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 .
Claims 1, 3-5, 7-8, 10-12, 14-15, 17-18, and 21-27 are pending for examination. Claim 1, 8, and 15 are independent.
Response to Amendment
The office action is responsive to the amendments filed on 07/21/2026. As
directed by the amendments claims 1, 5, 7-8, 12, and 14-15 are amended. Claim 19 is canceled. Claim 27 is new.
Response to Arguments
Applicant's arguments filed 07/21/2026 have been fully considered but they are not fully persuasive.
Applicant arguments regarding 35 U.S.C. § 101:
I. Claims Are Integrated into a Practical Application (Step 2A, Prong Two)
Examiner response: Examiner respectfully disagrees, reducing/pruning the input data for a model is not the same as reducing/pruning the model itself. The specification does not describe compressing the size of a fully trained MLM and instead describes reducing inputs based on their importance values. Similarly, reducing the input to a MLM does not re-structure the MLM itself. Identifying least-important input data values (which is a non-conventional step in optimizing a ML models) and pruning inputs using peaks of an importance distribution graph as reference points is understood as reciting an abstract idea without significantly more. The claims describe reducing input to a machine learning model based on importance ranking, which is directed to an abstract idea without significantly more. An improvement to the abstract idea itself is not considered an improvement in the functioning of a computer or an improvement to any other technology and is not sufficient to integrate the abstract idea into a practical application. The claimed limitations do not provide improvements to the functioning of a computer, or to any other technology or technical field as described in MPEP 2106.05(a).
Applicant further argues:
II. Claims Recite Significantly More Than a Judicial Exception (Step 2B)
Examiner response: Examiner respectfully disagrees that “the claims recite specific computational steps that alter the structure […] of the model itself”. The claims do not provide limitations describing altering structural elements of the MLM. The claims describe reducing input to a machine learning model based on importance ranking, which is directed to an abstract idea without significantly more. Also, the claims reciting non-conventional steps and withdrawal of 35 U.S.C. § 103 does not relate to the analysis performed for 35 U.S.C. § 101. In step 2B the processor and computing device limitations are addressed as generic computer equipment (See MPEP 2106.05(f)). The rejection for claim 1 does not recite claim limitation as being well, understood, routine and conventional activity.
Applicant further argues:
III. Claims Are Not Directed to a Mental Process or an Abstract Idea Without Significantly More
Examiner response: Examiner respectfully disagrees, the claims are not “directed to manipulating complex computer models in ways humans cannot practically perform.” All the limitations under step 2A prong 1 are practically performable in the human mind and are understood to be a recitation of a mental process with pen and paper or mathematical calculations. Example 47 from the “July 2024 Subject Matter Eligibility Examples” also describes generally training and applying a machine learning model (e.g., neural network) and backpropagation. It is unclear how exactly the sequence of operations “structurally alter a trained machine learning model”. Examiner disagrees that reducing the input data is also somehow structurally altering the model. The claimed limitations do not provide improvements to the functioning of a computer, or to any other technology or technical field as described in MPEP 2106.05(a), instead they describe an improvement to the abstract idea itself. Overall, the claim limitations are a combination of mental steps and mathematical calculations under step 2A Prong 1, and additional elements under steps 2A Prong 2 & 2B as detailed in the updated 101 rejection below.
Specification
The disclosure is objected to because of the following informalities: In para 0035 line 4 recites "multimodal Gaussian distribution,.", which ends with a comma and a period.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3-5, 7-8, 10-12, 14-15, 17-18, and 21-27 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “pruning an original machine learning model (MLM) to generate a reduced input MLM” Support for this limitation does not appears in the Speciation, drawings, or claims as originally submitted. Based on Examiner review, the nearest support disclosure for this limitation is found in para 0020:
[Para 0020]: Another benefit of at least one embodiment of the present disclosure, in addition to making an MLM more efficient from a computer resource perspective, is enhancing the accuracy of the MLM by pruning redundant inputs associated with the MLM, e.g. inputs that have a very low importance value (from a weighted perspective in relation to their individual paths) are removed and only higher value/importance inputs are employed.
As Highlighted in the passage above, the specification recites that the inputs to the MLM are pruned and not the model itself. Examiner notes that the specification does not appear to disclose that the MLM itself is pruned rather the specification discloses that the input being applied to the MLM is the one that is pruned/reduced. The specification also does not disclose a pruned MLM that further generates a reduced input.
Claim 1 recites “reducing the number of inputs by pruning one or more data values around one or more peaks of the distribution plot to generate a first reduced input MLM;” Support for this limitation does not appears in the Speciation, drawings, or claims as originally submitted. Based on Examiner review, the nearest support disclosure for this limitation is found in para 0039-0040:
[Para 0039]: Peak A represents the input with the highest importance, with all inputs thereafter having a declining value. In this embodiment, a threshold (not shown) can be set after Peak A, and any inputs after the threshold are automatically excluded from the reduced input MLM.
[Para 0040]: In one or more embodiments, the machine learning efficiency unit 103 can be configured to eliminate a pre-determined number of inputs between gaps and peaks and/or perform a more sophisticated analysis that eliminate inputs based on a particular mathematical computation, e.g., a derivative computation,
As Highlighted in the passage above, the specification recites that the inputs are pruned and not the “data values around one or more peaks”. Examiner notes that the specification appears to describe eliminating inputs based on gaps and peaks and not pruning data values around gaps or peaks.
Independent Claims 8 and 15 recite similar limitations and are also rejected under 112(a) for the same reasons.
Dependent claims 3-5, 7, 10-12, 14, 17-18, and 21-27 do not resolve the 112(a) rejection from independent claims 1, 8, and 15 and are also rejected under 112(a).
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, 3-5, 7-8, 10-12, 14-15, 17-18, and 21-27 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.
Claim 1 recites the limitation "the number of inputs" in line 22. There is insufficient antecedent basis for this limitation in the claim.
Independent Claims 8 and 15 recite similar limitations and are also rejected under 112(b) for the same reasons.
Dependent claims 3-5, 7, 10-12, 14, 17-18, and 21-27 do not resolve the 112(b) rejection from independent claims 1, 8, and 15 and are also rejected under 112(b).
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, 3-5, 7-8, 10-12, 14-15, 17-18, and 21-27 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1, 3-5, 7, 21-22, and 26-27 are directed to a method, claims 8, 10-12, 14, and 23-24 are directed to a non-transitory computer readable medium, and claims 15, 17-18, and 25 are directed to an apparatus. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1:
2A Prong 1:
pruning an original machine learning model (MLM) to generate a reduced input MLM,
determining an importance value of each input of the plurality of inputs associated with the original MLM by applying a second dataset at an output of the original MLM and performing a backpropagation operation with respect to each input of the plurality of input (This step for determining importance is practically performable in the human mind and is understood to be a recitation of a mental process with pen and paper (i.e., evaluation). Performing backpropagation is also understood as performing a mathematical calculation.), the back propagation operation comprising:
summing values of each weight along a path associated with each input, by starting at an output of the original MLM, using the second dataset, and tracing backward to each of the plurality of inputs, wherein an importance value, for each input of the plurality of inputs, is determined based on a summation of weight values associated with each node along the path associated with each input; (This step summing values is practically performable in the human mind and is understood to be a recitation of a mental process with pen and paper (i.e., evaluation). This step is also understood as performing a mathematical calculation.), and
generating a relative importance ranking for each input value based on a distribution plot of importance values in relation to each of the plurality of inputs; (This step for generating importance ranking is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).) and
reducing the number of inputs by pruning one or more data values around one or more peaks of the distribution plot to generate a first reduced input MLM; (This step for reducing inputs is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A method, comprising, via at least one processor of a computing device: (The processor and computer device are understood to be generic computer equipment. See MPEP 2106.05(f).)
the original MLM corresponding to a fully trained MLM associated with a plurality of weights generated along a path associated with each of a plurality of inputs based on a first training dataset, (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the MLM - See MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are generic computer functions in combination with field of use that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A method, comprising, via at least one processor of a computing device: (The processor and computer device are understood to be generic computer equipment. See MPEP 2106.05(f).)
the original MLM corresponding to a fully trained MLM associated with a plurality of weights generated along a path associated with each of a plurality of inputs based on a first training dataset, (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the MLM - See MPEP 2106.05(h).)
The additional elements as disclosed above in combination of the abstract idea
are not sufficient to amount to significantly more than the judicial exception as they are generic computer functions in combination with field of use that are implemented to perform the disclosed abstract idea above.
Regarding Claim 8
2A Prong 1:
pruning an original machine learning model (MLM) to generate a reduced input MLM,
determining an importance value of each input of the plurality of inputs associated with the original MLM by applying a second dataset at an output of the original MLM and performing a backpropagation operation with respect to each input of the plurality of input (This step for determining importance is practically performable in the human mind and is understood to be a recitation of a mental process with pen and paper (i.e., evaluation). Performing backpropagation is also understood as performing a mathematical calculation.), the back propagation operation comprising:
summing values of each weight along a path associated with each input, by starting at an output of the original MLM, using the second dataset, and tracing backward to each of the plurality of inputs, wherein an importance value, for each input of the plurality of inputs, is determined based on a summation of weight values associated with each node along the path associated with each input; (This step summing values is practically performable in the human mind and is understood to be a recitation of a mental process with pen and paper (i.e., evaluation). This step is also understood as performing a mathematical calculation.)
generating a relative importance ranking for each input value based on a distribution plot of importance values in relation to each of the plurality of inputs; (This step for generating importance ranking is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).) and
reducing the number of inputs by pruning one or more data values around one or more peaks of the distribution plot to generate a first reduced input MLM; (This step for reducing inputs is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a processor, cause the processor to: (mere instructions to apply the exception using a generic computer component- see MPEP 2106.05(f))
the original MLM corresponding to a fully trained MLM associated with a plurality of weights generated along a path associated with each of a plurality of inputs based on a first training dataset (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the MLM - See MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are generic computer functions in combination with field of use that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a processor, cause the processor to: (mere instructions to apply the exception using a generic computer component- see MPEP 2106.05(f))
the original MLM corresponding to a fully trained MLM associated with a plurality of weights generated along a path associated with each of a plurality of inputs based on a first training dataset (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the MLM - See MPEP 2106.05(h).)
The additional elements as disclosed above in combination of the abstract idea
are not sufficient to amount to significantly more than the judicial exception as they are generic computer functions in combination with field of use that are implemented to perform the disclosed abstract idea above.
Regarding Claim 15
2A Prong 2 & 2B: The claim recites another additional element “A computing apparatus comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the processor to:” (mere instructions to apply the exception using a generic computer component- see MPEP 2106.05(f))
Regarding Claims 3, 10, and 17
2A Prong 1:
wherein the distribution plot of importance values corresponds to a Gaussian distribution, wherein the Gaussian distribution comprises a multimodal Gaussian distribution that includes a plurality of peaks, determined based on an importance (I) plotted with respect to each of the plurality of inputs of MLM, the importance based on the plurality of weights. (This step is practically implementable in the human mind and is understood to be a mental process with the aid of pen and paper (i.e., evaluation).).
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 4, 11, and 18
2A Prong 1:
wherein reducing the number of inputs is based on an importance thresholds set around the plurality of peaks of the Gaussian distribution (This step is practically implementable in the human mind and is understood to be a mental process (i.e., evaluation).).
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 5, and 12
2A Prong 1:
wherein the generating of the relative importance ranking further comprises normalizing each summed weight. (This step is practically implementable in the human mind and is understood to be a mental process (i.e., evaluation).).
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 7
2A Prong 1: The claim does not recite any further abstract ideas.
2A Prong 2 & 2B:
wherein the memory threshold comprises a memory capacity of the target device. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the memory threshold - See MPEP 2106.05(h).)
Regarding Claim 14
2A Prong 1:
perform a second pruning of the reduced input MLM, if a size of the reduced input MLM is greater than a memory threshold of a target device, to generate a second reduced input MLM capable of being stored on a memory of the target device (This step is practically implementable in the human mind and is understood to be a mental process (i.e., evaluation).).
2A Prong 2 & 2B:
further including instructions that when executed by the processor, causes the processor to (mere instructions to apply the exception using a generic computer component- see MPEP 2106.05(f))
Regarding Claims 21, 23, and 25
2A Prong 1:
further comprising, (This step for determining gaps and removing inputs is practically implementable in the human mind and is understood to be a mental process (i.e., judgment).).
2A Prong 2 & 2B:
via the at least one processor (The processor is understood to be generic computer equipment. See MPEP 2106.05(f).)
Regarding Claims 22, 24, and 26
2A Prong 1:
wherein the Gaussian distribution comprises a single peak determined based on an importance (I) plotted with respect to each of the plurality of inputs of MLM, the importance based on the plurality of weights, the single peak representing an input of the plurality of inputs with the highest importance (I), wherein the at least one input is removed via eliminating at least one input following a threshold set after the single peak. (This step for determining the gaussian distribution and removing inputs is practically implementable in the human mind and is understood to be a mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 27
2A Prong 1:
further comprising performing a second pruning of the reduced input MLM, if a size of the reduced input MLM is greater than a memory threshold of a target device, to generate a second reduced input MLM capable of being stored on a memory of the target device. (This step is practically implementable in the human mind and is understood to be a mental process (i.e., evaluation).).
2A Prong 2 & 2B: The claim does not recite any additional elements.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Baker et al. (US 20230368029 A1) describes performing back-propagation and a learning coach .
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TEWODROS E MENGISTU whose telephone number is (571)270-7714. The examiner can normally be reached Mon-Fri 9:30-5:30.
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, ABDULLAH KAWSAR can be reached at (571)270-3169. 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.
/TEWODROS E MENGISTU/ Primary Examiner, Art Unit 2127
Prosecution Insights