to determine a set of training data sample; these teachings in combination under BRI read onto the estimate … mapping data as recited in this limitation).
As per claim 2, Zhang teaches the device according to claim 1, wherein the one or more processors are configured to estimate the mapping data based on the first data to which first metadata is given and the second data to which second metadata is given (p. 10 last 3 paragraphs; Zhang teaches selecting a group of data, comprising actual data and simulation data, as a tract, time sequence state action sequence (s1, a1, s2, a2, …), either time or state action of the simulation data and actual data can be interpreted as first meta data given to the first data and the second data given to the second data as recited, and Zhang’s teaching reads onto this claim).
As per claim 5, Zhang teaches the device according to claim 1, wherein the one or more processors are configured to determine a condition of the simulation based on mapping data obtained in advance (p. 8 ¶ 2 from the bottom; Zhang teaches obtaining physical state of the device to be controlled and inputting the detected state parameter into the preset model for simulation; the physical state of the device is a condition of the simulation, and the obtained physical state is mapping data obtained in advance).
As per claim 6, Zhang teaches the device according to claim 1, wherein the one or more processors are configured to determine a condition of the simulation based on the second data obtained in advance (p. 8 ¶ 2 from the bottom; Zhang teaches obtaining physical state of the device to be controlled and inputting the detected state parameter into the preset model for simulation; the physical state of the device is a condition of the simulation, and the physical state is part of the second data obtained in advance).
As per claim 7, the device according to claim 1, wherein the one or more processors are configured to:
determine whether or not estimation of the mapping data is ended (p. 10 last paragraph; Zhang teaches determining if the search for the next action and time should continue or end based on obtained the reward and state of the safety score; this teaching reads onto this limitation); and
when the estimation of the mapping data is not ended, determine a condition of the simulation and further perform the simulation according to the determined condition (p. 8 ¶ 2 from the bottom, p. 10 last paragraph; on p. 8 ¶ 2 from the bottom; Zhang teaches obtaining physical state of the device to be controlled and inputting the detected state parameter into the preset model for simulation; the physical state of the device is a condition of the simulation; Zhang teaches selecting a group of data as a track of the starting point, …, re-inputting the simulation model to obtain results; if results are reliable continue with the next search, which means repeating the step of simulation according to the determined condition; checking if results are reliable or not corresponds to the estimation of the mapping data is ended or not).
As per claim 8, Zhang teaches the device according to claim 1, wherein the one or more processors are further configured to:
calculate, by using a learning model learned to output a matching degree between the second data and the first data, the matching degree, and estimate the mapping data by using the calculated matching degree (p. 11 ¶ 1; Zhang teaches using real offline data training joint condition probability distribution model maybe according to the first data sample set, corresponding to the measured second data as claimed, and the preset simulation model to determine the second data sample set, corresponding to the simulation first data set as claimed; this teaching of performing joint condition probability between the two the claimed second data and first data to determine the data sample set reads onto this limitation);
output the estimated mapping data (p. 11 ¶ 1; Zhang teaches determining the data sample set from the calculation as discussed in the above limitation; the determined data sample set is interpreted as the estimated mapping data output); and
learn the learning model by using correct answer data of the mapping data set by referring to the outputted mapping data (p. 11 ¶ 1; Zhang teaches using data training for the joint condition probability distribution model, which is a deep neural network model, learning model; this teaching means the learning model learns using the correct answer data of the mapping data set by referring to the outputted mapping data).
As per claim 10, Zhang teaches the device according to claim 1, wherein the model is a physical model for performing the simulation (p. 8 ¶ 2 from the bottom, p. 9 ¶ 1; Zhang teaches performing obtaining physical state of the device to be controlled and inputting the detected state parameter into the preset model for simulation; the state parameter is used for characterizing the physical state of an automobile, for example, current vehicle speed; hence, the preset model for simulation as taught is as physical model).
As per claim 11, Zhang teaches the device according to claim 1, wherein the one or more processors are configured to perform the simulation and output the plurality of pieces of first data representing the outputs by the plurality of electronic devices (p. 8 ¶ 1-2 from the bottom, p. 9 ¶ 1-2; Zhang teaches performing simulation on a simulation model comprising of a plurality of electronic device such as sensors, controller, to produce simulation value).
As per claim 12, an information processing method performed by an information processing device, the method comprising:
performing, by using a model for performing a simulation of operations of a plurality of electronic devices, the simulation, and outputting a plurality of pieces of first data representing outputs by the plurality of electronic devices; and
estimating, based on the first data and a plurality of pieces of second data representing outputs obtained by operating the plurality of electronic devices, mapping data representing a correspondence between the plurality of electronic devices that output the first data and the plurality of electronic devices that output the second data.
As per claim 13, Zhang teaches a computer program product comprising a non-transitory computer-readable medium including programmed instructions, the instructions causing a computer to execute (p. 5 ¶ 9-11):
(these limitations below have already been discussed in claim 1. They are, hence, rejected for the same reasons)
performing, by using a model for performing a simulation of operations of a plurality of electronic devices, the simulation, and outputting a plurality of pieces of first data representing outputs by the plurality of electronic devices, and
estimating, based on the first data and a plurality of pieces of second data representing outputs obtained by operating the plurality of electronic devices, mapping data representing a correspondence between the plurality of electronic devices that output the first data and the plurality of electronic devices that output the second data.
As per claim 14, Zhang teaches an information processing system comprising:
a plurality of electronic devices (p. 8 p. 1; Zhang teaches a plurality of sensors corresponding to a plurality of electronic devices); and
(these limitations below have already been discussed in claim 1. They are, hence, rejected for the same reasons)
an information processing device, the information processing device comprising:
one or more processors configured to:
perform, by using a model for performing a simulation of operations of the plurality of electronic devices, the simulation, and output a plurality of pieces of first data representing outputs by the plurality of electronic devices; and
estimate, based on the first data and a plurality of pieces of second data representing outputs obtained by operating the plurality of electronic devices, mapping data representing a correspondence between the plurality of electronic devices that output the first data and the plurality of electronic devices that output the second data.
Claim Rejections - 35 USC § 103
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.
Claims 3-4 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (CN113743603) as applied to claim 1 above, and further in view of Wang et al. (Automated Point Mapping For Building Control Systems: Recent Advances and Future Research Needs, Automation in Constructions 85, 2018 p. 107-123).
As per claim 3, Zhang teaches the device according to claim 1,
Zhang does not teach:
extract first feature data representing features of the plurality of pieces of first data and second feature data representing features of the plurality of pieces of second data; and
estimate the mapping data based on the first feature data and the second feature data.
However, Wang teaches:
extract first feature data representing features of the plurality of pieces of first data and second feature data representing features of the plurality of pieces of second data; and (p. 113, bullets 1) & 4), p. 116 left col. ¶ 4; Wang extracting statistical features from the time-series of a source building with the ground-truth labeling of all BAS points and from the time-series data of the target building; the ground truth’s series data corresponds to simulated data, and time-series data of the target building corresponds to measured data)
estimate the mapping data based on the first feature data and the second feature data (p. 111 right col. ¶ 3, p. 113 bullets 2) & 4); Wang teaches using data features extracted from the target building as inputs to trained classifier from step 2, bullet 2), to compute for each BAS point according to the similarity between the classifier’s prediction and the BAS point’s local clustering for estimating the mapping BAS point between the two plurality pieces of first and second data).
Zhang and Wang are analogous art because they are in the same field of device control technology field. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Zhang and Wang. One of ordinary skill in the art would have been motivated to make such a combination because Wang’s teachings would have helped automate mapping of points in building control systems and between building control systems (Wang, p. 107 Abstract).
As per claim 4, Zhang and Wang in combination teach the device according to claim 3, Wang further teaches wherein the one or more processors are configured to extract the second feature data by analyzing the second metadata given to the second data (p. 109 bullet 4), p. 113 right col. bullets 1) & 4; Wang teaches extract statistical data features from time-series data of the target building; this teaching reads onto this claim).
As per claim 9, Zhang teaches the device according to claim 1,
Zhang does not teach:
wherein the one or more processors are configured to calculate an error between first correlation data representing a correlation between the plurality of pieces of first data and second correlation data representing a correlation between the plurality of pieces of second data, and estimate the mapping data based on the calculated error.
However, Wang teaches:
to calculate an error between first correlation data representing a correlation between the plurality of pieces of first data and second correlation data representing a correlation between the plurality of pieces of second data, and estimate the mapping data based on the calculated error (p. 113 right col. bullet1) & 4), p. 116 right col. ¶ 4p. 117 left col. ¶ 3; Wang teaches calculating statistical features of time series data for both ground-truth BAS points and the target building for mapping data using classifiers/classification; in addition, Zhang teaches performing classification in combination with calculating correlation of time-series data for identifying relative spatial locations of sensors; as a result, threshold value of the IMF correlation coefficients can be used to correctly determine relationships between sensors in different rooms, and accuracy in determining which specific sensors were in each room cluster was achieved; this teaching reads onto to this limitation).
Zhang and Wang are analogous art because they are in the same field of device control technology field. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Zhang and Wang. One of ordinary skill in the art would have been motivated to make such a combination because Wang’s teachings would have helped automate mapping of points in building control systems and between building control systems (Wang, p. 107 Abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Cuong Van Luu whose telephone number is 571-272-8572. The examiner can normally be reached on Monday - Friday from 8:30 to 5:00.
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/CUONG V LUU/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189