The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This office action is in response to Applicant’s submission filed on 16 June 2026. THIS ACTION IS NON-FINAL.
In response to the restriction requirement, Applicant’s election without traverse of the instant application based on the reply filed on 6/16/2026 and 7/16/2026 interview is acknowledged.
Status of Claims
Claims 1-10, 14-20 are pending.
Claims 11-13 are cancelled.
Claims 14-20 are withdrawn.
Claim 1 includes limitations interpreted under 35 U.S.C. 112(f), because it uses a generic placeholder coupled with functional language without reciting sufficient structure to achieve the function.
Claims 1-10 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement.
Claims 1-10 are rejected under 35 U.S.C. 112(b) as indefinite.
Claim 1-5 are rejected under 35 U.S.C. 101 for being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
In claim 1, claim limitation, a "model compression unit", have been interpreted under 35 U.S.C. 112(f), because they use a generic placeholder coupled with functional language without reciting sufficient structure to achieve the function. The specification does not provide descriptions of the structure of these elements.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim(s) limitations treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Rejections - 35 USC § 112
112(b) Rejection
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.
A claim is indefinite if, when read in light of the specification, it fails to inform, with reasonable certainty, those skilled in the art about the scope of the invention. Nautilus, Inc. v. Biosig Instruments, Inc., 110 USPQ.2d 1688, U.S. Supreme Court (2014).
Claims 1-10 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 pre-AIA the applicant regards as the invention.
Regarding claim 1, including metaphorical terminology lacking structural / procedural definitions: a “model compression unit”, the specification does not provide sufficient structural / procedural description to determine the scope of these limitations, the claims are therefore indefinite.
Regarding claims 2-10, which depend on above rejected claim 1, are rejected for the same reason.
Regarding claims 3, 9, “the machine-readable storage medium”, lack of antecedent basis, the claim is therefor indefinite. For the purpose of applying prior art, this limitation is construed to be “the machine-readable medium”.
Regarding claim 8, “… converting an input having a first bit width to the convolution layer to a second bit width prior to performing matrix calculations…”, s susceptible to two readings: (a) converting [an input to the convolution layer, having a first bit width] to a second bit width, or (b) some other parsing, the claim is therefore indefinite, the claim is therefore indefinite. For the purpose of applying prior art, this limitation is construed to be “… converting an input to the convolution layer from a first bit width to a second bit width prior to performing matrix calculations in the convolution layer …”.
Regarding claim 9, “… for converting the output from …”, “the output”, lack of antecedent basis, the claim is therefore indefinite. For the purpose of applying prior art, this limitation is construed to be “… for converting an output from …”.
35 U.S.C. 112(a) Rejections
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.
Claims 1-10 are 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, including metaphorical terminology lacking support of structural / procedural description: a “model compression unit”, the specification does not provide sufficient structural / procedural description for these limitations. The claims are therefore rejected for failing to comply with the written description requirement.
Regarding claims 2-10, which depend on above rejected claim 1, are rejected for the same reason.
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.
Judicial Exception
Claims 1-5 of the claimed invention are directed to a judicial exception, an abstract idea, without significantly more.
(Independent Claims) With regards to claim 1,
Step 1: The claim recites a machine, which falls into one of the statutory categories.
2A – Prong 1: the claim, in part, recites
(a)“analyzing …. the device information and the performance requirements information to determine an amount to compress one or more machine learning models to permit the resource-constrained computing device to operate the one or more machine learning models on the resource-constrained computing device…” (mental process and/or math concept ), 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. That is, other than reciting a computing device, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the language about generic computer elements, “analyzing … to determine”, in the limitation citied above encompasses observing / evaluating resource and performance data to determine size reduction of data processing model, which is based on observation, evaluation, judgement, and/or opinion, that could be performed by human using paper / pen / calculator. 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.
(b) “compressing … the one or more machine learning models to permit the one or more machine learning models to operate on the resource- constrained computing device to generate one or more compressed machine learning models by altering a structure of the one or more machine learning models to require fewer resources when executed than an uncompressed version of the one or more machine learning models…” (mental process and/or math concept ), 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. That is, other than reciting a computing device, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the language about generic computer elements, “compressing … by altering a structure …”, in the limitation citied above encompasses adjusting data processing model based on certain requirement, which is based on observation, evaluation, judgement, and/or opinion, that could be performed by human using paper / pen / calculator. 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.
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements: (a) “a processor; and a machine-readable medium storing executable instructions that, when executed, cause the processor alone or in combination with other processors to perform operations …”, “model compression unit”, which is mere instruction to apply an exception (see MPEP 2106.05(f)); (b) “obtaining, via a model compression unit, device information and performance requirements information for a resource-constrained computing device”, extra-solution activity of pre-solution data gathering (see MPEP.2106.05(g)); (c) “deploying the one or more compressed machine learning models to the resource- constrained computing device to process ink stroke information captured by a user interface of the resource-constrained computing device” which is field of use and technological environment (see MPEP 2106.05(h)). For (a), these computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of state transition probability calculation) such that it amounts no more than mere instructions to apply the exception using a generic computer component. For (b), these steps are insignificant extra solution activity, like mere data gathering and outputting, as stated in MPEP.2106.05(g), “(3) Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering)”.
Accordingly, this additional elements do 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.
2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, claim 1 recites the additional elements: (a) “a processor; and a machine-readable medium storing executable instructions that, when executed, cause the processor alone or in combination with other processors to perform operations …”, “model compression unit”, which is mere instruction to apply an exception (see MPEP 2106.05(f)); (b) “obtaining, via a model compression unit, device information and performance requirements information for a resource-constrained computing device”, which is extra-solution activity of pre-solution data gathering (see MPEP.2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"); (c) “deploying the one or more compressed machine learning models to the resource- constrained computing device to process ink stroke information captured by a user interface of the resource-constrained computing device” which is field of use and technological environment (see MPEP 2106.05(h)). Hence the additional elements do not add anything significant to the abstract idea. The claim is not patent eligible.
(Dependent claims)
Claims 2-5 are dependent on claim 1, and include all the limitations of claim 1. Therefore, claims 2-5 recite the same abstract ideas.
With regards to claim 2, The claim recites additional element of “wherein the device information comprises processor type information, device memory information, and device storage information, and wherein the performance requirements information comprises model latency requirements, model size requirements, and model accuracy requirements”, which adds specificity to what is still largely data-gathering, which is extra-solution activity of pre-solution data gathering (see MPEP.2106.05(g))). Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. 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, the additional element of “wherein the device information comprises processor type information, device memory information, and device storage information, and wherein the performance requirements information comprises model latency requirements, model size requirements, and model accuracy requirements”, which is extra-solution activity of pre-solution data gathering (see MPEP.2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The claim is not patent eligible.
With regards to claim 3, The claim recites additional element of “training the one or more machine learning models to process the ink stroke information, wherein at least one of the one or more machine learning models is a stroke classification mode”, which is mere instruction to apply an exception (see MPEP 2106.05(f)). Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. 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, the additional element of “training the one or more machine learning models to process the ink stroke information, wherein at least one of the one or more machine learning models is a stroke classification mode”, which is mere instruction to apply an exception (see MPEP 2106.05(f)). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The claim is not patent eligible.
With regards to claim 4, the claim recites element of “wherein the resource-constrained computing device lacks sufficient computing resources to operate an instance of the one or more machine learning models”, which reinforce the condition that the abstract data processing steps in claim 1 is performed under. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 5, The claim recites additional element of “wherein the one or more machine learning models includes a convolutional neural network (CNN)”, which narrows to a specific, well-known architecture for the process of claim 1, which is field of use and technological environment (see MPEP 2106.05(h)). Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. 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, the additional element of “wherein the one or more machine learning models includes a convolutional neural network (CNN)”, which is field of use and technological environment (see MPEP 2106.05(h)). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The claim is not patent eligible.
Allowable Subject Matter
Claims 1-10 include allowable subject matter since when reading the claims in light of the specification, as per, MPEP §2111.01 or Toro Co. v. White Consolidated Industries Inc., 199F.3d 1295, 1301, 53 USPQ2d 1065, 1069, 1069 (Fed.Cir. 1999), none of the references of record alone or in combination disclose or suggest the combination of limitations specified in claims 1-10.
In interpreting the claims, in light of the specification filed on 7 November 2023, the Examiner finds the claimed invention to be patentably distinct from the prior arts of record.
Regarding the independent claim 1, the primary reason for the allowance is the inclusion of the specific process and system for jointly analyzing device information and performance requirements information of a resource-constrained computing device to determine an amount to compress one or more machine learning models, compressing the models by altering their structure accordingly, and deploying the compressed models to that same resource-constrained computing device to process ink stroke information captured by its own user interface.
Regarding the dependent claims, which include all the limitations of the independent claims, are also allowed.
The following are references close to the invention claimed:
Turgeman et al., US-PATENT NO. 11928583B2) [hereafter Turgeman] teaches obtaining edge-related metrics and performance functions to select/deploy compressed deep learning models to an edge device. However, Turgeman does not teach the specific process and system for jointly analyzing device information and performance requirements information of a resource-constrained computing device to determine an amount to compress one or more machine learning models, compressing the models by altering their structure accordingly, and deploying the compressed models to that same resource-constrained computing device to process ink stroke information captured by its own user interface.
Hegde et al., US-PATENT NO.11403523 B2 [hereafter Hegde] teaches compressing a neural network using sparsity constraints and knowledge distillation to drop or retain weights for resource-constrained portable devices. However, Hegde does not teach the specific process and system for jointly analyzing device information and performance requirements information of a resource-constrained computing device to determine an amount to compress one or more machine learning models, compressing the models by altering their structure accordingly, and deploying the compressed models to that same resource-constrained computing device to process ink stroke information captured by its own user interface.
Sandler et al., "MobileNetV2: Inverted Residuals and Linear Bottlenecks," CVPR 2018 [hereafter Sandler] teaches a depthwise separable convolution architecture that alters model structure to reduce computation for mobile devices. However, Sandler does not teach the specific process and system for jointly analyzing device information and performance requirements information of a resource-constrained computing device to determine an amount to compress one or more machine learning models, compressing the models by altering their structure accordingly, and deploying the compressed models to that same resource-constrained computing device to process ink stroke information captured by its own user interface.
Lin, et al., "MCUNet: Tiny Deep Learning on IoT Devices," NeurIPS 2020 [hereafter Lin] teaches jointly designing a neural network architecture and inference engine constrained by a target device's memory and latency budget. However, Lin does not teach the specific process and system for jointly analyzing device information and performance requirements information of a resource-constrained computing device to determine an amount to compress one or more machine learning models, compressing the models by altering their structure accordingly, and deploying the compressed models to that same resource-constrained computing device to process ink stroke information captured by its own user interface.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TSU-CHANG LEE whose telephone number is 571-272-3567. The fax number is 571-273-3567.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas, can be reached 571-272-2589.
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/TSU-CHANG LEE/
Primary Examiner, Art Unit 2128
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