DETAILED ACTION
This Office action is in reply to application No. 18/635,706, filed 15 April 2024, with a preliminary amendment filed concurrently. Claim 8 has been cancelled. Claims 1-7, 9 and 10 are pending and are considered below.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7, 9 and 10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. In the independent claims, there is insufficient antecedent basis for “the evolution” and “the k known time series data”. Further in regard to claim 7, the term “similar” is a term of degree which renders the claim further indefinite; reasonable people could reasonably disagree as to whether two given thermal signatures are “similar”, and nothing in the specification or drawings provides any clarification.
Further in regard to claim 4, “an extended period of time” is indefinite because it is an open comparative; i.e., extended as compared to what? Further in regard to claim 6, there is insufficient antecedent basis for “the time domain signals”, “the frequency components”, and “the features”, and the phrase “less distant” is an open comparative.
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 1-7, 8 and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims lie within statutory categories of invention, as each is directed to a method (process), non-transitory computer-readable medium (manufacture) or computer device (machine). The claim(s) recite(s) data gathering (obtaining time series data related to power consumption or production), making a comparison of the data to previously-obtained data, and training a model in no particular manner but merely for a particular purpose, and adjusting the model.
First, managing information about power consumption or production is a fundamental business practice; businesses have been doing this for many decades before there was any such thing as a computer. Second, aside from the model-training steps (discussed in more detail below), these are steps which can be performed mentally or with pen and paper records.
This judicial exception is not integrated into a practical application because aside from the bare inclusion of a generic computer and nondescript use of machine learning, discussed below, nothing is done beyond what was set forth above, which does not go beyond generally linking the abstract idea to the technological environment of generic, AI-enabled, networked computers. See MPEP § 2106.05(h).
As the claims only manipulate data concerning power production or consumption, they do not improve the “functioning of a computer” or of “any other technology or technical field”. See MPEP § 2106.05(a). They do not apply the abstract idea “with, or by use of a particular machine”, MPEP § 2106.05(b), as the below-cited Guidance is clear that a generic computer is not the particular machine envisioned.
They do not effect a “transformation or reduction of a particular article to a different state or thing”, MPEP § 2106.05(c). First, such data, being intangible, are not a particular article at all. Second, the claimed manipulation is neither transformative nor reductive; as the courts have pointed out, in the end, data are still data.
They do not apply the abstract idea “in some other meaningful way beyond generally linking [it] to a particular technological environment”, MPEP § 2106.05(e), as the lack of technical and algorithmic detail in the claims is so as not to go beyond such a general linkage.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional claim limitations, considered individually and as an ordered combination, are insufficient to elevate an otherwise-ineligible claim. Claim 10, which has the most, includes a processor and memory storing instructions. These elements are recited at a high degree of generality and the specification does not meaningfully limit them, such that a generic computer will suffice.
It only performs generic computer functions of nondescriptly manipulating data and sharing data with persons and/or other devices. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea.
The type of information being manipulated does not impose meaningful limitations or render the idea less abstract. In light of Recentive1, merely using known machine learning techniques where the only purported improvement is in the type of data being manipulated is insufficient.
That the claimed use of machine learning, of which the most specific feature is fine tuning a model, was well-understood, routine and conventional at the time of filing can be shown by, for example, Mandal et al. (U.S. Publication No. 2022/0374810) that uses models by the use of “any machine learning approach such as KNN (K Nearest Neighbor”, [0068] among others. It was then “well known” that “calibration of the accuracy of models” “typically entails the tuning of the weights” of known models. [0094; emphasis added]
The claim elements when considered in ordered combination – a generic computer performing a chronological sequence of abstract steps while using well-understood, routine and conventional techniques – does nothing more than when they are analyzed individually. The other independent claims are simply different embodiments but are likewise directed to a generic computer performing the same process.
The dependent claims further do not amount to significantly more than the abstract idea: claim 2 simply gives a well-known example of a type of model; claims 3 and 7 are simply further descriptive of the type of information being manipulated; claim 4i simply recites a timing parameter; claims 5 and 6 simply recite additional, abstract manipulation of information.
The claims are not patent eligible. For further guidance please see MPEP § 2106.03 – 2106.07(c) (formerly referred to as the “2019 Revised Patent Subject Matter Eligibility Guidance”, 84 Fed. Reg. 50, 55 (7 January 2019)).
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.
Claim(s) 1, 4, 7, 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Guha et al. (U.S. Publication No. 2015/0186904) in view of Togawa (U.S. Publication No. 2022/0253461).
In-line citations are to Guha. Claims are examined as best understood.
With regard to Claim 1:
Guha teaches: A method for training at least one model [0025; a model is trained] able to predict a power consumption or production of at least one electric equipment, [id.; it can “derive the power generated by [] solar patterns or wind turbines”] also called target, said method comprising the following steps:
(a) obtaining time series data representing the evolution of the power consumption or production of said target over a first period of time… [0044; the modeling may use “time series forecast with moving averages” in order to “fine-tune” the models; it necessarily obtained the data in order to use it]
(c) training of a first prediction model or backbone for each of said k known electric equipments, [0025 as cited above] said backbone being able to predict the evolution over time of the consumption or the production of the corresponding known electric equipment, said backbone being trained on the corresponding time series data over the second period of time,
(d) training at least one second prediction model, called target model, by fine tuning at least one of the first trained prediction model on the time series data of said target over the first period of time. [0044 as cited above]
Guha does not explicitly teach comparing said target time series data to known time series data representing the evolution, over a second period of time, of the power consumption or production of known electric equipments, said second period of time being greater than the first period of time, to determine the k known time series data that are the most similar to the target time series data, but it is known in the art. Togawa teaches a time-series data processing method [title] that can determine “power consumption” at a “processing facility” related to a “target”. [0027] It compares “time series data sets with each other” and classifies them, determining a “similarity degree”. [0039] Measurements are taken at “predetermined time intervals”, [0030] some of which are shorter than others. [abstract] The purpose is to make predictions. [0031] Togawa and Guha are analogous art as each is directed to electronic means for managing power consumption data.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Togawa with that of Guha in order to improve accuracy, as taught by Togawa; [0006] further, it is simply a substitution of one known part for another with predictable results, simply making a prediction on the basis of Togawa’s comparison instead of, or in addition to, on the basis of Guha’s data; the substitution produces no new and unexpected result.
In this and the subsequent claims, as a backbone is only optionally trained, the purpose of the backbone (“said backbone being able to predict the evolution over time of the consumption or the production of the corresponding known electric equipment, said backbone being trained on the corresponding time series data over the second period of time”) refers to an optional step which is considered but given no patentable weight. Optional steps do not distinguish over the art as they can always be omitted. See MPEP § 2111.04. Further, it is not positively claimed that the backbone performs any steps, but only that it is capable of doing so (the predicting) or has done so in the past (the training). That data are “representing the evolution, over a second period of time, of the power consumption or production of known electric equipments, said second period of time being greater than the first period of time” consists entirely of nonfunctional, descriptive language, disclosing at most human interpretation of data but which imparts neither structure nor functionality to any claimed embodiment and so is considered but given no patentable weight.
With regard to Claim 4:
The method according to claim 1, wherein the target model is re-trained at successive time interval, on the time series data of said target over an extended first period of time. [Guha, 0044 as cited above in regard to claim 1]
With regard to Claim 7:
The method according to claim 1, wherein step (b) comprises a selection of the time series data of known electric equipments having a thermal signature similar to the thermal signature of the target.
This claim is not patentably distinct from claim 1, which already has (and therefore must have chosen to use) time series data of known equipment. That the equipment has a “thermal signature similar to the thermal signature of the target”, in addition to being indefinite, purports to limit the known equipment and not the claimed method.
With regard to Claim 9:
Guha teaches: A non-transitory computer-readable recording medium on which is recorded a program for implementing the method according to claim 1, when said program is executed by a processor. [0047; a “machine-readable medium” which is, 0048, a “recordable medium”; 0049; a “processor device” implements, 0044, “software” which may be stored on the medium]
With regard to Claim 10:
Guha teaches: A computer device comprising:
- an input interface configured to receive at least one input time series signal,
- a memory configured to store at least instructions of a computer program,
- a processor configured to access the memory to read the instructions which when executed by the processor cause the method according to claim 1 to be performed, and
– an output interface configured to provide the trained target model. [0025; the system receives inputs and provides outputs; 0047; a “machine-readable medium” which is, 0048, a “recordable medium”; 0049; a “processor device” implements, 0044, “software” which may be stored on the medium]
The content of output which is merely transmitted or displayed and then not further processed such as that output comprises “the trained target model”, consists entirely of nonfunctional printed matter which bears no functional relation to the claimed substrate and so is considered but given no patentable weight.
Claim(s) 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Guha et al. in view of Togawa further in view of Sarwat et al. (U.S. Patent No. 10,958,211).
With regard to Claim 2:
The method according to claim 1, wherein each first prediction model and second prediction model is a recurrent neural network, for example a LSTM.
Guha and Togawa teach the method of claim 1 but do not explicitly teach a neural network, but it is known in the art. Sarwat teaches a power management system [title] in which “neural networks” such as “LSTM-CNN” are used. [Col. 19, lines 39-41] It is used to estimate power consumption patterns and uses ambient temperature, [Col. 34, lines 46-47, 55] and solves the problem of prior art systems not accounting for the “impacts of seasonal variations”. [Col. 8, lines 56-57] Sarwat and Guha are analogous art as each is directed to electronic means for managing power related information.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Sarwat with that of Guha and Togawa in order to improve estimation accuracy, as taught by Sarwat; [Col. 15, lines 46-47] further, it is simply a substitution of known parts for others with predictable results, simply using Sarwat’s neural network in place of, or in addition to, that of Guha, and using Sarwat’s interpretation of data rather than, or in addition to, that of Guha; the substitution produces no new and unexpected result.
The phrase “for example a LSTM” is non-limiting and so is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.
With regard to Claim 3:
The method according to claim 1, wherein each time series data comprises successive values each associated to a specific time stamp, each value being a tensor comprising dimensions or features representing respectively:
- the power consumption or production of said target,
- the outside temperature,
- the seasonality of the corresponding power consumption or production. [Sarwat, as cited above in regard to claim 2]
This claim is not patentably distinct from claim 1 as it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretation of data but which imparts neither structure nor functionality to the claimed method. The reference is provided for the purpose of compact prosecution.
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Guha et al. in view of Togawa further in view of Meng et al. (U.S. Publication No. 2022/0327408).
With regard to Claim 5:
The method according to claim 1, wherein, during training of the backbone and/or during training of the target model, sequences are extracted from the corresponding time series data and are associated by pairs, each pair comprising an input sequence, used as an input data from which to determine a prediction, and a target segment forming a ground-truth prediction to be found by the model on the basis of the first segment, said segment being located temporally after the first segment in the corresponding time series data.
Guha and Togawa teach the method of claim 1, including the use of time series data and making predictions as cited above, but do not explicitly teach a ground truth, but it is known in the art. Meng teaches a machine-learning system [title] that performs “feature extraction”. [0077] It is used to “generate a predicted electricity consumption report”. [0007] It performs “time series forecasting” to make predictions and provides the “ground-truth of such predictions”. [0100] Meng and Guha are analogous art as each is directed to electronic means for using time series data to make predictions related to the production or use of power.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Meng with that of Guha and Togawa in order to reduce inaccuracy, as taught by Meng; [0003] further, it is simply a substitution of one known part for another with predictable results, simply providing a prediction in the manner of Meng instead of, or in addition to, that of Guha; the substitution produces no new and unexpected result.
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Guha et al. in view of Togawa further in view of Wu et al. (U.S. Publication No. 2020/0191943).
With regard to Claim 6:
The method according to claim 1, wherein step (b) comprises the following sub-steps:
(b1) converting the time domain signals of the time series data into frequency domain,
(b2) analyzing the frequency components of the signals to identify the frequency bands or peaks that are most important in characterizing the signals,
(b3) extract the features that capture the frequency signature of the signals,
(b4) measure the distance between the frequency signatures of the signals,
(b5) determine the k signals that are the less distant from the target time series data.
Guha and Togawa teach the method of claim 1, but do not explicitly teach time-series to frequency conversion, but it is known in the art. Wu teaches an object tracking system [title] that can “reduce power consumption” of a device. [0081] It measures a distance between two vectors. [0099] It may perform a “frequency transform” on “time series” data. [0137] It may identify “significant local peaks”, [0145] and may select certain of these based on a selection criterion. [0146] It may identify signals with a “minimum distance” which is “smaller than a threshold”. [0204] It uses “machine learning”. [0104] Wu and Guha are analogous as each is directed to the use of machine learning and time series data to manage power-related information.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Wu with that of Guha and Togawa in order to reduce power consumption, as taught by Wu; further, it is simply a substitution of one known part with predictable results, simply making comparisons using the data of Wu rather than, or in addition to, that of Guha; the substitution produces no new and unexpected result.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT C ANDERSON whose telephone number is (571)270-7442. The examiner can normally be reached M-F 9:00 to 5:30.
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, Bennett Sigmond can be reached at (303) 297-4411. 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.
/SCOTT C ANDERSON/Primary Examiner, Art Unit 3694
1 Recentive Analytics, Inc. v. Fox Corp. et al., 134 F.4th 1205, 1216 (Fed. Cir. 2025)
Prosecution Insights