DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 5/27/2026 have been fully considered but they are not persuasive.
The applicant argues the prior art of reference, Kim (US 2016/0328647), fails to teach the limitation “generating, based on a set of parameters that define a machine model, an item of software which, when executed by one or more processors, provides an implementation for the machine learning model; and applying one or more software protection techniques to the item of software”. More specifically, the applicant argues Kim does not teach a first step of generating an item of software and a subsequent step of applying a protection technique to the software since the machine learning (ML) model does not exist at the time the machine protection techniques are applied (see remarks pg. 8, para. 1-5). The examiner respectfully disagrees. First, the examiner would like to note claim 1 does not recite or claim a specific order or steps for applying a protection technique to a generated machine learning model or software. Claim 1 only requires the generation of a software item with a protection technique applied to it without specifying an order for applying the protection technique. Second, the prior art Kim recites in [0048] “at block 520…Performing the program analysis can include compiling the source of the ML program based on the ML program annotation to generate the ML model. The ML model can be generated in a binary format...”. Further, [0051] and [0052] teach inserting binary code into the compiled ML program for protecting the program. Further, figure 5, step 530 is labeled “insert binary code based on program analysis”, which implies the program is already generated and further analyzed for inserting binary protection code.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 5, 6, 10-12, 16, 19, 20, and 23-26 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kim et al. (Kim) (US 2020/0184070).
Regarding claims 1 and 16, Kim discloses a system comprising one or more hardware processors ([0005], [0054], the system is embodied as hardware), the one or more hardware processors arranged to carry out a machine learning model protection method ([0004], a binary code is inserted for protecting a machine learning model), the machine learning model protection method comprising:
generating, based on a set of parameters that define a machine learning model, an item of software which, when executed by one or more processors, provides an implementation for the machine learning model ([0025], [0026], [0036], information for generating a machine language model is generated by development component 210 and used by program analysis component 220 for generating a machine language model for execution); and
applying one or more software protection techniques to the item of software ([0024], [0035], [0038], [0047], [0050], [0053], a binary code is inserted for protecting the generated model is inserted into the model).
Regarding claims 5 and 19, Kim discloses the machine learning model is representable, at least in part, as a plurality of nodes, each node having corresponding node functionality ([0029], [0030], the model is represented as a set of nodes which represents a program statement (function)); and
the item of software comprises a plurality of node functions, wherein each node function, when executed by the one or more processors, provides an implementation of the node functionality of a respective subset of the plurality of nodes ([0029], [0030], the model is represented as a set of nodes which represents a program statement (function)).
Regarding claims 6 and 20, Kim discloses wherein the respective subset of the plurality of nodes is a single node of the plurality of nodes ([0029], program statements are embodied as single nodes).
Regarding claims 10 and 23, Kim discloses wherein the set of parameters are data interpretable by a machine learning framework software application (220) ([0025], [0026], [0036], information for generating a machine language model is generated by development component 210 and used by program analysis component 220 for generating a machine language model for execution; [0054], the system is embodied as software and/or hardware) to perform the machine learning model ([0025], [0026], [0036], information for generating a machine language model is used by development component 210 for generating a machine language model for execution).
Regarding claims 11 and 24, Kim discloses wherein the set of parameters specify one or more of: (b) some or all of the structure of the machine learning model ([0025] The development component 210 is configured to implement a development stage that designs the ML program for generating a ML model. During the development stage, the source code of the ML program can be annotated to generate an ML program annotation that indicates which part needs confidentiality protection).
Regarding claims 12 and 25, Kim discloses wherein generating the item of software comprises including, as part of the item of software, instructions which, when executed by the one or more processors, provide one or more security features in combination with the implementation for the machine learning model ([0025] The development component 210 is configured to implement a development stage that designs the ML program for generating a ML model. During the development stage, the source code of the ML program can be annotated to generate an ML program annotation that indicates which part needs confidentiality protection).
Regarding claim 26, the limitations of claim 26 are rejected in the analysis of claim 16. Kim further discloses a non-transitory computer readable medium storing a computer program which, when executed by one or more hardware processors, causes the one or more hardware processors to carry out a machine learning model protection method ([0055], a stored program is executed).
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.
Claim(s) 2, 3, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (Kim) (US 2020/0184070) in view of Lin et al. (Lin) (US 2016/0328647).
Regarding claims 2, 3, 17, and 18, Kim discloses the system of claim 16 (See claim 16 above).
Kim is silent about wherein the item of software implements arithmetic operations as fixed-point operations; and wherein the machine learning model protection method comprises at least one of: (a) obtaining a user-defined precision for the fixed-point operations for use in said generating the item of software; and (b) obtaining a user-defined specification for a number of bits for representing an input to and/or an output of the arithmetic operations.
Lin from the same or similar field of endeavor discloses wherein the item of software implements arithmetic operations as fixed-point operations ([0010], [0025], the model is configured as affixed point implementation); and wherein the machine learning model protection method comprises: (b) obtaining a user-defined specification for a number of bits for representing an input to and/or an output of the arithmetic operations ([0010], [0028], [0031], [0049], [0052], the bit width for parameters and calculations for a neural network is adjusted based on user input).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Lin into the teachings of Kim to reduce a model size, reduce processing time, reduce memory bandwidth, and/or reduce power consumption (Kim: [0025]).
Claim(s) 7, 9, 21, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (Kim) (US 2020/0184070) in view of applicant admitted prior art (AAPA).
Regarding claims 7, 9, 21, and 22, Kim discloses the system of claim 19 (see claim 19 above) and the system of claim 16 (see claim 16 above).
Kim is silent about wherein the machine learning model is one of:(a) a model for a neural network and each of the plurality of nodes is a respective neuron of the neural network.; (b) a model for a decision tree and each of the plurality of nodes is a respective node of the decision tree; or (c) a model for a random forest and each of the plurality of nodes is a respective node of the random forest; and wherein the machine learning model is a support vector machine.
The applicant admits as prior art (AAPA) wherein the machine learning model is one of: (a) a model for a neural network and each of the plurality of nodes is a respective neuron of the neural network (pg. 1, para. 2, many types of machine language models include neural networks, decision trees, support vector machines, and random forests); (b) a model for a decision tree and each of the plurality of nodes is a respective node of the decision tree (pg. 1, para. 2, many types of machine language models include neural networks, decision trees, support vector machines, and random forests); or (c) a model for a random forest and each of the plurality of nodes is a respective node of the random forest (pg. 1, para. 2, many types of machine language models include neural networks, decision trees, support vector machines, and random forests); and wherein the machine learning model is a support vector machine (pg. 1, para. 2, many types of machine language models include neural networks, decision trees, support vector machines, and random forests).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of AAPA into the teachings of Kim dependent upon the choice of system design for implementing common machine language model types.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Srinivasan et al. (Srinivasan) (US 2021/0133577) ([0002], machine learning models are encrypted).
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 JEFFERY A WILLIAMS whose telephone number is (571)270-7579. The examiner can normally be reached M-F 8:00-5:00.
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, Sath Perungavoor can be reached at 571-272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEFFERY A WILLIAMS/Primary Examiner, Art Unit 2488
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