DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to pending claims 1-25 filed 4/3/2024.
Claim Objections
The following claim(s) are objected to for formality issues:
Claim 7 is assumed to dependent on claim 6, not 4, because of reference to “the other output confidence”.
Appropriate correction(s) are required.
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.
Claim(s) 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The 35 U.S.C. 101 subject matter eligibility analysis first asks whether the claim is directed to one of the four statutory categories (Step 1). It next asks whether the claim is directed to an abstract idea (Step 2A), via Prong 1, whether an abstract idea (e.g., mathematical concept, mental process, certain methods of organizing human activity) is recited, and Prong 2, whether it is integrated into a practical application. It finally asks whether the claim as a whole includes additional elements that amount to significantly more than the judicial exception (Step 2B). See MPEP 2106.
STEP 1: The claims falls within one of the four statutory categories:
All claims are directed to methods, hardware apparatuses, and computer-readable media and hence fall within one of the four statutory categories.
STEP 2A PRONG 1: The claims recite a judicial exception: Claim 1 recites receiving input, monitoring power metrics, and determining storage or noting of a value. These are processes of observation and judgment, as may be performed by a researcher monitoring performance of a neural network, and may be performed in the mind.
Additional elements are underlined in the analysis below. In particular:
For claim 1: a method comprising:
receiving one or more inputs for a neural network (Receiving data for passing to a neural network may be performed mentally);
monitoring a power utilization metric during processing of the one or more inputs by the neural network (monitoring a metric during processing may be performed mentally);
determining, responsive to the power utilization metric exceeding a first threshold, whether an output confidence at a pre-output layer of the neural network exceeds a second threshold (monitoring performance, confidence, etc. of a neural network is observation and may be performed mentally); and
storing, responsive to the output confidence exceeding the second threshold, an entry in a memory lookup table comprising an output of the neural network (Storing values in tables in response to observed conditions may be performed mentally).
For claim 2: The method of claim 1, wherein the power utilization metric comprises an amount of power consumed (Considering amount data may be performed mentally).
For claim 3. The method of claim 1, wherein the power utilization metric comprises a number of activated neurons of the neural network (Considering amount data may be performed mentally).
For claim 4: The method of claim 1, wherein monitoring the power utilization metric comprises estimating an amount of power consumed based on a number of activated neurons of the neural network (Considering amount data, estimating amount data may be performed mentally).
For claim 5: The method of claim 1, wherein the entry further comprises an activation map signature (Observing and recording activation map signature may be performed mentally).
For claim 6. The method of claim 1, further comprising determining whether to store the entry in the memory lookup table based on a comparison between the output confidence at the pre-output layer of the neural network and another output confidence at an output layer of the neural network (Recording data based on observation and judgment may be performed mentally).
For claim 7. The method of claim 4, wherein determining whether to store the entry in the memory lookup table comprises comparing a difference between the other output confidence and the output confidence to a difference threshold (Recording data based on observation and judgment including comparisons may be performed mentally).
For claim 9. The method of claim 8, further comprising:
determining, during processing of the other one or more inputs by the neural network, that another power utilization metric exceeds a third threshold (Observation and comparison may be performed mentally); and
responsive to the other power utilization metric exceeding the third threshold, providing an intermediary output of the neural network instead of processing the other one or more inputs through all layers of the neural network (Providing outputs in response to observation and judgment may be performed mentally).
The remaining claims 10-18, 19-25 recite apparatuses and computer readable media corresponding to the above methods and hence are analyzed similarly.
STEP 2A PRONG 2: The claims do not integrate the exception into a practical application:
Claim 10 further recites: an apparatus comprising:
a processing device; and
memory operatively coupled to the processing device, wherein the memory stores computer program instructions that, when executed, cause the processing device to. However, this is mere instructions to implement the abstract idea on a computer including processor, memory, and stored instructions and hence does not constitute an integration into a practical application.
Claim 19 further recites: a computer program product comprising a computer readable storage medium, wherein the computer readable storage medium comprises computer program instructions. However, this is mere instructions to implement the abstract idea on a computer including processor, memory, and stored instructions and hence does not constitute an integration into a practical application.
STEP 2B: The claim as a whole do not include additional elements that amount to significantly more than the abstract idea:
Claim 10 further recites: an apparatus comprising:
a processing device; and
memory operatively coupled to the processing device, wherein the memory stores computer program instructions that, when executed, cause the processing device to. However, the use of general purpose computer hardware, including processors, memory, etc. to implement an abstract idea is well-understood, routine, and conventional in the field of data processing and hence does not constitute significantly more.
Claim 19 further recites: a computer program product comprising a computer readable storage medium, wherein the computer readable storage medium comprises computer program instructions. However, the use of general purpose computer hardware, including processors, memory, etc. to implement an abstract idea is well-understood, routine, and conventional in the field of data processing and hence does not constitute significantly more.
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) 1-5, 8-14, 17-23 are rejected under 35 U.S.C. 103 as being unpatentable over Keramidas (US 12554313 B2) in view of Hood (US 20220237116 A1).
For claim 1, Keramidas discloses: a method comprising:
receiving one or more inputs for a neural network (fig.1:105, c.3:55-c.4:15);
monitoring a power utilization metric during processing of the one or more inputs by the neural network (fig.2, c.5¶4-c.6¶3 gives overview of caching technique, with c.6¶2-3 contemplating storage criteria including power consumption comparison calculation);
determining, responsive to the power utilization metric exceeding a first threshold (ibid: determining if power use of the operations would exceed threshold of cache read, see also c.8¶3 (fig.4)); and
storing, responsive to the output confidence exceeding a threshold, an entry in a memory lookup table comprising an output of the neural network (ibid: storing output of neural net node in cache).
Keramidas does not disclose: wherein the determining is whether an output confidence at a pre-output layer of the neural network exceeds a second threshold; wherein the threshold is the second threshold.
Hood discloses: wherein the determining is whether an output confidence at a pre-output layer of the neural network exceeds a second threshold (fig.8, 0163-164: determining uncertainty metrics indicating confidence at a particular node within range threshold); wherein the threshold is the second threshold (ibid: storage determination is determined according to second, additional threshold threshold).
It would have been obvious before the effective filing date to one of ordinary skill in the art to modify the method of Keramidas by incorporating the additional threshold determinations of Hood. Both concern the art of neural network caching, and the incorporation would have, according to Hood, allow better maintaining of cache based on criteria (0162-163).
For claim 2, Keramidas modified Hood discloses the method of claim 1, as described above. Keramidas modified by Hood further discloses: wherein the power utilization metric comprises an amount of power consumed (Keramidas c.6¶2).
For claim 3, Keramidas modified Hood discloses the method of claim 1, as described above. Keramidas modified by Hood further discloses: wherein the power utilization metric comprises a number of activated neurons of the neural network (Keramidas c.6¶2: power consumption of a single neuron of the neural network; c.8¶2: plurality of nodes, such as on a layer or multiple layers).
For claim 4, Keramidas modified Hood discloses the method of claim 1, as described above. Keramidas modified by Hood further discloses: wherein monitoring the power utilization metric comprises estimating an amount of power consumed based on a number of activated neurons of the neural network (Keramidas c.6¶2 power consumed based on a single activation neuron, c.8¶2: plurality of nodes distributed).
For claim 5, Keramidas modified Hood discloses the method of claim 1, as described above. Keramidas modified by Hood further discloses: wherein the entry further comprises an activation map signature (Keramidas c.6¶2, c.8¶2 power consumed based on activation pattern signature comprising neurons under consideration, hence, entry comprising the activation of the neuron).
For claim 8, Keramidas modified Hood discloses the method of claim 1, as described above. Keramidas modified by Hood further discloses: receiving another one or more inputs for the neural network (fig.1, c.3 last ¶, c.5¶1, c.8¶2: receiving signals during inference);
determining, during processing of the other one or more inputs by the neural network, whether an entry matching an activation map signature for processing the other one or more inputs is stored in the memory lookup table (ibid: determining cache hits during inference); and
responsive to the entry being stored in the memory lookup table, providing a stored output of the entry instead of processing the other one or more inputs through all layers of the neural network (ibid: cache looks ups are provided rather than routing through respective neural network layers).
For claim 9, Keramidas modified Hood discloses the method of claim 8, as described above. Keramidas modified by Hood further discloses: determining, during processing of the other one or more inputs by the neural network, that another power utilization metric exceeds a third threshold (fig.2, c.5¶4-c.6¶3: as described above, cache policy is determined for additional, later layers); and
responsive to the other power utilization metric exceeding the third threshold, providing an intermediary output of the neural network instead of processing the other one or more inputs through all layers of the neural network (fig.1, c.3 last ¶, c.5¶1, c.8¶2: cached results are provided for later layers based on cache hits rather than full propagation through the neural).
Claim 10 recites an apparatus corresponding to the method of claim 1 and is hence rejected for the same reasons. Keramidas further discloses: an apparatus comprising:
a processing device (fig.3:330); and
memory operatively coupled to the processing device, wherein the memory stores computer program instructions that, when executed, cause the processing device to (fig.3:320, 340) perform the claimed steps.
Claim 19 recites an apparatus corresponding to the method of claim 1 and is hence rejected for the same reasons. Keramidas further discloses: a computer program product comprising a computer readable storage medium, wherein the computer readable storage medium comprises computer program instructions that, when executed perform the claimed steps (fig.3: 310-340, c.7¶1-2).
Claims 11-14, 17-23 recite apparatuses and computer readable media corresponding to the above claims and are hence likewise rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Vasileiadis (US 20220291964 A1) discloses workflow caching.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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/LIANG LI/
Primary examiner AU 2143
