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 .
DETAILED ACTION
This is Non-Final Office Action, in responses to Patent Application filed 01/10/2024; claims foreign priority to 202310099691.4, filed 02/10/2023. Claim(s) 1-20 are pending. Claim(s) 1, 10 and 19 is/are independent.
In addition, 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 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.
Information Disclosure Statement
A signed and dated copy of applicant’s IDS, which was filed 06/12/2025 is/are attached to this Office Action.
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) 1-20 fail to recite statutory subject matter, as defined in 35 U.S.C. 101, because: The claimed invention is/are directed to a judicial exception (i.e., abstract idea) without significantly more.
Step 1: YES (Claim(s) is/are process, machine, manufacture or composition of the matter). ... comprising: an instruction generation method for a neural network accelerator, comprising: determining a plurality pieces of first spatial information corresponding to a neural network model, wherein the first spatial information is used to represent at least one neural network layer in the neural network model; and searching a cache for a policy mapping relationship based on the first spatial information, to obtain first policy information respectively corresponding to at least one piece of the first spatial information, wherein the policy mapping relationship comprises a plurality pieces of target spatial information and target policy information respectively corresponding to the target spatial information, and the plurality pieces of target spatial information comprises the at least one piece of first spatial information; and determining an overall optimization policy corresponding to the neural network model based on a dynamic programming algorithm and a plurality pieces of policy information, to generate executable instructions for the neural network accelerator, wherein the plurality pieces of policy information comprises the first policy information respectively corresponding to the at least one piece of first spatial information... and therefore, fall into one of the four categories of patent eligible subject matter (process, machine, manufacture or composition of the matter).
Step 2A : Prong One: ( whether a claim recites a judicial exception ?) the claim(s) recite ... comprising: an instruction generation method for a neural network accelerator, comprising: … the first spatial information is used to represent at least one neural network layer in the neural network model; and searching a cache for a policy mapping relationship based on the first spatial information, to obtain first policy information … wherein the policy mapping relationship … ; and determining an overall optimization policy corresponding to the neural network model based on a dynamic programming algorithm and a plurality pieces of policy information, to generate executable instructions for the neural network accelerator, … ... These limitation(s) recite mental processes and mathematical calculation...since... searching a cache for a policy mapping relationship based on the first spatial information, …. to obtain first policy information…[APPLY IT] , to determining an overall optimization policy corresponding to the neural network model based on a dynamic programming algorithm and a plurality pieces of policy information, to generate executable instructions for the neural network accelerator… Also, the determining an overall optimization policy corresponding to the neural network model based on a dynamic programming algorithm; required high level mathematical calculation … (see the current specifications in USPGPUB 20240273375 A1 Para(s) 29-33 (i.e.,… he optimization policy corresponding to each division result is obtained through calculation….then… to resolve the problem of long duration for compilation and optimization, an embodiment of this disclosure provides an instruction generation method for a neural network accelerator. According to the method, a neural network model is firstly split into a plurality pieces of first spatial information. Subsequently, a policy mapping relationship is searched for policy information corresponding to the plurality pieces of first spatial information. Finally, an overall optimization policy corresponding to the neural network model, that is, a compilation and optimization result of the neural network model, is determined by using the policy information and a dynamic programming algorithm…for the above interpretations). Thus, these limitation(s) recite mental processes and mathematical calculation(s).
--------------Step 2A : Prong Two: (Do the claim(s) recite “additional element(s) that integrate the “Judicial Exception” into “A Practical Application” ? The claim(s) recite additional limitation(s) such as “ neural network accelerator/electronic device” that is… searching a cache for a policy mapping relationship based on the first spatial information, …. to obtain first policy information then applied (APPLY IT) to obtain first policy information…[APPLY IT] , to determining an overall optimization policy corresponding to the neural network model based on a dynamic programming algorithm and a plurality pieces of policy information, to generate executable instructions for the neural network accelerator …. it is noted, the improvement in the abstract idea itself ... but do not integrate the judicial exception into a practical application, ... These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not integrate the judicial exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
It is noted; An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.... (See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025) (Appeals Review Panel Decision)re dismissed without adequate explanation).
Thus, in light of the Advance notice of change to the MPEP in light of Ex Parte Desjardins new (December 5, 2025) and the memorandum dated August 4, 2025 and December 4, 2025...(i.e. Examples of claims that improve technology or a technical field and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (data structure claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253, 125960, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea);
Also, After the examiner has consulted the specification and determined that the disclosed invention improves technology or a technical field, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp.,838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues)....The full scope of the claim under the BRI should be considered to determine if the claim reflects an improvement in technology or a technical field (e.g., the improvement described in the specification). [MPEP § 2106.05(a) Fourth and Fifth Para(s)] . See also Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential) (“Examiners and panels should not evaluate claims at such a high level of generality” that potentially meaningful technical limitations).
Step 2B: (Whether a Claim Amounts to Significantly More) ? The claim(s) recite additional limitation(s) such as ... “neural network accelerator/electronic device” that is applied (APPLY IT) to obtain first policy information…[APPLY IT] , to determining an overall optimization policy corresponding to the neural network model based on a dynamic programming algorithm and a plurality pieces of policy information, to generate executable instructions for the neural network accelerator ....These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not amount to significantly more than the abstract idea itself (MPEP 2106.05, 2106.04(d) and 2106.05(f)).
As to the dependent claim(s) 22-8, 11-18 and 20 further recite, addition limitation(s) such as, (second policy information, second spatial information, candidate policies, cost parameter, encoding the second optimization policy, quantity of neural network layers and/or an input/output type, at least one of single-input single-output, single-input dual-output, and dual-input dual-output; and the model parameter is represented by using a directed cyclic graph or a computational instruction sequence and hardware resource information, etc.,) These limitation(s) only amounts to mere instructions to implement the abstract idea ...and do not include elements that amount to significantly more than the abstract idea and are also rejected under the same rational.
Accordingly, claims 1-20 fail to recite statutory subject matter, as defined in 35 U.S.C. 101.
Allowable Subject Matter
Claim(s) 1-20 would be allowable if rewritten and/or amending to remedy the 101 rejection(s).
Reason for Allowance
Under the broadest reasonable interpretation of the claimed limitation which is consistence with the Applicant's Specification, the prior arts of recorded when taken individually or in combination do not expressly teach or render obvious the limitations recited in claim(s) 1, 10 and 19 when taken in the context of the claims as a whole, especially the concept of, “… An instruction generation method for a neural network accelerator, comprising: determining a plurality pieces of first spatial information corresponding to a neural network model, wherein the first spatial information is used to represent at least one neural network layer in the neural network model; and searching a cache for a policy mapping relationship based on the first spatial information, to obtain first policy information respectively corresponding to at least one piece of the first spatial information, wherein the policy mapping relationship comprises a plurality pieces of target spatial information and target policy information respectively corresponding to the target spatial information, and the plurality pieces of target spatial information comprises the at least one piece of first spatial information; and determining an overall optimization policy corresponding to the neural network model based on a dynamic programming algorithm and a plurality pieces of policy information, to generate executable instructions for the neural network accelerator, wherein the plurality pieces of policy information comprises the first policy information respectively corresponding to the at least one piece of first spatial information…” As claimed and further supported in the specifications PGPUB 20240273375 A1- The Abstract and Para(s) [0007]-[0009], [0033], [0081]-[0089], [0090]-[0096], and [0111]-[0123].
In addition, neither a reference uncovered that would have provided a basis of evidence for asserting a motivation, nor one of ordinary skilled in the art before the effective filing date of the claimed invention, would have combined them to arrive at the present invention as recited in the context of independent claim(s) 1, 10 and 19 as a whole.
Thus, claim(s) 1, 10 and 19 is/are allowed over the prior arts of record. Dependent claims 2-9, 11-18 and 20 are also allowable due to its dependency of independent claim(s) 1, 10 and 19.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fogal et al. (“ US 20210398015 A1” filed 11/10/2020, discloses executable machine learning models that are executable in a zero-runtime operating environment. This allows the machine learning models to be deployed in limited memory environments such as embedded domains. A machine learning compiler is provided to generate the executable machine learning models (the Abstract).
Matveev et al. (“ 10,915,816 B2” filed 09/18/2020, discloses system and method of inferring a neural network (NN) on one or more target computing devices. The NN may include a plurality of layers, where at least one layer includes one or more kernels. Embodiments may include: receiving a data structure representing the NN; analyzing the data structure to produce one or more tasks, where each task may include computations pertaining to a kernel of the NN; selecting a sparse version of at least one kernel and replacing the at least one kernel with the sparse version; and compiling the one or more tasks to produce one or more respective tensor columns, The one or more tensor columns are adapted to fit in respective one or more cache memories of the one or more target computing devices, and include task instruction code that represents at least one computation of the kernel of the NN (the Abstract).
Surendran et al. (“ US 20220350683 A1” filed 04/26/2021, discloses DL compiler 102 generates vectorized code for one or more instructions of code 106. At least one embodiment, code generator 116 performs vectorization during generation of code 106. In at least one embodiment, vectorization of global memory accesses is an optimization that provides a performance improvement, especially for bandwidth limited kernels….wherein code generator 116 marks graph (e.g., DAG) as non-vectorizable if code generator 116 finds operation and/or memory accesses cannot be vectorized.[Para(s) 96-97 and 100-101].
Henderon (“ US 20220292543 A1” filed 03/09/2021, is directed to a system and method of the virtual creation and subsequent, automated or semiautomated, development or construction of popup retail storefronts, popup events, mobile popup shops, food trucks, popup storefront modular high rise smart buildings, or popup retail franchises, which include the machine learning interoperation or integration of various virtual marketplace environments, blockchain & block-lattice architectures, interactive data engineering geographic information systems, and interactive data visualization devices. Embodiments of this present invention may include an interoperative or integrated real property search mechanism, a virtual design environment, social graph networking graph learning generated frameworks for retailers by which a joint-usership experience or popup event co-sponsorship may be facilitated in the creation and launch of one or more popup shops or popup events at one or more remote locations, a commercial shelf space or commercial space marketplace environment or auction place environment, and other integrated user interfaces (the Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUOC A TRAN whose telephone number is (571)272-8664. The examiner can normally be reached Monday-Friday 9am-5pm EST.
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, Cesar Paula can be reached at 571-272-4128. 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.
/QUOC A TRAN/Primary Examiner, Art Unit 2145
Prosecution Insights