DETAILED ACTION
1. This is in response to the application No. 18/934,695 filed on 11/01/2024. Claims 1-8 are submitted for examination. Claims 1 and 5 are independent.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
3. This application filed on 11/01/2024 claims foreign priority to 10-2023-0150206, filed on 11/02/2023 and claims foreign priority to 10-2024-0137998, filed on 10/10/2024.
Information Disclosure Statement
4. No information disclosure statements (IDS) is submitted for this application.
Drawings
5. The drawings filed on November 1st, 2024 are accepted.
Specification
6. The specification filed on November 1st, 2024 is objected to because of the following informalities:
Applicant’s published specification, Pub. No. 2025/0150256 on paragraph 0058 and referring to figure 3, recites the following: “Each of the reference AI model 30 and the plaintext AI model 300 may output an output value corresponding to the input data (S320).”
However, this is contrary to what is stated earlier in paragraph 0057 and the corresponding figure 3, where the input data is designated as S310 not S320.
Thus, paragraph 0058 should be corrected as, “Each of the reference AI model 30 and the plaintext AI model 300 may output an output value corresponding to the input data (S310).”
Appropriate correction is required.
Claim Objections
7. Dependent claim 3 and 7 are objected to because of the following informalities: claim 3 and claim 7, at the last two lines, recites the following underlined limitation: “a model having
a optimized input distribution compared to the reference AI model, or a polynomial neural network”.
It should be corrected as “a model having an optimized input distribution compared to the reference AI model, or a polynomial neural network”. Appropriate correction is required.
8. Dependent claim 8 depends on dependent claim 7. Thus, carries the deficiencies of the above dependent claim 7 and is likewise objected.
Appropriate correction is required.
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.
Independent claims 1 and 5 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. Independent claims 1 and 5 recites the following underlined claim limitation or term that are ambiguous and unclear, “AI model friendly to homomorphic encryption” The term “friendly” is unclear in its scope and the scope of the claim can’t be determined. The scope of the term has to be defined.
Appropriate correction is required.
Independent claims 1 and 5 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. Independent claims 1 and 5 recites the following underlined claim limitation that are ambiguous and unclear, “…designed to operate homomorphic encryption efficiently compared…” The term “efficiently” is unclear in its scope and the scope of the claim can’t be determined. The scope of the term has to be defined.
Appropriate correction is required.
Dependent claims 2-4 and 6-8 that depends on the above independent claims 1 and 5 respectively, carry the deficiencies of the above parent independent claims 1 and 5 and are likewise rejected.
Claim Rejections - 35 USC § 103
10. 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
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.
11. Independent claims 1 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Sarpatwar et al (Sarpatwar), (US Publication No. 2021/0397988 A1, Pub. Date: Dec. 23, 2021) in view of Desai et al (Desai)(US Publication No. 2020/0311573 A1, Pub. Date: Oct. 1, 2020) and in further view of Joye et al (Joye) (US Publication No. 20220247551A1, Aug. 4, 2022 )
The following is referring to independent claims 1 and 5:
As per independent claim 1, Sarpatwar discloses a server device [Figure 1, ref. 104, “Server” and figure 2, ref. data processing system 200, para. 0023, With reference now to FIG. 2, a block diagram of a data processing system is shown in which illustrative embodiments may be implemented. Data processing system 200 is an example of a computer, such as server 104] comprising:
a communicator [Para. 0023-0024, Figure 2, ref. 210, “Communications unit 210” and a communication fabric 202]; a memory [Figure 2, ref. 206, “memory 206”]; and a processor [Figure 2, ref. 204, “Processor unit 204”, Para. 0023-0024, “data processing system 200 includes communications fabric 202, which provides communications between processor unit 204, memory 206, persistent storage 208, communications unit 210, input/output (I/O) unit 212, and display 214”] wherein the processor is configured to
receive a reference artificial intelligence (AI) model [Para. 0006, a method to create a full homomorphic encryption (FHE)-compatible machine learning model begins by obtaining a first (teacher) machine learning model, the first machine learning model having been pre-trained using first training data. The “teacher machine learning model” that is obtained or received corresponds to the limitation, “a reference artificial intelligence (AI) model” and see also claim 8, ‘an apparatus comprising processor, … obtain a first machine learning model, the first machine learning model having been pre-trained using first training data;] and ,
acquire a plaintext AI model friendly to homomorphic encryption [Abstract, “create a full homomorphic encryption (FHE)-friendly machine learning model FHE-friendly model”, The full homomorphic encryption (FHE)-friendly machine learning model corresponds to the claim limitation, “a plaintext AI model friendly to homomorphic encryption”] by performing a knowledge distillation task based on the reference AI model [Abstract, knowledge distillation framework wherein the FHE-friendly (student) ML model closely mimics the predictions of a more complex (teacher) model, wherein the teacher model is one that, relative to the student model, is more complex and that is pre-trained on large datasets. In the approach herein, the distillation framework uses the more complex teacher model to facilitate training of the FHE-friendly model, but using synthetically-generated training data in lieu of the original datasets used to train the teacher.] for a lightweight AI model [Para. 0006, “wherein the teacher model is one that, relative to the student model, is more complex and that is pre-trained on large datasets” This indicates that the student model is a lightweight AI model compared to the more complex teacher model] designed to operate homomorphic encryption efficiently compared to the reference AI model [Abstract, knowledge distillation framework wherein the FHE-friendly (student) ML model closely mimics the predictions of a more complex (teacher) model, wherein the teacher model is one that, relative to the student model, is more complex and that is pre-trained on large datasets. In the approach herein, the distillation framework uses the more complex teacher model to facilitate training of the FHE-friendly model, but using synthetically-generated training data in lieu of the original datasets used to train the teacher. This implies that the FHE-friendly (student) ML model is more efficient compared to the complex teacher/reference AI model], and
Sarpatwar doesn’t explicitly disclose the following underlined claim limitation: ”receive a reference artificial intelligence (AI) model of an external device through the communicator and store the received reference AI model in the memory”
However, Desai explicitly discloses the above underlined claim limitation:
“receive a reference artificial intelligence (AI) model of an external device through the communicator [Par. 0030 teaches how a cloud resource predication platform/server receives a model or a training model from another device, “rather than training a model, the cloud resource prediction platform may receive a model from another device (e.g., a server device). For example, a server device may generate a model based on having trained the model in a manner similar to that described above and may provide the model to the cloud resource prediction platform”] and store the received reference AI model in the memory,[Para. 0030, also teaches loading the learning model/AI model into the platform that implies that the received model is retained or stored by the platform, “rather than training a model, the cloud resource prediction platform may receive a model from another device (e.g., a server device). For example, a server device may generate a model based on having trained the model in a manner similar to that described above and may provide the model to the cloud resource prediction platform (e.g., may pre-load the cloud resource prediction platform with the model”]
Sarpatwar and Desai are analogous and are in the same field of endeavor as they both are directed to utilizing a machine learning or AI model
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarpatwar by implementing a mechanism such as “receive a reference artificial intelligence (AI) model of an external device through the communicator and store the received reference AI model in the memory” as per teachings of Desai for the purpose of training and generating a trained machine learning model, and responding to the customer’s request and performing one or more actions based on the projection. [Para. 0003, Desai, the method may include training a machine learning model…to generate a trained machine learning model, and receiving a request for new resource usage by a customer associated with the cloud computing environment. The method may include processing the request for the new resource usage, with the trained machine learning model, to generate …and performing one or more actions based ..]
Furthermore, Sarpatwar discloses how a more complex teacher, reference/plaintext AI model is generally converted into a full homomorphic encryption (FHE)-friendly student machine learning model [Abstract, “to create a full homomorphic encryption (FHE)-friendly machine learning model. The approach herein leverages a knowledge distillation framework wherein the FHE-friendly (student) ML model closely mimics the predictions of a more complex (teacher) model, wherein the teacher model is one that, relative to the student model, is more complex and that is pre-trained on large datasets. In the approach herein, the distillation framework uses the more complex teacher model to facilitate training of the FHE-friendly model”]
However, the combination of Sarpatwar and Desai doesn’t explicitly disclose the following underlined claim limitation: “convert the plaintext AI model into a homomorphic encryption AI model by encrypting data used by the plaintext AI model”
Joye discloses the above underlined claim limitation: “convert the plaintext AI model [Para. 0322, A server may store a set of Machine Learning model” the machine learning model corresponds to the plaintext AI model] into a homomorphic encryption AI model by encrypting data used by the plaintext AI model [Para. 0032“The server may encrypt the model parameter vector by encrypting each of the components of the model parameter vector using an additively homomorphic encryption algorithm” Examiner Note: model parameters meets the data used by the plaintext AI model, Encrypting the model parameter vector under a homomorphic encryption algorithm converts the plaintext model parameter into an encrypted representation usage under Homomorphic encryption and this meets the limitation, “convert the plaintext AI model into a homomorphic encryption AI model by encrypting data used by the plaintext AI model”]
Sarpatwar, Desai and Joye are analogous and are in the same field of endeavor as they all are directed to utilizing a machine learning or AI model.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarpatwar and Desai by adding a mechanism such as “convert the plaintext AI model into a homomorphic encryption AI model by encrypting data used by the plaintext AI model” as per teachings of Joye in order to preserve and protect the secrecy of the parameters of the Machine Learning models and the privacy of the input data fed to the Machine Learning model. [See at least Joye, abstract, “Methods and systems are provided for evaluating Machine Learning models in a Machine-Learning-As-A-Service context, whereby the secrecy of the parameters of the Machine Learning models and the privacy of the input data fed to the Machine Learning model are preserved as much as possible]
As per independent claim 5, independent claim 5 is method version of the device claim 1 and has a similar scope as that of the above independent claim 1. Thus, claim 5 is rejected for the same reason/rationale as that of the above independent claim 1.
12. Claims 2-3 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Sarpatwar et al (Sarpatwar), (US Publication No. 2021/0397988 A1, Pub. Date: Dec. 23, 2021) in view of Desai et al (Desai)(US Publication No. 2020/0311573 A1, Pub. Date: Oct. 1, 2020) and in further view of Joye et al (Joye) (US Publication No. 20220247551A1, Aug. 4, 2022 ) and further in view of Geoffrey Hinton et al (Hinton) (NPL document titled, “Distilling the Knowledge in a Neural Network” , March 9, 2015)
The following is referring to dependent claims 2-3 and 6-7:
As per dependent claim 2, the combination of Sarpatwar, Desai and Joye discloses the method or the device as applied to claim 1 above. Furthermore, Sarpatwar discloses the method/device, wherein the processor is configured to input the same learning data to the reference AI model and the lightweight AI model [Figure 5 and para. 0079-0080, “The transfer data set 507 is generated, preferably randomly, using a given input data “and “the student model 504 are learned using, as “synthetic” training data, the transfer data set 507, and predictions of the teacher model 500 on that transfer data set. As shown on figure 5 and para. 0079-0080, the transfer data generated from input distribution. The same transfer data is provided to the teacher model to produce predictions and the same transfer data is provided to the student model during training. This means the same learning data is input during the training].
comparing para. 0080, ” the student model strives to minimize the loss between the predictions of the teacher and student models” This teaches comparison between teacher and student outputs and loss is defined between them]
The combination of Sarpatwar, Desai and Joye doesn’t disclose the following underlined claim limitation, “acquire a distillation loss by comparing at least one of embedding vectors, logits, or class values, respectively output from the reference AI model and the lightweight AI model, and acquire the plaintext AI model by a training process, feeding back the distillation loss to the lightweight AI model multiple times.
However, Hinton discloses the above underlined claim limitation:
comparing at least one of embedding vectors, logits, or class values, respectively output from the reference AI model and the lightweight AI model, [Page 2, “using the logits (the inputs to the final softmax) …as the targets and ….minimize the squared difference between the logits produced by the cumbersome model and the logits produced by the small model.” And page 2, “use the class probabilities …as “soft targets” for training the small model” This teaches comparing the teacher logits vs student logits] and acquire the plaintext AI model by a training process, feeding back the distillation loss to the lightweight AI model multiple times.[Page 3, “knowledge is transferred to the distilled model by training it/distilled model ….using a soft target distribution”, “The first objective function is the cross entropy with the soft targets”, page 3, 2.1, “Each case in the transfer set contributes a cross-entropy gradient…with respect to each logit…of the distilled model. Note: Cross-entropy gradient with respect to logits is explicit loss feedback used to update the student during training. Training by gradient is iterative across many cases/steps (i.e. the loss is fed back “multiple times” On page 4, “The model is trained with a distributed stochastic gradient descent” This is iterative training or multiple iterations]
Furthermore, Hinton discloses “to input the same learning data to the reference AI model and the lightweight AI model” [Abstract, “A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions”]
Sarpatwar, Desai Joye and Hinton are analogous and are in the same field of endeavor as they all are directed to utilizing a machine learning or AI model.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sarpatwar, Desai and Joye by adding a mechanism such as “comparing at least one of embedding vectors, logits, or class values, respectively output from the reference AI model and the lightweight AI model, and acquire the plaintext AI model by a training process, feeding back the distillation loss to the lightweight AI model multiple times” as per teachings of Hinton in order to improve the performance of almost any machine learning algorithm and in order to train the model rapidly and in parallel.. [See at least Hinton, abstract, “improve the performance of almost any machine learning algorithm and these specialist models can be trained rapidly and in parallel”]
As per dependent claim 6, dependent claim 6 is method version of the device claim 2 and has a similar scope as that of the above dependent claim 2. Thus, claim 6 is rejected for the same reason/rationale as that of the above dependent claim 2.
As per dependent claim 3, the combination of Sarpatwar, Desai and Joye disclose the method or the device as applied to claim 2 above. Furthermore, Sarpatwar discloses the method/device, wherein the, wherein the reference AI model is the plaintext model that is pre-trained [para. 0006. “the teacher model is one that, relative to the student model, is more complex and that is pre-trained on large datasets”, “by obtaining a first (teacher) machine learning model, the first machine learning model having been pre-trained using first training data”] includes one of a model having a reduced number of layers or parameters compared to the reference AI model [Para. 0006, “The second machine learning model, which is depth-constrained. Para. 0099, “preferably a shallow neural network is employed as the student model architecture”; “the teacher model is one that, relative to the student model, is more complex”, meaning the student model is less complex. Note: This directly corresponds to reduced layer; lower complexity and shallow architecture]and easily performing an operation in a homomorphic encryption state [Abstract, “ product to create a full homomorphic encryption (FHE)-friendly machine learning model. The approach herein leverages a knowledge distillation framework wherein the FHE-friendly (student) ML model closely mimics the predictions of a more complex (teacher) model, wherein the teacher model is one that, relative to the student model, is more complex and that is pre-trained on large datasetsPara 0082, “Given a multiplicative depth budget, compute the set of configurations of neural networks, along with polynomial activation functions that satisfy the depth budget” and para. 0099, “polynomial approximation of activation functions] .
As per dependent claim 7, dependent claim 7 is method version of the device claim 3 and has a similar scope as that of the above dependent claim 3. Thus, claim 7 is rejected for the same reason/rationale as that of the above dependent claim 3.
Allowable Subject Matter
13. Claims 4 and 8 are objected to as being dependent upon a rejected base claims 3 and 7 respectively, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
14 The following is an examiner’s statements of reasons for allowance:
The above prior arts of record including the rest of the cited prior arts neither anticipates nor renders obvious the claimed subject matter of the instant application that is taken as a whole including the following specific claim limitation recited in dependent claim 4 and a similar claim recited in dependent claim 8: “wherein the processor is configured to convert the plaintext AI model into the homomorphic encryption AI model by performing: a task of defining at least one parameter used by the homomorphic encryption AI model, a packing task of merging the data used by the plaintext AI model into an encrypted form, a task of determining the type, order, number of times, and structure of an operation performed by the homomorphic encryption AI model, a task of securing a storage for storing at least one key used for the homomorphic encryption and ciphertext, a polynomial approximation task of approximating a nonlinear operation into a polynomial, a task of adjusting a trade-off relationship between the degree and precision of each polynomial used in the polynomial approximation task, a task of adjusting an input distribution of a function used in the polynomial approximation task, a task of adjusting an approximation error in a process of operating the polynomial, and a task of adjusting a homomorphic encryption operation to be performed by a graphic processing unit (GPU)”
For this reason, the specific claim limitations recited in the dependent claims 4 and 8 taken as whole would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
15. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A. US Patent No. 11551035 B2 Schneider et al discloses a method for evaluating data is based on a computational model, the computational model comprising model data, a training function and a prediction function. The method includes training the computational model by: receiving training data and training result data for training the computational model, and computing the model data from the training data and the training result data with the training function. The method includes predicting result data by: receiving field data for predicting result data; and computing the result data from the field data and the model data with the prediction function. The training data may be plaintext and the training result data may be encrypted with a homomorphic encryption algorithm, wherein the model data may be computed in encrypted form from the training data and the encrypted training result data with the training function. The field data may be plaintext, wherein the result data may be computed in encrypted form from the field data and the encrypted model data with the prediction function.
B. US Publication No. 20200036510 A1 to Gomez et al discloses deploying an encrypted neutral network model. Encrypting input data with an FHE public key and performing inference using encrypted parameters while approximating activation function into polynomials to operate in the encrypted domain.
C. US Publication No. 20200242466 A1 to Mohassel et al discloses privacy preserving machine learning where a neural network trained on plaintext data can make predication on encrypted data using fully homomorphic encryption with the model held by one party and evaluated on another party’s private inputs.
D. US Publication No. 20210081203 A1 to Vald et al discloses homomorphic
encryption (FHE) ciphertext processing using approximate polynomials (e.g., for modulus reduction/bootstrapping), including calculating error and adjusting
polynomial degree/samples to improve the approximation used during homomorphic encryption (HE) evaluation.
E. See other cited prior arts.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMSON B LEMMA whose telephone number is 571-272-3806. The examiner can normally be reached on M-F 8am-10pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Yin-Chen Shaw can be reached on 571-272-8878. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SAMSON B LEMMA/Primary Examiner, Art Unit 2498