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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/25/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 14 is objected to because of the following informalities:
Claim 14, line 1 recites “modifying data” which appears to redefine or establish a new of “the modifying data” of claim 11, line 2.
Appropriate correction is required.
Claim Interpretation – Intended Use
The Examiner notes:
Claim 1 recites “a calculation stage for modifying… and for outputting…”.
Claim 4 recites “an encryption unit for encrypting… a read request for the modifying data…”.
Claim 5 recites “a sensor for providing…”.
Claim 7 recites “receiving configuration data for an artificial intelligence model”.
Claim 15 recites “provide the modified sensor data to the classification stage for use as input data…”.
The above-identified claim limitations are all a manner of operating the device. A claim containing a "recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus" if the prior art apparatus teaches all the structural limitations of the claim. See MPEP 2114(II).
Note that the above-identified claim limitations are all intended usage limitations that suggests or makes optional a limitation, but does not require steps to be performed and does not necessarily limit the claim to a particular structure; thus the limitation does not limit the scope of the claim; See MPEP § 2103(I)(C).
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 1
“a calculation stage for modifying…”
“a classification stage configured for running…”
Claim 5
“an encryption unit for encrypting…”
Claim 15
“a classification stage configured to run…”
“a calculation stage configured to modify…”
Claim 16
“an encryption unit configured to encrypt…”
Claim 18
“a secure element configured to communicate…”
A) Generic placeholder/Non-structural term: “stage”, “unit”, “element”
B) Functional language: “modifying”, “running”, “encrypting”, “run”, “modify”, “encrypt”, “communicate” and Linking word/phrase: “for”, “configured for”, “configured to”
C) Not modified by sufficient structure: “calculation”, “classification”, “encryption”, “secure”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 5, 15-16, 18, claim limitations “calculation stage”, “classification stage”, “encryption unit” and “secure element” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Regarding “calculation stage”, paragraph [0054] discloses “In this embodiment, the TEE comprises a calculation stage 12 and a memory 11 for the salt.” but discloses no definite structure of the stage.
Regarding “classification stage”, paragraph [0041] discloses “The classification stage 7 is typically implemented in a microcontroller or microprocessor. It may contain a combination of software, hardware and firmware, running on a standard central processing unit.” but discloses no definite structure of the stage.
Regarding “encryption unit”, paragraph [0023] discloses “The secure element 10 further comprises an encryption unit 18” but discloses no definite structure of the unit.
Regarding “secure element”, paragraph [0021] discloses “Fig. 2 discloses an embodiment of a training apparatus 1 with a protection for the AI model” and paragraph [0023] discloses “The secure element 10 contains a memory 11 for salt and a calculation stage 12” but discloses no definite structure of the element.
In addition, in cases involving a special purpose computer-implemented means-plus-function limitation, the Federal Circuit has consistently required that the structure be more than simply a general purpose computer or microprocessor and that the specification must disclose an algorithm for performing the claimed function. See MPEP 2181(II)(B).
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim 7 recites the limitation "the processing data" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-4, 6, 8-14, 17, 19-20 are rejected under a similar rationale. The dependent claims included in the statement of rejection but not specifically addressed in the body of the rejection have inherited the deficiencies of their parent claim and have not resolved the deficiencies. Therefore, they are rejected based on the same rationale as applied to their parent claims above.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 7-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (35 U.S.C. 101 Judicial Exception) without significantly more. The claims recite running an AI model on pre-processed/modified sensor data, comprising: “receiving configuration data…”, “storing a received AI model…”, “receiving sensor data…”, receiving/storing modifying data…, “pre-processing the sensor data…”, “modify the sensor data”, “running the AI model…”, “outputting output data…”, which are directed to the abstract idea of mental processes. This judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered separately and in combination, do not add significantly more to the abstract idea, as they are well-understood, routine, conventional computer functions as recognized by the courts.
Based upon consideration of all the relevant factors with respect to the claimed invention as a whole, the claims are determined to be directed to an abstract idea without significantly more. The rationale for this determination is explained infra:
The following are Principles of Law:
A patent may be obtained for “any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof”; 35 U.S.C. § 101. The Supreme Court has consistently held that this provision contains an important implicit exception: laws of nature, natural phenomena, and abstract ideas are not patentable; See Alice Corp. v. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014); Gottschalk v. Benson, 409 U.S. 63, 67 (1972) (“Phenomena of nature, though just discovered, mental processes, and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work.”). Notwithstanding that a law of nature or an abstract idea, by itself, is not patentable, an application of these concepts may be deserving of patent protection; See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1293–94 (2012). In Mayo, the Court stated that “to transform an unpatentable law of nature into a patent-eligible application of such a law, one must do more than simply state the law of nature while adding the words ‘apply it.’” Mayo, 132 S. Ct. at 1294 (citation omitted).
In Alice, the Court reaffirmed the framework set forth previously in Mayo “for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of these concepts.” Alice, 134 S. Ct. at 2355. The test for determining subject matter eligibility requires a first step of determining whether the claims are directed to a process, machine, manufacture, or composition of matter. If the claims are directed to one of the four patent-eligible subject matter categories, then the Examiner must perform a two-part analysis to determine whether a claim that is directed to a judicial exception recites additional elements that amount to significantly more than the exception. The first part of the second step in the analysis is to “determine whether the claims at issue are directed to one of those patent-ineligible concepts.” Id. If the claims are directed to a patent-ineligible concept, then the second part of the second step in the analysis is to consider the elements of the claims “individually and ‘as an ordered combination”’ to determine whether there are additional elements that “‘transform the nature of the claim’ into a patent-eligible application.” Id. (quoting Mayo, 132 S. Ct. at 1298, 1297). In other words, the second step in the analysis is to “search for an ‘inventive concept’‒ i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent on the [ineligible concept] itself.’” Id. (brackets in original) (quoting Mayo, 132 S. Ct. at 1294). The prohibition against patenting an abstract idea “cannot be circumvented by attempting to limit the use of the formula to a particular technological environment or adding insignificant post-solution activity.” Bilski v. Kappos, 561 U.S. 593, 610–11 (2010) (citation and internal quotation marks omitted). The Court in Alice noted that “[s]imply appending conventional steps, specified at a high level of generality,” was not “enough” [in Mayo] to supply an “‘inventive concept.’” Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1300, 1297, 1294).
In the “2019 Revised Patent Subject Matter Eligibility Guidance” (2019 PEG), the USPTO has prepared revised guidance for use by USPTO personnel in evaluating subject matter eligibility based upon rulings by the courts.
The Examiner is bound by and applies the framework as set forth by the Court in Mayo and reaffirmed by the Court in Alice and follows the 2019 PEG for determining whether the claims are directed to patent-eligible subject matter.
Step 1: Are the claims at issue directed to a process, machine, manufacture, or composition of matter?
The Examiner finds that the claims are directed to one of the four statutory categories.
Step 2A – Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The Examiner finds that the claims are directed to the abstract idea of running an AI model on pre-processed/modified sensor data, comprising: “receiving configuration data…”, “storing a received AI model…”, “receiving sensor data…”, receiving/storing modifying data…, “pre-processing the sensor data…”, “modify the sensor data”, “running the AI model…”, “outputting output data…”, which are directed to the abstract idea of mental processes.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the Judicial Exception into a practical application?
The abstract idea is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
In addition, the step of outputting output data constitutes extra solution activity because (1) these techniques are well known, (2) this is insignificant extra solution activity and (3) is mere data gathering or outputting. This is a post-solution step that is not integrated into the claim as a whole.
In determining whether the abstract idea was integrated into a practical application, the Examiner has considered whether there were any limitations indicative of integration into a practical application, such as:
(1) Improvements to the functioning of a computer, or to any other technology or technical field; See MPEP § 2106.05(a)
(2) Applying or using a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition; See Vanda Memo (Recent Subject Matter Eligibility Decision: Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals)
(3) Applying the judicial exception with, or by use of, a particular machine; See MPEP § 2106.05(b)
(4) Effecting a transformation or reduction of a particular article to a different state or thing; See MPEP § 2106.05(c)
(5) Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception; See MPEP § 2106.05(e) and Vanda Memo
The Examiner notes that claim features of: “receiving configuration data…”, “storing a received AI model…”, “receiving sensor data…”, receiving/storing modifying data…, “pre-processing the sensor data…”, “modify the sensor data”, “running the AI model…”, “outputting output data…” does not improve the functioning of a computer or technical field, do not effect a particular treatment or prophylaxis for a disease or medical condition, do not apply or use a particular machine, do not effect a transformation or reduction of a particular article to a different state or thing, and do not apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Instead, the claim features of running an AI model on pre-processed/modified sensor data merely use a general-purpose computer as a tool to perform the abstract idea (See MPEP § 2106.05(f)) and merely generally link the use of the abstract idea to a field of use (See MPEP § 2106.05(h)). Thus, the Examiner finds that the claimed invention does not recite additional elements that integrate the Judicial Exception into a practical application.
By contrast, as recited in claim 1, running the AI model on encrypted, modified data, does recite additional elements that integrate the Judicial Exception into a practical application by improving the functioning of a computer.
Step 2B: Is there something else in the claims that ensures that they are directed to significantly more than a patent-ineligible concept?
The claims, as a whole, require nothing significantly more than generic computer implementation or can be performed entirely by a human. The additional element(s) or combination of element(s) in the claims other than the abstract idea per se amount to no more than recitation of generic computer structure (e.g., memory) that serves to perform generic computer functions (e.g., receiving, storing, pre-processing, modifying, running, outputting) that are well-understood, routine, and conventional activities previously known to the pertinent industry. The claimed configuration data, sensor data, modifying data, output data are all numbers, data structures, or datum. Each of these elements are individually dispositive of patent eligibility because of the following legal holdings:
“Data in its ethereal, non-physical form is simply information that does not fall under any of the categories of eligible subject matter under section 101.” Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350 (Fed. Cir. 2014).
The Supreme Court has also explained that “[a]bstract software code is an idea without physical embodiment,” i.e., an abstraction. Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449 (2007).
A claim that recites no more than software, logic, or a data structure (i.e., an abstract idea) – with no structural tie or functional interrelationship to an article of manufacture, machine, process or composition of matter does not fall within any statutory category and is not patentable subject matter; data structures in ethereal, non-physical form are non-statutory subject matter. In re Warmerdam, 33 F.3d 1354, 1361 (Fed. Cir. 1994); see Nuijten, 500 F.3d at 1357.
Furthermore, the claimed invention does not have a specific asserted improvement in computer capabilities, nor is it a specific implementation of a solution to a problem in the software arts; See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016).
Rather, the claims are merely directed towards the abstract idea of running an AI model on pre-processed/modified sensor data, which is similar to ideas that the courts have found to be abstract, as noted supra, and the claims are without a “practical application” or anything “significantly more”.
Considering each of the claim elements in turn, the function performed by the computer system at each step of the process does no more than require a generic computer to perform a well-understood, routine, and conventional activity at a high level of generality.
For example, pre-processing data, running an AI model, outputting output data, encrypting sensor data, training an AI model, transferring the AI model, modifying sensor data, provide modified sensor data, encrypting the modifying data, establishing the modifying data, receiving modifying data from a remote computer which has been found by the courts to be a well-understood, routine, conventional activity in computers; See e.g. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Similarly, receiving configuration data, receiving sensor data, receiving modifying data, receiving modifying data from the edge device, storing a received AI model, storing the modifying data is merely storing, updating and retrieving information in memory, which has been found by the courts to be a well-understood, routine, conventional activity in computers; See e.g. Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Further note that the abstract idea of running an AI model on pre-processed/modified sensor data to which the claimed invention is directed has a prior art basis outside of a computing/technological environment, e.g., receiving an rotated image of an object, rotating the image to be presented in a preset orientation and classifying the object in the image.
The prohibition against patenting an abstract idea “cannot be circumvented by attempting to limit the use of the formula to a particular technological environment or adding insignificant post-solution activity.” Bilski v. Kappos, 561 U.S. 593, 610–11 (2010) (citation and internal quotation marks omitted). The Court in Alice noted that “[s]imply appending conventional steps, specified at a high level of generality,” was not “enough” [in Mayo] to supply an “‘inventive concept.’” Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1300, 1297, 1294).
Viewed as a whole, the claims simply recite the steps of using generic computer components. The claims do not purport, for example, to improve the functioning of the computer system itself. Nor does it affect an improvement in any other technology or technical field. Instead, the claims amount to nothing significantly more than an instruction to implement the abstract idea using generic computer components. This is insufficient to transform an abstract idea into a patent-eligible invention.
Claim Rejections - 35 USC § 103
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) 1-3, 7-9, 11-15, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seo et al. (USP App Pub 2023/0394299; hereinafter Seo) in view of Dethise et al. (USP App Pub 2024/0303548; hereinafter Dethise).
Regarding claim 1, An edge device (Seo: claim 7, “A computing device for implementing a method for classifying encrypted data using a deep learning model”; paragraph [0154], “a program according to an embodiment of the present invention may be configured as a PC-based program or an application dedicated to a mobile terminal”), comprising
a first memory configured to receive and store an artificial intelligence model (AI model) (Seo: paragraph [0059], “The inference model 1500 may be stored in the computing device 1000”; paragraph [0148], “The processor 11100 may execute the software module or the instruction set stored in memory 11200, thereby performing various functions for the computing device 11000 and processing data”; paragraph [0100], “First, the inference model 1500 may receive a large amount of pre-learning data through the data learning step performed by the data learning module 1400 to perform the pre-learning”);
a pre-processor configured to pre-process sensor data (Seo: paragraph [0055], “plurality of original data received from a separate computing device such as a user terminal”; claim 2, “the original data corresponds to image data”);
a …memory configured for storing modifying data (Seo: paragraph [0085], “one or more preset masking elements” i.e., a preset masking element corresponds to the claimed modifying data; paragraph [0145], “the computing device 11000 may at least include at least one processor 11100, a memory 11200”);
a calculation stage for modifying the pre-processed data based on the modifying data (Seo: paragraph [0085], “the original data can be modified through other additional methods … by applying one or more preset masking elements to the original data such that a part of the original data can be masked by one or more preset masking elements”) and for outputting encrypted, modified data (Seo: paragraph [0087], “the data encryption step (S200) may include a step (S210) of encrypting the original data, which is modified through the data transformation step (S110), with a plurality of random phase masks modified through the mask transformation step (S120)” i.e., original data is modified first with a preset masking element, then encrypted with a random phase mask; paragraph [0092], “the data encrypted through step S210 is stored in the computing device 1000”); and
a classification stage configured for running the AI model on the encrypted, modified data (Seo: paragraph [0090], “the real part and imaginary part divided through step S220 are input into the inference model 1500 in the data classification step (S300)”) and for outputting output data generated by running the AI model on the encrypted, modified data (Seo: paragraph [0090], “the inference model 1500 may perform the labelling task for the encrypted data corresponding to the input real part and imaginary part”).
Seo does not teach … a protected memory…
However, in the same field of endeavor, Dethise does teach … a protected memory (Dethise: paragraph [0049], “Trusted Execution Environment (TEE) is a secure area of a processor”; paragraph [0058], “the training enclave implemented as a TEE may comprise computer code”; paragraph [0071], “The training enclaves apply (314) their corresponding masks to their gradients to hide the actual gradients”; paragraph [0049], “It guarantees application code and data loaded inside to be protected with respect to confidentiality and integrity using specialized hardware” i.e., masks are used within the training enclave, therefore it is data loaded inside the TEE of the training enclave)…
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the stored preset masking elements of Seo to incorporate the teachings of Dethise to use a TEE to store the masks. The motivation for doing so is to increase the security and trust of the user in the system (Dethise: paragraph [0049], “the TEE offers an execution space that provides a higher level of security for applications than a rich operating system and more functionality than a ‘secure element’ (SE); thus, increasing the trust of the user in the executing application”).
Regarding claim 2, Seo and Dethise teach the edge device of claim 1, wherein the protected memory is part of a secure element of the edge device (Dethise: paragraph [0049], “Trusted Execution Environment (TEE) is a secure area of a processor”).
The motivation for combining references for claim 2 is the same as the motivation stated in the rejection of claim 1.
Regarding claim 3, Seo and Dethise teach the edge device of claim 1, wherein the protected memory is part of a trusted execution environment (TEE) of a microprocessor (Dethise: paragraph [0049], “Trusted Execution Environment (TEE) is a secure area of a processor”).
The motivation for combining references for claim 3 is the same as the motivation stated in the rejection of claim 1.
Regarding claim 7, A method for classifying data at an edge device (Seo: claim 7, “A computing device for implementing a method for classifying encrypted data using a deep learning model”; paragraph [0154], “a program according to an embodiment of the present invention may be configured as a PC-based program or an application dedicated to a mobile terminal”), comprising:
receiving configuration data for an artificial intelligence model (AI model) (Seo: paragraph [0059], “The inference model 1500 may be stored in the computing device 1000”; paragraph [0148], “The processor 11100 may execute the software module or the instruction set stored in memory 11200, thereby performing various functions for the computing device 11000 and processing data”; paragraph [0100], “First, the inference model 1500 may receive a large amount of pre-learning data through the data learning step performed by the data learning module 1400 to perform the pre-learning”);
receiving sensor data from a sensor (Seo: paragraph [0055], “plurality of original data received from a separate computing device such as a user terminal”; claim 2, “the original data corresponds to image data”) and receiving modifying data … of the edge device (Seo: paragraph [0085], “one or more preset masking elements” i.e., a preset masking element corresponds to the claimed modifying data; paragraph [0145], “the computing device 11000 may at least include at least one processor 11100, a memory 11200”);
pre-processing the sensor data (Seo: paragraph [0055], “plurality of original data received from a separate computing device such as a user terminal”; claim 2, “the original data corresponds to image data”);
running the AI model (Seo: paragraph [0090], “the real part and imaginary part divided through step S220 are input into the inference model 1500 in the data classification step (S300)”) on the pre-processed data and on the modifying data (Seo: paragraph [0085], “the original data can be modified through other additional methods … by applying one or more preset masking elements to the original data such that a part of the original data can be masked by one or more preset masking elements”); and
outputting output data (Seo: paragraph [0090], “the inference model 1500 may perform the labelling task for the encrypted data corresponding to the input real part and imaginary part”) generated by running the AI model on the processing data and the modifying data (Seo: paragraph [0087], “the data encryption step (S200) may include a step (S210) of encrypting the original data, which is modified through the data transformation step (S110), with a plurality of random phase masks modified through the mask transformation step (S120)” i.e., original data is modified first with a preset masking element, then encrypted with a random phase mask; paragraph [0092], “the data encrypted through step S210 is stored in the computing device 1000”).
Seo does not teach … from a protected memory element …
However, in the same field of endeavor, Dethise does teach … from a protected memory element (Dethise: paragraph [0049], “Trusted Execution Environment (TEE) is a secure area of a processor”; paragraph [0058], “the training enclave implemented as a TEE may comprise computer code”; paragraph [0071], “The training enclaves apply (314) their corresponding masks to their gradients to hide the actual gradients”; paragraph [0049], “It guarantees application code and data loaded inside to be protected with respect to confidentiality and integrity using specialized hardware” i.e., masks are used within the training enclave, therefore it is data loaded inside the TEE of the training enclave)…
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the stored preset masking elements of Seo to incorporate the teachings of Dethise to use a TEE store the masks. The motivation for doing so is to increase the security and trust of the user in the system (Dethise: paragraph [0049], “the TEE offers an execution space that provides a higher level of security for applications than a rich operating system and more functionality than a ‘secure element’ (SE); thus, increasing the trust of the user in the executing application”).
Re. claims 8-9, they recite analogous limitations as claims 2-3, respectively, and therefore are rejected for the same reasons.
Regarding claim 11, Seo and Dethise teach the method of claim 7, further comprising
training the AI model based on test datasets (Seo: paragraph [0001], “in which original data is modified such that the original data can be used as training data in a deep learning-based inference model”) and on the modifying data (Seo: paragraph [0058], “The data learning module 1400 performs a data learning step, in which the data learning module 1400 performs a process of learning the inference model 1500 by using the plurality of encrypted data as learning data of the inference model 1500 to allow the inference model 1500 to effectively process the task of classifying a plurality of encrypted data”); and
transferring the trained AI model to the edge device (Dethise: paragraph [0102], “the model owner only gets access to the final model after the training is complete”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the inference system of Seo and Dethise to incorporate the teachings of Dethise to transfer the model after the training is complete. The motivation for doing so is to avoid leaking information about gradients (Dethise: paragraph [0102], “This is because the model owner might store any intermediate updates as a fake “model update” to leak information about the gradients”).
Regarding claim 12, Seo and Dethise teach he method of claim 11, wherein the transferring comprises encrypted communication (Dethise: paragraph [0127], “the apparatus comprises means for receiving a masked model gradient from the means for storing private data and at least one encrypted model and means for updating the model based on the masked model gradients” i.e., the apparatus updates an encrypted model).
The motivation for combining references for claim 12 is the same as the motivation stated in the rejection of claim 11.
Regarding claim 13, Seo and Dethise teach the method of claim 11, wherein the training (Seo: paragraph [0001], “in which original data is modified such that the original data can be used as training data in a deep learning-based inference model”; Dethise: paragraph [0070], “The signaling chart of FIG. 3 illustrates a workflow for a typical round of training”; FIG. 3, model and data are downloaded by the training enclave from outside the training enclave) comprises receiving the modifying data from a remote computer (Dethise: paragraph [0070], “The administration enclave generates (310) the DP-noisy masks and delivers (312) a corresponding mask to each involved training enclave”; FIG. 1, admin enclave is an external computer).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the inference system of Seo and Dethise to incorporate the teachings of Dethise to receive the masks from the admin enclave. The motivation for doing so is to increase the security and trust of the user in the system (Dethise: paragraph [0049], “the TEE offers an execution space that provides a higher level of security for applications than a rich operating system and more functionality than a ‘secure element’ (SE); thus, increasing the trust of the user in the executing application”; paragraph [0060], “the administration enclave running inside a TEE generates a plurality of masks”).
Regarding claim 14, Seo and Dethise teach the method of claim 11, wherein the training (Seo: paragraph [0001], “in which original data is modified such that the original data can be used as training data in a deep learning-based inference model”; Dethise: paragraph [0070], “The signaling chart of FIG. 3 illustrates a workflow for a typical round of training”) comprises receiving modifying data from the edge device (Dethise: paragraph [0071], “The training enclaves apply (314) their corresponding masks to their gradients to hide the actual gradients. The training enclaves transmit (316) the masked model gradients to the aggregator enclave”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the inference system of Seo and Dethise to incorporate the teachings of Dethise to receive masking data from the administration enclave. The motivation for doing so is to only recover the model update with the noise (Dethise: paragraph [0068], “When summing these masked gradients, the model updating logic from the model owner running in the aggregator enclave then only recovers the model update with added DP noise. As a result, the next iteration of the training continues with an updated model that contains DP noise”).
Regarding claim 15, An artificial intelligence (AI) based classification system for an edge device (Seo: claim 7, “A computing device for implementing a method for classifying encrypted data using a deep learning model”; paragraph [0154], “a program according to an embodiment of the present invention may be configured as a PC-based program or an application dedicated to a mobile terminal”), comprising:
a memory configured to store a received AI model (Seo: paragraph [0059], “The inference model 1500 may be stored in the computing device 1000”; paragraph [0148], “The processor 11100 may execute the software module or the instruction set stored in memory 11200, thereby performing various functions for the computing device 11000 and processing data”), wherein the AI model is pre-trained … based on training data (Seo: paragraph [0103], “the initial inference model 1500 may correspond to the inference model 1500 pre-trained with the above-described pre-learning data”), wherein the training data comprises training edge device sensor data (Seo: paragraph [0055], “plurality of original data received from a separate computing device such as a user terminal”; claim 2, “the original data corresponds to image data”) that is modified based on modifying data associated with the edge device (Seo: paragraph [0085], “the original data can be modified through other additional methods … by applying one or more preset masking elements to the original data such that a part of the original data can be masked by one or more preset masking elements”);
a … memory configured to store the modifying data (Seo: paragraph [0085], “one or more preset masking elements” i.e., a preset masking element corresponds to the claimed modifying data; paragraph [0145], “the computing device 11000 may at least include at least one processor 11100, a memory 11200”);
a classification stage configured to run the AI model based on input data (Seo: paragraph [0090], “the real part and imaginary part divided through step S220 are input into the inference model 1500 in the data classification step (S300)”); and
a calculation stage configured to modify sensor data of the edge device based on the modifying data (Seo: paragraph [0085], “the original data can be modified through other additional methods … by applying one or more preset masking elements to the original data such that a part of the original data can be masked by one or more preset masking elements”) and provide the modified sensor data to the classification stage for use as input data for the AI model (Seo: paragraph [0090], “the inference model 1500 may perform the labelling task for the encrypted data corresponding to the input real part and imaginary part”).
Seo does not teach … by an external device… a protected memory…
However, in the same field of endeavor, Dethise does teach … by an external device (Dethise: paragraph [0070], “Each training enclave downloads the private data of the owner and the encrypted model”)… a protected memory (Dethise: paragraph [0049], “Trusted Execution Environment (TEE) is a secure area of a processor”; paragraph [0058], “the training enclave implemented as a TEE may comprise computer code”; paragraph [0071], “The training enclaves apply (314) their corresponding masks to their gradients to hide the actual gradients”; paragraph [0049], “It guarantees application code and data loaded inside to be protected with respect to confidentiality and integrity using specialized hardware” i.e., masks are used within the training enclave, therefore it is data loaded inside the TEE of the training enclave)…
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the stored preset masking elements of Seo to incorporate the teachings of Dethise to download the pre-trained model from an external source and use a TEE store the masks. The motivation for doing so is to increase the security and trust of the user in the system (Dethise: paragraph [0049], “the TEE offers an execution space that provides a higher level of security for applications than a rich operating system and more functionality than a ‘secure element’ (SE); thus, increasing the trust of the user in the executing application”; paragraph [0058], “the training enclave implemented as a TEE may comprise computer code, which when executed by at least one processor, causes the training enclave, for example, to download the private data of the owner, receive the encrypted model”).
Re. claims 19-20, they recite analogous limitations as claims 2-3, respectively, and therefore are rejected for the same reasons.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seo in view of Dethise in further view of M L et al. (USP App Pub 2022/0414536; hereinafter ML).
Regarding claim 4, Seo and Dethise teach the edge device of claim 1, further comprising
an encryption unit for encrypting the modifying data (Seo: paragraph [0087], “the data encryption step (S200) may include a step (S210) of encrypting the original data”)
Seo and Dethise do not teach …in response to a read request for the modifying data by an external device.
However, in the same field of endeavor, ML does teach …in response to a read request for the modifying data by an external device (ML: paragraph [0050], “At 402, in the illustrated embodiment, client device 150 sends a request 160 (e.g., an HTTP request), to server system 102, to perform an operation 162 via the server system 102”; paragraph [0052], “At 408, in the illustrated embodiment, the server system 102 retrieves and generates (at least a portion of) the encrypted input data 142”; FIG. 1, Machine Learning Model 140 is run at Client Device 150).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the encryption step of Seo and Dethise to incorporate the teachings of ML to encrypt the input data in response to receiving a request from an external client device. The motivation for doing so is to allow for “running machine learning models at the client device may also improve the user experience by reducing delay” (ML: paragraph [0015]).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seo in view of Dethise in further view of Kyakuno et al. (USP App Pub 2019/0199511; hereinafter Kyakuno).
Regarding claim 5, Seo and Dethise teach the edge device of claim 1, further comprising…
Seo and Dethise do not teach …a sensor for providing the sensor data.
However, in the same field of endeavor, Kyakuno does teach …a sensor for providing the sensor data (Kyakuno: paragraph [0027], “The camera module 11 takes an image”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the image data received from a separate computing device in Seo and Dethise to incorporate the teachings of Kyakuno to use a camera as the separate computing device to provide image data. The motivation for doing so is to use an imaging device to produce image data (Kyakuno: paragraph [0034], “The camera module 11 includes an imaging device 31”).
Claim(s) 6, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seo in view of Dethise in further view of Chia et al. (USP App Pub 2014/0258731; hereinafter Chia).
Regarding claim 6, Seo and Dethise teach the edge device of claim 1, wherein the calculation stage (Seo: paragraph [0087], “the data encryption step (S200) may include a step (S210) of encrypting the original data, which is modified through the data transformation step (S110)”)…
Seo and Dethise do not teach …implements an AES (Advanced Encryption Standard) encryption in electronic code-book mode.
However, in the same field of endeavor, Chia does teach …implements (Chia: paragraph [0055], “The step S05 is to encrypt one of the unencrypted data blocks d.sub.1.about.d.sub.n according to the selected encryption program by each of the encryption elements 122 to generate an encrypted data”) an AES (Advanced Encryption Standard) encryption in electronic code-book mode (Chia: paragraph [0040], “the encryption algorithms can include…128 advanced encryption standard (128AES) … The encryption modes include…electronic codebook (ECB)”; Page 3, Table 1, selection #0 is AES combined with ECB).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the data encryption step of Seo and Dethise to incorporate the teachings of Chia to use an AES encryption algorithm in an ECB encryption mode. The motivation for doing so is to “increase the security and reliability of data transmission with higher encryption efficiency” (Chia: paragraph [0008]).
Regarding claim 10, Seo and Dethise teach the method of claim 7, wherein the sensor data is encrypted (Seo: paragraph [0087], “the data encryption step (S200) may include a step (S210) of encrypting the original data, which is modified through the data transformation step (S110)”) based on the modifying data (Seo: paragraph [0085], “the original data can be modified through other additional methods … by applying one or more preset masking elements to the original data such that a part of the original data can be masked by one or more preset masking elements”) …
Seo and Dethise do not teach …through AES (Advanced Encryption Standard) encryption in electronic code-book mode.
However, in the same field of endeavor, Chia does teach …through (Chia: paragraph [0055], “The step S05 is to encrypt one of the unencrypted data blocks d.sub.1.about.d.sub.n according to the selected encryption program by each of the encryption elements 122 to generate an encrypted data”) AES (Advanced Encryption Standard) encryption in electronic code-book mode (Chia: paragraph [0040], “the encryption algorithms can include…128 advanced encryption standard (128AES) … The encryption modes include…electronic codebook (ECB)”; Page 3, Table 1, selection #0 is AES combined with ECB).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the data encryption step of Seo and Dethise to incorporate the teachings of Chia to use an AES encryption algorithm in an ECB encryption mode. The motivation for doing so is to “increase the security and reliability of data transmission with higher encryption efficiency” (Chia: paragraph [0008]).
Claim(s) 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seo in view of Dethise in further view of Kulkarni et al. (USP App Pub 2020/0372394; hereinafter Kulkarni).
Regarding claim 16, Seo and Dethise teach the AI based classification system of claim 15, further comprising …
Seo and Dethise do not teach …an encryption unit configured to encrypt the modifying data prior to providing the modifying data to the external device.
However, in the same field of endeavor, Kulkarni does teach …an encryption unit (Kulkarni: paragraph [0068], “Data contributor 322”) configured to encrypt the modifying data prior to providing the modifying data (Kulkarni: paragraph [0068], “Data contributor 322 provides encrypts mask 326 using application 342's public key and provides encrypted mask 329 to application 342”) to the external device (Kulkarni: paragraph [0070], “Component 344 of application 342 decrypts each encrypted mask received, e.g., encrypted masks 329 and 339” i.e., application 342 receives the encrypted masks; paragraph [0073], “application 302 receives trained model 355 from application 342”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the artificial intelligence system of Seo and Dethise to incorporate the teachings of Kulkarni to encrypt the masks. The motivation for doing so is to mitigate a risk that data can be unmasked (Kulkarni: paragraph [0019], “The data contributor shares the encrypted random mask with analyst. So, neither the analyst nor the broker can unmask the data and get real user data”).
Regarding claim 17, Seo and Dethise teach the AI based classification system of claim 15, wherein
the modifying data stored in the protected memory (Dethise: paragraph [0049], “Trusted Execution Environment (TEE) is a secure area of a processor”; paragraph [0058], “the training enclave implemented as a TEE may comprise computer code”; paragraph [0071], “The training enclaves apply (314) their corresponding masks to their gradients to hide the actual gradients”; paragraph [0049], “It guarantees application code and data loaded inside to be protected with respect to confidentiality and integrity using specialized hardware” i.e., masks are used within the training enclave, therefore it is data loaded inside the TEE of the training enclave)…
Seo and Dethise do not teach …is received from a remote computer in an encrypted communication.
However, in the same field of endeavor, Kulkarni does teach …is received from a remote computer (Kulkarni: paragraph [0070], “Component 344 of application 342 decrypts each encrypted mask received, e.g., encrypted masks 329 and 339” i.e., application 342 receives the encrypted masks; paragraph [0073], “application 302 receives trained model 355 from application 342”) in an encrypted communication (Kulkarni: paragraph [0068], “Data contributor 322 provides encrypts mask 326 using application 342's public key and provides encrypted mask 329 to application 342”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the artificial intelligence system of Seo and Dethise to incorporate the teachings of Kulkarni to receive encrypted masks from a data contributor. The motivation for doing so is to mitigate a risk that data can be unmasked (Kulkarni: paragraph [0019], “The data contributor shares the encrypted random mask with analyst. So, neither the analyst nor the broker can unmask the data and get real user data”).
Regarding claim 18, Seo and Dethise teach the AI based classification system of claim 15, further comprising…
…associated with the edge device (Seo: claim 7, “A computing device for implementing a method for classifying encrypted data using a deep learning model”).
Seo and Dethise do not teach …a secure element configured to communicate with the external device to establish the modifying data…
However, in the same field of endeavor, Kulkarni does teach …a secure element (Kulkarni: FIGURE 3, Contributor Mask Decryption component 344) configured to communicate with the external device to establish (Kulkarni: paragraph [0070], “Component 344 of application 342 decrypts each encrypted mask received, e.g., encrypted masks 329 and 339”) the modifying data (Kulkarni: paragraph [0068], “Data contributor 322 provides encrypts mask 326 using application 342's public key and provides encrypted mask 329 to application 342”)…
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the artificial intelligence system of Seo and Dethise to incorporate the teachings of Kulkarni to receive encrypted masks from a data contributor. The motivation for doing so is to mitigate a risk that data can be unmasked (Kulkarni: paragraph [0019], “The data contributor shares the encrypted random mask with analyst. So, neither the analyst nor the broker can unmask the data and get real user data”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADITYA SRIRAM whose telephone number is (703)756-1715. The examiner can normally be reached M-Sa: 9:00 AM - 5:00 PM MST or PST.
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, William Korzuch can be reached at (571) 272-7589. 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.
/A.S./Examiner, Art Unit 2491
/WILLIAM R KORZUCH/Supervisory Patent Examiner, Art Unit 2491
Prosecution Insights