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 .
Status of Claims
This action is responsive to Applicant’s claims filed 09/01/2026
Claims 1-2 and 5-8, 10-14, and 16-20 are currently pending and have been examined here.
Claims 1, 5, 10-11, and 19-20 have been amended.
Claims 3-4, 9, and 15 have been canceled.
Response to Arguments
Applicant’s arguments, see pages 8-11, with respect to the 35 U.S.C. 101 rejections have been fully considered, and they are persuasive. The 35 U.S.C. 101 rejections have been withdrawn.
Applicant’s arguments with respect to the 35 U.S.C. 103 rejections have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claims 11 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5-8, 10, 12-13, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (WIPO Document No. WO2022083624A1; hereinafter "Wang") in view of Kirshenboim et al. (U.S. PG Pub. No. 20240144051; hereinafter "Kirshenboim") further in view of Choi et al. (Korean Patent Document No. KR 20220144281 A; hereinafter "Choi") further in view of Alakuijala et al. (U.S. PG Pub. No. 20210027195; hereinafter "Alakuijala").
As per claim 1, Wang teaches:
A method for optimizing an artificial intelligence based model, which is performed by a computing device, the method comprising:
Wang teaches a system and method for determining modifications to a first model based on constraints (filtering criteria) which include hardware capabilities of a target device. (Wang: abstract, paragraph [0010, 36])
obtaining model information corresponding to the artificial intelligence based model, and obtaining target device information corresponding to a target device in which the model is to be executed;
Wang teaches that the system may receive constraint conditions including hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 36])
obtaining blocks of operators corresponding to the model by determining operators constituting the model by parsing the model information, and
Wang teaches an initial model set based on structural units and connections and relationships within them, wherein the search space comprising blocks of operators may be parsed in order to generate and configure an initial model. (Wang: paragraph [0022, 101-102, 105])
configuring the model in units of a block of operators by grouping the determined operators into one or more blocks based on the model information, wherein each of the blocks has a structure indicating a connection relationship between operators to preserve a structural topology and a design intention of the model;
Wang teaches an initial model set based on structural units and connections and relationships within them, wherein the search space comprising blocks of operators may be parsed in order to generate and configure an initial model. (Wang: paragraph [0022, 101-102, 105])
determining at least one candidate block from the block pool using the target device information as filtering criteria;
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112])
With respect to the following limitation:
changing a structure of the model to correspond to the target device, by replacing units of a block, at least one of the blocks of operators corresponding to the model with the at least one candidate block, based on a performance comparison comparing a first performance-related score of the at least one candidate block with a second performance-related score of the blocks of operators of the model;
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Wang, however, does not appear to explicitly teach choosing blocks from an original model to maintain in the target derived model, or doing so based on a performance related score.
To be thorough, and to the extent that Wang does not explicitly teach choosing blocks from the original model to maintain into the derived model, Kirshenboim teaches this element. Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim teaches combining the above elements with the teachings of Wang for the benefit of providing a system and method which can automatically generate models that are far more efficient than those generated using traditional techniques that do not consider the availability of hardware-supported inference operations during model generation while still providing comparable accuracy to models generated using traditional techniques. (Kirshenboim: paragraph [0019]) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Kirshenboim with the teachings of Wang to achieve the aforementioned benefits.
Choi, however, teaches that a score for a layer (a block) of a model may be determined, wherein the score indicates the performance of the layer (based on operational efficiency of a model on a device and memory usage on the device), and wherein individual layers of the model may be changed based on the score. (Choi: paragraphs [0123, 152-153, 156-160]) Choi teaches combining the above elements with the teachings of Wang in view of Kirshenboim for the benefit of realizing improved performance by helping optimized modeling. (Choi: paragraph [0153]) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Choi with the teachings of Wang in view of Kirshenboim to achieve the aforementioned benefits.
Wang in view of Kirshenboim further in view of Choi further teaches:
and wherein the first and second performance-related scores are quantitatively determined based on at least one of block latency, block accuracy, block memory usage, or block power consumption when a block is executed in the target device;
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) The motivation to combine Kirshenboim persists. Choi, as outlined above, teaches that a score for a layer (a block) of a model may be determined, wherein the score indicates the performance of the layer (based on operational efficiency of a model on a device), and wherein layers may be changed based on the score. (Choi: paragraphs [0123, 152-153, 156-160]) The motivation to combine Choi persists.
performing at least one of compression, weight-lightening, and quantization of the changed model to correspond to the target device.
Alakuijala, however, teaches that a model may be derived from another model through compression and quantization of the first model, wherein the quantization may be performed in order to correspond to a target device's data storage or processing capabilities. (Alakuijala: paragraphs [0017-18, 33, 62-68], Fig. 2) Alakuijala teaches combining the above elements with the teachings of Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala for the benefit of reductions in bandwidth use which is a significant cost to mobile computing, and allowing for increased use of machine learned models in bandwidth-limited networks. (Alakuijala: paragraph [0033]) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Alakuijala with the teachings of Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala to achieve the aforementioned benefits.
As per claim 2, Wang in view of Kirshenboim further in view of Choi teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein each of the blocks of operators includes operators requisitely used for achieving functions of the model, and each of the blocks of operators has a structure including a node indicating an operator and an edge indicating a connection between operators.
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) The motivation to combine Kirshenboim persists.
As per claim 5, Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein the filtering criteria are determined based on a combination of the model information and the target device information.
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim further teaches the consideration of input and output sizes in determining which blocks to preserve in the model based on the filtering criteria comprising the device hardware. (Kirshenboim: paragraph [0018, 29-30]) The motivation to combine Kirshenboim persists.
As per claim 6, Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala teaches all of the limitations of claim 5, as outlined above, and further teaches:
wherein the filtering criteria include: a first filtering criterion for determining a block having an input size or an output size corresponding to an input size or an output size of each of the blocks of operators constituting the model among a plurality of prestored blocks in the block pool, as the candidate block.
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim further teaches the consideration of input and output sizes in determining which blocks to preserve in the model based on the filtering criteria comprising the device hardware. (Kirshenboim: paragraph [0018, 29-30]) The motivation to combine Kirshenboim persists.
As per claim 7, Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala teaches all of the limitations of claim 5, as outlined above, and further teaches:
wherein the filtering criteria include: a second filtering criterion for determining a block constituted only by operators supported in the target device among a plurality of prestored blocks in the block pool, as the candidate block.
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim further teaches the consideration of input and output sizes compatible with the device in determining which blocks to preserve in the model based on the filtering criteria comprising the device hardware. (Kirshenboim: paragraph [0018, 29-30]) The motivation to combine Kirshenboim persists.
As per claim 8, Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein the obtaining the blocks of operators corresponding to the model includes: determining blocks of operators for covering the operators corresponding to the model information among blocks in the block pool including a reference block corresponding to a set of predefined operators and a custom block corresponding to a set of operators according to user definition, and wherein one block among the blocks of operators corresponds to a subset of the operators constituting the model.
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim further teaches that the user may specify which parameters are to be incorporated into a model. (Kirshenboim: paragraph [0070], Fig 8) The motivation to combine Kirshenboim persists.
As per claim 10, Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein the first and second performance-related scores include: at least one of block latency measured or expected in the target device, block accuracy measured or expected in the target device, or block power consumption measured or expected in the target device.
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim further teaches the consideration of accuracy of the candidate blocks in performing the task in determining which blocks to include. (Kirshenboim: paragraph [0036-38]) The motivation to combine Kirshenboim persists.
As per claim 12, Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala teaches all of the limitations of claim 1, as outlined above, and further teaches:
converting the changed model to have a runtime supportable by the target device;
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim further teaches the determination as to whether the model meets an execution time metric in determining whether the model is to be used. (Kirshenboim: paragraph [0036-38]) The motivation to combine Kirshenboim persists.
obtaining a benchmark result generated by executing the converted model in the target device;
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim further teaches the determination as to whether the model meets an execution time metric in determining whether the model is to be used. (Kirshenboim: paragraph [0035-38, 44]) The motivation to combine Kirshenboim persists.
determining whether the benchmark result satisfies at least one constraint of a first constraint corresponding to the target device or a second constraint set by a user;
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim further teaches the determination as to whether the model meets an execution time metric in determining whether the model is to be used. (Kirshenboim: paragraph [0036-38]) The motivation to combine Kirshenboim persists.
and determining reperforming of the changing the structure of the model to correspond to the target device when it is determined that the benchmark result does not satisfy the at least one constraint.
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim further teaches the determination as to whether the model meets an execution time metric in determining whether the model is to be used. (Kirshenboim: paragraph [0036-38]) The motivation to combine Kirshenboim persists.
As per claim 13, Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein the blocks of operators or the candidate block are selectable from the block pool including a reference block corresponding to a set of predefined operators or a custom block corresponding to a set of operators according to user definition, and each of the blocks in the block pool is defined as metadata indicating a feature of a block or features of operators in the block.
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim further teaches that the user may specify which parameters and which operations (metadata in the form of data describing data of the blocks) are to be incorporated into a model. (Kirshenboim: paragraph [0070], Fig 8) The motivation to combine Kirshenboim persists.
As per claim 16, Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein the changing the structure of the model includes: changing the structure of the model to correspond to the target device by replacing at least one of the blocks of operators corresponding to the model with the at least one candidate block constituted by replacement operators executable in the target device.
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim further teaches the replacement of operators in the first model with others in the second, wherein the replacement operators may be compatible with the target device. (Kirshenboim: paragraph [0026, 31-33]) The motivation to combine Kirshenboim persists.
As per claim 17, Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala teaches all of the limitations of claim 16, as outlined above, and further teaches:
wherein the replacement operators are determined among operators supportable in the target device based on a mathematical operation result of the operator, an input size of the operator, an output size of the operator, an input value of the operator, an output value of the operator, and a relationship between previous or post operators of the operator.
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim further teaches the consideration of input and output sizes, input and output values, weights for edges, and accuracy in determining which blocks to preserve in the model based on the filtering criteria comprising the device hardware. (Kirshenboim: paragraph [0018, 21, 29-30, 27, 369-38, 98]) The motivation to combine Kirshenboim persists.
As per claim 18, Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala teaches all of the limitations of claim 16, as outlined above, and further teaches:
wherein an operator which is not the same as an operator included in the model but identically performs a function performed by the operator included in the model, or an operator having a shape of data input or output by the operator included in the model and data of a shape quantitatively replaceable, among operators supportable in the target device, is determined as the replacement operators.
Wang further teaches that the first model may be altered to perform a new task, wherein the altering may comprise selecting new sets of blocks to comprise the model, and wherein the new sets of blocks may be chosen based on the filtering constraint comprising the hardware deployment conditions and chip memory size. (Wang: paragraph [0013, 22, 36, 101-102, 105, 112]) Kirshenboim teaches the determination of a seed model 202, from which one or more child models may be determined based on processing capability of a device to implement the child model within, wherein the system may choose blocks (inference operations and connections therebetween) to incorporate into the child model based on this filter criteria. (Kirshenboim: paragraphs [0029-35, 39], Fig. 1, 2A) Kirshenboim further teaches that child models may be selected such that the shape of data output matches that of the parent models. (Kirshenboim: paragraph [0047-54], Figs. 4-6) The motivation to combine Kirshenboim persists.
As per claim 19, Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala the limitations of this claim which are substantially identical to those of claim 1, as outlined above, and further teaches:
A non-transitory computer-readable medium comprising a computer program, wherein when the computer program is executed by at least one processor, the computer program allows at least one processor to perform operations for optimizing an artificial intelligence based model, and the operations comprise:
Wang teaches a system and method for determining modifications to a first model based on constraints (filtering criteria) which include hardware capabilities of a target device. (Wang: abstract, paragraph [0010, 36]) Wang further teaches the implementation of the system and method using a computer device and therefore teaches a non-transitory computer readable storage medium storing code which, when executed by a processor, performs the functions of the system. (Wang: paragraphs [0040-44])
As per claim 20, Wang in view of Kirshenboim further in view of Choi further in view of Alakuijala the limitations of this claim which are substantially identical to those of claim 1, as outlined above, and further teaches:
A computing device comprising:
Wang teaches a system and method for determining modifications to a first model based on constraints (filtering criteria) which include hardware capabilities of a target device. (Wang: abstract, paragraph [0010, 36]) Wang further teaches the implementation of the system and method using a computer device and therefore teaches a non-transitory computer readable storage medium storing code which, when executed by a processor, performs the functions of the system. (Wang: paragraphs [0040-44])
at least one processor;
Wang teaches a system and method for determining modifications to a first model based on constraints (filtering criteria) which include hardware capabilities of a target device. (Wang: abstract, paragraph [0010, 36]) Wang further teaches the implementation of the system and method using a computer device and therefore teaches a non-transitory computer readable storage medium storing code which, when executed by a processor, performs the functions of the system. (Wang: paragraphs [0040-44])
and a memory, wherein the at least one processor is configured to perform:
Wang teaches a system and method for determining modifications to a first model based on constraints (filtering criteria) which include hardware capabilities of a target device. (Wang: abstract, paragraph [0010, 36]) Wang further teaches the implementation of the system and method using a computer device and therefore teaches a non-transitory computer readable storage medium storing code which, when executed by a processor, performs the functions of the system. (Wang: paragraphs [0040-44])
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMMETT K WALSH whose telephone number is (571)272-2624. The examiner can normally be reached Mon.-Fri. 6 a.m. - 4:45 p.m..
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/EMMETT K. WALSH/Primary Examiner, Art Unit 3626
