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 .
Response to Arguments
Applicant's arguments filed 1/14/2026 have been fully considered but they are not persuasive.
Applicant argues that the “claims are integrated in a practical application” because there is an image and audio signal from image and audio sensors. Remarks 8. Applicant also argues that the image and audio sensors amount to significantly more than the abstract idea. Id. MPEP 2106.05(d) says that image capture is well-understood, routine and conventional, and therefore doesn’t amount to significantly more than the abstract idea, “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… Electronically scanning or extracting data from a physical document… examples of other types of activity that the courts have found to be well-understood, routine, conventional activity… Recording a customer’s order…” Examiner finds that the image and audio sensors are generic computer parts and they do not integrate the abstract idea into a practical applications, and the sensors do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 16-20, 26, 27, 29 and 36 are rejected under 35 U.S.C. 101 because the claimed invention is a mental concept. The claims recite degrading data and then inferring something about the data. This judicial exception is not integrated into a practical application because the additional elements of a processor, audio sensor, image sensor and a memory are well-understood routine and conventional.1 The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the processor, image and audio sensor and memory are generic computer parts.
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 16-20, 26, 27, 29 and 36 are rejected under 35 U.S.C. 103 as being unpatentable over US20180131190A1 to Murugesan et al (Muru) and US20040223054A1 to Rotholtz.
Muru teaches claims 16 and 26. An apparatus (Muru fig. 3 system 302, figs 4 and 5) comprising:
at least one processor; and (Muru fig. 4 processors)
at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to; (Muru fig. 4 memory)
acquire a data signal (Muru para 44 “The electric production values…”)
degrade an acquired data signal, using a source coding module, to generate a degraded signal having a fidelity dependent on a first measure of available energy, wherein the acquired data signal is degraded based on a scalar dependent on said first measure of available energy; and (Muru para 44 “The electric production values may also be spaced apart by time intervals at the low resolution or the electric production values may be downsampled to the low resolution.” The spaced apart time intervals are the sampling rate. The rate is Applicant’s scalar dependent on the first measured available energy. The electric production values are the available energy.)
generate an output based on the degraded data signal, wherein the output is generated using an inference module that has parameters dependent on a second measure of available energy, wherein the inference module is configured to output degradable inferences dependent on the degraded signal received by the inference module from the source coding module. (Muru para 50 “The machine-learning predictor 414 uses the low resolution consumption data and the model built during the prior training phase to predict the electric energy values.” The predictor is trained, its parameters are dependent on, the training data. The training data is the second measure of available energy, Muru para 55 “electric energy consumption training data and photovoltaic production training data…”)
Muru doesn’t have an audio and image sensor.
However, Rotholtz teaches how to acquire a data signal using at least one of an audio sensor or an image sensor, wherein the acquired data signal comprises at least one of an audio data signal or an image data signal; (Rotholtz para 13 “Captured and compressed images, audio or sensor data are continually recorded into the memory 20…”)
Muru, Rotholtz and the claims are all directed to signal processing. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to apply Muru’s techniques on audio and video data because uncompressed data is costly to store and transfer.
Muru teaches claim 17. An apparatus as claimed in claim 16, wherein the instructions that, when executed by the at least one processor further cause the apparatus at least to:
select the inference module from a plurality of available inference modules dependent on the second measure of available energy. (Muru para 41 builds separate seasonal models that separately mode the data based on the season of the energy consumption, “the training data can be divided into four portions each corresponding to a respective season of the year, and configuring the machine-learning predictor 414 to build the model includes configuring the machine-learning predictor 414 to separately model each of the portions corresponding to the seasons.”)
Muru teaches claims 18 and 27. An apparatus as claimed in claim 16, wherein the instructions that, when executed by the at least one processor further cause the apparatus at least to:
determine the first measure of available energy, wherein the first measure of available energy is a measure of an instantaneous energy supply. (Muru para 56 “The computer system receives a consumption time series of electric consumption values for an electric system…”)
Muru teaches claims 19 and 29. An apparatus as claimed in claim 16, wherein the instructions that, when executed by the at least one processor further cause the apparatus at least to:
determine the second measure of available energy, wherein the second measure of available energy is a forecast of future available energy. (Muru para 55 “The computer system receives electric energy consumption training data and photovoltaic production training data…” The training predictions of production values are the forecast future available energy, and the parameters are based on the training predictions because of error backpropagation in training.)
Muru teaches claim 20. An apparatus as claimed in claim 16, wherein the parameters of the inference module are trained together with the source coding module at a particular measure of available energy. (Examiner interprets this to mean the inference module is trained on the output from the source coding module at a particular measure of available energy. Muru does this with its seasonal training in paragraph 41, “the training data can be divided into four portions each corresponding to a respective season of the year, and configuring the machine-learning predictor 414 to build the model includes configuring the machine-learning predictor 414 to separately model each of the portions corresponding to the seasons.”)
Muru teaches claim 36. A system comprising:
at least one processor; and (Muru fig. 4 processor 402)
at least one memory (Muru fig. 4 memory 404)
storing instructions that, when executed by the at least one processor, cause the apparatus at least to;
train an inference module, for a particular available energy, together with a source coding module; (Examiner interprets this to mean the inference module is trained on the output from the source coding module at a particular measure of available energy. Muru does this with its seasonal training in paragraph 41, “the training data can be divided into four portions each corresponding to a respective season of the year, and configuring the machine-learning predictor 414 to build the model includes configuring the machine-learning predictor 414 to separately model each of the portions corresponding to the seasons.”)
degrade an acquired data signal, using the source coding module, to generate a degraded signal having a fidelity dependent on a first measure of available energy, and (Muru para 44 “The electric production values may also be spaced apart by time intervals at the low resolution or the electric production values may be downsampled to the low resolution.” The spaced apart time intervals are the sampling rate. The rate is Applicant’s scalar dependent on the first measured available energy. The electric production values are the available energy.)
generate by using the inference module an output based on the degraded data signal. (Muru para 50 “The machine-learning predictor 414 uses the low resolution consumption data and the model built during the prior training phase to predict the electric energy values.” The predictor is trained, its parameters are dependent on, the training data. The training data is the second measure of available energy, Muru para 55 “electric energy consumption training data and photovoltaic production training data…”)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AUSTIN HICKS/Primary Examiner, Art Unit 2142
1 MPEP 2106.05(d) “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… Electronically scanning or extracting data from a physical document… examples of other types of activity that the courts have found to be well-understood, routine, conventional activity… Recording a customer’s order…”