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 .
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, 16-23 and 26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 16, 16-23 and 26 recites “determining, selecting, processing..”. These claimed steps are collecting and analyzing information which are similar to the concepts identified by the courts as abstract ideas, such as collecting information, analyzing it, and displaying certain results of the collection and analysis (Elec. Power Grp., LLL v. Alstom S.A, 119 USPQ2d 1739 (Fed. Cir. 2016)).
The claim does not include significant element that are sufficient to amount to significantly more than the judicial exception.
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 24-25 and 27-29 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites series of steps of “determining, selecting, processing” related to concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
The claim recites determining, according to a given negative or positive metric…., selecting for processing the input data…, processing the input data…. The limitation of “determining, selecting, processing” are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a computer system, neural network, a vehicle, driving assistance system, a robot or a system for medical imaging” nothing in the claim element precludes the step from practically being performed in the mind.
For example, but for the “by a computer system or neural network” language, “determining, selecting, processing” in the context of this claim encompasses the user manually calculating or processing the amount of use of each data, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components.
For example, but for the “by the computer system or neural network” language, “determining, selecting, processing” in the context of this claim encompasses the user thinking that the most important input data should be used in resulting an output data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – using computer system, neural network, a vehicle, driving assistance system, a robot or a system for medical imaging to perform the determining, selecting, processing steps. The “computer system or neural network” in steps is recited at a high-level of generality (i.e., as a generic “system/machine” performing a generic computer function of determining, selecting, processing based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “computer system, neural network, a vehicle, driving assistance system, a robot or a system for medical imaging” to perform both the determining, selecting, processing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
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 16-29 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Abeloe US 2022/00/3793.
In regarding to claim 16 Abeloe teaches:
16. A method for processing input data x with a neural network that includes a set N of neurons, the method comprising the following steps: determining, according to a given negative metric, a subset P⊂N of neurons whose use can be omitted without unduly impairing a performance of the neural network;
Abeloe, 0125-0126
determining, according to a given positive metric, a subset A⊂(N\P) of those neurons that significantly contribute to processing of the input data x is determined from the subset N\P;
Abeloe, 0125-0126
selecting, for processing the input data x, a subset D⊂N of neurons, which is a superset D⊇A of the subset A;
Abeloe, 0125-0126
processing the input data x into output data y using neurons of the superset D.
Abeloe, 0125-0126
In regarding to claim 17 Abeloe teaches:
17. The method according to claim 16, wherein the neurons of the superset D are implemented on a hardware platform whose resources are insufficient for an implementation of all of the neurons of the set N of neurons.
Abeloe, 0125-0126
In regarding to claim 18 Abeloe teaches:
18. The method according to claim 16, wherein one or more neurons from a subset (N\P)\A whose use was favored by the negative metric but not by the positive metric, are selected for additional inclusion in the superset D.
Abeloe, 0125-0126
In regarding to claim 19 Abeloe teaches:
19. The method according to claim 18, wherein a number of the one or more neurons to be included in the superset D is determined based on a given budget of computing capacity.
Abeloe, 0125-0126
In regarding to claim 20 Abeloe teaches:
20. The method according to claim 19, wherein the given budget of computing capacity is established as a total number |D| of neurons in the superset D.
Abeloe, 0125-0126
In regarding to claim 21 Abeloe teaches:
21. The method according to claim 18, wherein the neurons from the subset (N\P)\A are selected in descending order of importance for processing the input data x.
Abeloe, 0125-0126
In regarding to claim 22 Abeloe teaches:
22. The method according to claim 21, wherein the order of importance is established based on a value determined by the positive metric for the neurons from the subset (N\P)\A.
Abeloe, 0125-0126
In regarding to claim 23 Abeloe teaches:
23. The method according to claim 16, wherein the negative metric evaluates the neurons of the set N of neurons independently of the input data x.
Abeloe, 0125-0126
In regarding to claim 24 Abeloe teaches:
24. The method according to claim 16, wherein: a neural network is selected in which inputs that are supplied to each neuron are aggregated by forming a weighted sum to activate the neuron, and the negative metric evaluates the neurons at least based on the weights in the weighted sum.
Abeloe, 0125-0126
In regarding to claim 25 Abeloe teaches:
25. The method according to claim 16, wherein the positive metric: maps the input data x to a hash value H(x) with reduced dimensionality, ascertains a hash value h* most similar to H(x) from a given look-up table in which hash values h are stored in association with information about participation of neurons, and uses the information stored in the look-up table in association with the hash value h* to evaluate neurons from the subset in N\P.
Abeloe, 0125-0126
In regarding to claim 26 Abeloe teaches:
26. The method according to claim 16, wherein a preselection of those neurons which significantly contribute to the processing of the specific input data x is made based on values of the positive metric for a plurality of input data {tilde over (x)} from a domain and/or distribution X to which the input data x also belong.
Abeloe, 0125-0126
In regarding to claim 27 Abeloe teaches:
27. The method according to claim 16, further comprising: determining a control signal from the output data y; and controlling, using the control signal: (i) a vehicle, and/or (ii) a driving assistance system, and/or (iii) a robot, and/or (iv) a system for quality control, and/or (v) a system for monitoring areas, and/or (vi) a system for medical imaging.
Abeloe, 0125-0126
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL T TEKLE whose telephone number is (571)270-1117. The examiner can normally be reached Monday-Friday 8:00-4:30 ET.
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/DANIEL T TEKLE/Primary Examiner, Art Unit 2481
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