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 .
Claim Rejections - 35 USC § 101
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1
Step 1, This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a computer system to perform at least one step or act. Thus, the claim is to a machine, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A – Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Step “segment the input data into patches of variable length” This step is nothing more to observations, evaluations, judgments to split input data (e.g., splitting text into sentence, paragraph, image from text, and so on). This step can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea).
Step “encode the patches into latent representations” This step simply represents complex data (e.g., patches) to shorthand/symbol. This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea).
Step “process the latent representations using a deep learning core without decoding of the latent representations” (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (e.g., a deep learning core). That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about processing shorthand/symbol. The “without decoding” is a negative limitation that describe the absence of a step. (e.g., . Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment)
Step “generate output data based on the processed latent representations” is simply generate a conclusion based on analysis is a fundamental building block of human thought. This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea)
Step “wherein computational resources are dynamically allocated based on characteristics of the input data” This step simply adding additional workers if the job is complex. This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea)
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the mentioned limitations/steps fall within the mental process grouping of abstract ideas and are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements/limitations: a computer system, a hardware memory, nontransitory machine-readable storage media, receive input data from a plurality of client devices
a) MPEP § 2106.05(a) "Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field."
There is no improvement to Functioning of a Computer or to Any Other Technology or Technical Field. The limitation “receive input data from a plurality of client devices [Wingdings font/0xF3] data gathering”. The limitation does not make any improvements to the functionalities of a computer, database technology, or any other technologies.
b) MPEP § 2106.05(b) Particular Machine. The judicial exception does not apply to any particular machine.
The claim is silent regarding specific limitations directed to an improved computer system, processor, memory, network, database, or Internet, nor do applicant direct examiner’s attention to such specific limitations. "[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 573 U.S. at 223; see also Bascom Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1348 (Fed. Cir. 2016) ("An abstract idea on 'an Internet computer network' or on a generic computer is still an abstract idea."). Applying this reasoning here, the claim is not directed to a particular machine, but rather merely implement an abstract idea using generic computer components such as a computer system, a hardware memory, nontransitory machine-readable storage media. Thus, the claims fail to satisfy the "tied to a particular machine" prong of the Bilski machine-or-transformation test.
c) MPEP § 2106.05(c) Particular Transformation.
The claim operates to collecting data and displaying calculated output. The steps are not a "transformation or reduction of an article into a different state or thing constituting patent-eligible subject matter[.]" See In re Bilski, 545 F.3d 943, 962 (Fed. Cir. 2008) (en bane), aff'd sub nom, Bilski v. Kappas, 561 U.S. 593 (2010); see also CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) ("The mere manipulation or reorganization of data ... does not satisfy the transformation prong."). Applying this guidance here, the claims fail to satisfy the transformation prong of the Bilski machine-or-transformation test.
d) MPEP § 2106.05(e) Other Meaningful Limitations.
This section of the MPEP guides: Diamond v. Diehr provides an example of a claim that recited meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. 450 U.S. 175, ... (1981). In Diehr, the claim was directed to the use of the Arrhenius equation ( an abstract idea or law of nature) in an automated process for operating a rubber-molding press. 450 U.S. at 177-78 .... The Court evaluated additional elements such as the steps of installing rubber in a press, closing the mold, constantly measuring the temperature in the mold, and automatically opening the press at the proper time, and found them to be meaningful because they sufficiently limited the use of the mathematical equation to the practical application of molding rubber products. 450 U.S. at 184... In contrast, the claims in Alice Corp. v. CLS Bank International did not meaningfully limit the abstract idea of mitigating settlement risk. 573 U.S._ .... In particular, the Court concluded that the additional elements such as the data processing system and communications controllers recited in the system claims did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers") or were well-understood, routine, conventional activity. MPEP § 2106.05(e).
The limitations “receive input data from a plurality of client devices [Wingdings font/0xF3] data gathering” is not meaningful limitation because it is pre-solution activity. The limitation is not meaningful limitation.
e) MPEP § 2106.05(g) Insignificant Extra-Solution Activity.
The limitation “receive input data from a plurality of client devices [Wingdings font/0xF3] data gathering” is not meaningful limitations because it is pre-solution activity.
6) MPEP § 2106.05(h) Field of Use and Technological Environment.
[T]he Supreme Court has stated that, even if a claim does not wholly pre-empt an abstract idea, it still will not be limited meaningfully if it contains only insignificant or token pre- or post-solution activity-such as identifying a relevant audience, a category of use, field of use, or technological environment. Ultramercial, Inc. v. Hulu, LLC, 722 F.3d 1335, 1346 (Fed. Cir. 2013). Limitations “a computer system, a hardware memory, nontransitory machine-readable storage media” are simply a field of use that attempts to limit the abstract idea to a particular technological environment.
Accordingly, the additional limitation “receive input data from a plurality of client devices [Wingdings font/0xF3] data gathering” does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim “receive input data from a plurality of client devices [Wingdings font/0xF3] data gathering” does not recite any non-convention or non-generic arrangement. Taking the limitation as an ordered combination adds nothing that is not already present when the elements are taken individually. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 2 recites “wherein segmenting the input data into patches of variable length comprises analyzing information density within the input data” This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea)
Claim 3 recites “wherein analyzing information density comprises calculating entropy values for portions of the input data” This step of calculating entropy values for portions of the input data is nothing more than mathematical process (i.e., mathematical concept [Wingdings font/0xF3] abstract idea)
Claim 4 recites “wherein the input data comprises byte sequences, and the system allocates more computational resources to high-entropy regions of the byte sequences and fewer computational resources to low-entropy regions” This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea)
Claim 5 recites “wherein encoding the patches comprises: generating initial representations of elements within each patch; capturing contextual patterns from sequences of elements; and using an attention mechanism to pool element-level representations into patch-level representations” This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea). Further, “attention mechanism” is used as a tool to perform the abstract ideas
Claim 6 recites “wherein capturing contextual patterns comprises using hash-based embeddings of element sequences of varying lengths” Hashed-based embedding is mathematical mapping used to represent data into a vector space. This step is nothing more than mathematical process (i.e., mathematical concept [Wingdings font/0xF3] abstract idea)
Claim 7 recites “wherein the deep learning core comprises a transformer architecture that processes the latent representations without requiring fixed-vocabulary tokenization of the input data” Transformer architecture is purely mathematical operations. This step is nothing more than mathematical process (i.e., mathematical concept [Wingdings font/0xF3] abstract idea)
Claim 8 recites “encrypt the latent representations using homomorphic encryption before transmission; process the encrypted latent representations without decoding; aggregate encrypted model updates from the plurality of client devices; and update the deep learning core based on the aggregated encrypted model updates” Homomorphic encryption is a specific of complex mathematical algorithm. Encryption and decryption are abstract mathematical concepts.
Claim 9 recites “wherein the instructions further cause the system to implement privacy-enhancing techniques to the encrypted model updates to prevent extraction of client device information” This step is simply protecting sensitive data (e.g., redacting) and nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea)
Claim 10 recites “wherein the system dynamically modifies patch sizes based on available computational resources while maintaining prediction accuracy” This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea)
Claim 11 recites “wherein the system is initialized using parameters from a pre-trained model and subsequently optimized for byte-level processing” The step of “initialized” and “optimized” simply setting values to variables for the pre-trained model. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claims 12-20 are similar to claims 1-9. The claims are rejected based on the same reasons.
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.
Claim(s) 1-3, 5-7, 11, 12-14, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Li (U.S. Pub 2023/0397172), in view of Tutuianu (U.S. Patent 10715387 B1)
Claim 1
Li discloses a computer system comprising (fig. 9):
a hardware memory (fig. 9 memory 930), wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media comprising software instructions that cause the system to ([0249], non-transitory computer storage):
receive input data from a plurality of client devices ([0078], “… each UE 115 may be triggered for local training… Based on the triggering of the local training, each UE 115 may generate a local dataset 315 (e.g., based on one or more channel measurements made at the UE 115, one or more beam measurements made at the UE 115, prior successful or unsuccessful decoding of communications at the UE 115, etc.);
segment the input data into patches of variable length ([0078], “… Based on the local dataset each UE 115 may determine a local stochastic gradient 320 (e.g., g1 through gk)…” [0080], “… In this example, g 405 is a vector of local stochastic gradients (SGs), and a vectorf 420 is determined where f=g/∥g∥ is the normalized vector of SGs. The vector f may be partitioned into M blocks 425 each with length-L such that f=[v1 T, v2 T, . . . , vM T]T, where the length-L vector vi is referred to as the i-th block gradients. Zero-padding might be used if LM>Dim(f). Further, normalized block gradients may be defined as si=vi/∥vi∥, and a hinge vector h 430 may be defined as h=[h1, . . . , hM]T≙[∥vv∥, . . . , ∥vM∥]T…”);
encode the patches (local stochastic gradient) into latent representations (quantized stochastic gradient) ([0080], “… using a block-wise vector quantization technique to generate a quantized stochastic gradient (ĝ) 450 that is reported to a base station …”);
process the latent representations using a deep learning core (global model) without decoding of the latent representations ([0086], “… Based on the configuration information, the UE (or other edge device) may report block-quantized gradients to the base station…” [0075], “… The base station 105-a, based on the set of compressed gradient vectors 225, may update a global model 235 of the machine learning algorithm (e.g., NN autoencoder) …” [0078], “… the UEs 115 may report the parameters as distributed gradient updates with compressed gradient vectors that are determined based on a quantizer 325 function at each UE 115. The base station 105-b may aggregate the received local models or gradients at gradient averaging function 330, and the aggregated information provided to a global model update 335 function. The aggregation may be, for example, parameter/gradient averaging, or other aggregation of local information as part of a federated learning procedure. Upon updating the global model, the base station 105-b provide an updated global model or NN-weights to the UEs 115…” <examiner note: The global model is updated based on the received quantized stochastic gradient without decoding them back to local stochastic gradient or original local dataset>); and
generate output data based on the processed latent representations ([0078], “… Upon updating the global model, the base station 105-b provide an updated global model or NN-weights to the UEs 115…”;
However, Li does not explicitly disclose wherein computational resources are dynamically allocated based on characteristics of the input data.
Tutuianu discloses wherein computational resources are dynamically allocated based on characteristics of the input data (col 7 line 18-20, “… A traffic prediction module 112… may receive traffic data… indicate a quantity data received by the host devices…” col 8, line 12-18, “… a quantity modules 124… may determine a quantity of host devices… quantity of processing units, to be used to process a predicted quantity of data… specifically, the provisioning module 124 may determine a quantity of target resources 126 to be used by the host devices…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to a traffic prediction module that determine quantities of data received by host devices to generate traffic prediction as disclosed by Tutuianu into Li so that when the base station receives enormous data from edge device, the traffic prediction module can “dynamically controlling a number of computing devices used to process requests and other types of data, in advance of a predicted increase or decrease in the amount of data received”
Claim 2
Claim 1 is included, Li discloses wherein segmenting the input data into patches of variable length comprises analyzing information density within the input data ([0080] “…a UE (or other edge device) may determine a local stochastic gradient (g) 405 that may be quantized using a block-wise vector quantization technique to generate a quantized stochastic gradient (ĝ) 450 that is reported to a base station (or other edge server). In some cases, quantizers that may be used in such techniques can compress the gradients for transmission via Grassmannian quantizers for normalized vectors. In this example, g 405 is a vector of local stochastic gradients (SGs), and a vectorf 420 is determined where f=g/∥g∥ is the normalized vector of SGs. The vector f may be partitioned into M blocks 425 each with length-L such that f=[v1 T, v2 T, . . . , vM T]T, where the length-L vector vi is referred to as the i-th block gradients. Zero-padding might be used if LM>Dim(f). Further, normalized block gradients may be defined as si=vi/∥vi∥, and a hinge vector h 430 may be defined as h=[h1, . . . , hM]T≙[∥vv∥, . . . , ∥vM∥]T…”)
Claim 3
Claim 2 is included, Li disclose wherein analyzing information density comprises calculating entropy values for portions of the input data. ([0080] “…a UE (or other edge device) may determine a local stochastic gradient (g) 405 that may be quantized using a block-wise vector quantization technique to generate a quantized stochastic gradient (ĝ) 450 that is reported to a base station (or other edge server). In some cases, quantizers that may be used in such techniques can compress the gradients for transmission via Grassmannian quantizers for normalized vectors. In this example, g 405 is a vector of local stochastic gradients (SGs), and a vectorf 420 is determined where f=g/∥g∥ is the normalized vector of SGs. The vector f may be partitioned into M blocks 425 each with length-L such that f=[v1 T, v2 T, . . . , vM T]T, where the length-L vector vi is referred to as the i-th block gradients. Zero-padding might be used if LM>Dim(f). Further, normalized block gradients may be defined as si=vi/∥vi∥, and a hinge vector h 430 may be defined as h=[h1, . . . , hM]T≙[∥vv∥, . . . , ∥vM∥]T…”)
Claim 5
Claim 1 is included, Li further discloses wherein encoding the patches comprises: generating initial representations of elements within each patch; capturing contextual patterns from sequences of elements; and using an attention mechanism to pool element-level representations into patch-level representations ([0081], “… Quantization may be determined using Bρ-bits to quantize the norm of the local SGs, such that ρ=∥g∥ (as indicated at 410), by a scalar quantizer 415 (Cρ). The scheme may use Bs-bits to quantize each normalized block gradient (i.e., si), by a uniform and even Grassmannian quantizer 440 (Cs). A Grassmannian quantizer refers to a quantizer comprising codewords representing unit norm vectors, where an even quantizer codebook is provided such that C:C=C + ∪C −, with C + ∩C −=∅ and −c∈C − , ∀c∈C +. Quantization of h may use Bh-bits to quantize each hinge vector by a positive Grassmannian quantizer 435 (Ch). The positive quantizer codebook may include codewords within a positive codebook represent only positive-entry vectors…”)
Claim 6
Claim 5 is included, Li discloses wherein capturing contextual patterns comprises using hash-based embeddings of element sequences of varying lengths ([0082], “… The quantization may provide a payload size that includes a total of, Bρ+MBs+Bh bits to quantize the vector g. The quantized version of g is {circumflex over (g)}={circumflex over (ρ)}[{circumflex over (f)}1 T, . . . , {circumflex over (f)}M T]={circumflex over (ρ)}[{circumflex over (h)}1{circumflex over (s)}1 T, . . . , {circumflex over (h)}M{circumflex over (s)}M T] where {circumflex over (ρ)}, ĥi and ŝi are quantized versions of ρ, hi, and si, respectively that are combined at combining function 445. For a given bit allocation {Bρ, Bs, Bh} and partitioning scheme {M, L}, codebooks {Cρ, Cs, Ch} may to be optimized to minimize estimation MSE between g 405 and ĝ 450. In some cases, the design of the scalar quantizer 415 codebook Cρ may be a uniform scaler quantizer; the design of the Grassmannian quantizer 440 codebook Cs…”)
Claim 7
Claim 1 is included, Li discloses wherein the deep learning core comprises a transformer architecture that processes the latent representations without requiring fixed-vocabulary tokenization of the input data ([0082], “… . In some cases, the design of the scalar quantizer 415 codebook Cρ may be a uniform scaler quantizer; the design of the Grassmannian quantizer 440 codebook Cs…”)
Claim 11
Claim 1 is included, Li discloses wherein the system is initialized using parameters from a pre-trained model and subsequently optimized for byte-level processing. ([0085], “… the UE (or other edge device) may be configured with a number of bits that are to be included in a report to the base station (or other edge server) that indicate one or more of UE determined partitioning parameters, UE determined quantizer codebooks, a UE determined bit allocation scheme, or any combinations thereof. The number of bits, in some cases, may be explicitly configured or indicated in the configuration information from the base station, or may be at least partly identified implicitly based on a quantity of the scheduled UL resources for reporting the quantized gradients. Further, the orders of the bits within the report payload for different purposes (e.g., UE determined partitioning/codebook/bit-allocation, payloads of the bits quantized by different quantizers) may be RRC configured or predetermined (e.g., according to a standard or specification associated with a radio access technology). The UE may receive the configuration information, or one or more portions of the configuration information, separately for each of a group of one or more rounds of gradient reports. In other cases, the UE may receive the configuration information for an overall training task that spans multiple groups of one or more rounds of gradient reporting…)
Claims 12-14, and 16-18 are similar to claims 1-3 and 5-7. The claims are rejected based on the same reasons.
Claim(s) 4, 10, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Li (U.S. Pub 2023/0397172), in view of Tutuianu (U.S. Patent 10715387 B1), as applied to claim1 and 12 respectively, and further in view of Abdelwahed (U.S. Pub 2024/0235727 A1)
Claim 4
Claim 1 is included, however, Li does not disclose wherein the input data comprises byte sequences, and the system allocates more computational resources to high-entropy regions of the byte sequences and fewer computational resources to low-entropy regions.
Abdelwhed discloses wherein the input data comprises byte sequences, and the system allocates more computational resources to high-entropy regions of the byte sequences and fewer computational resources to low-entropy regions ([0025] FEC data. For example, bit allocator 216 may allocate more bits to high entropy data that is harder to predict, and less bits to data that is low entropy such as silent data portions. Accordingly, FEC data associated with silent data portions may be allocated fewer bits within a packet as low bitrate FEC data may be sufficient to recreate such silent data in the event the packet that initially sent the silent data is lost. On the other hand, if the current data being sent is for a silent audio portion and one or more of the FEC data portions being sent are for high entropy data, bit allocator 216 may allocate more bits to the one or more FEC portions than to the current data portion. The logic implemented through bit allocator 216, particularly for use with neural audio codecs, may be built using self-attention layers with positional embeddings or convolutional layers…)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate bit allocator as disclosed by Abdelwhed into Li so that when the data is transmitted from the edge devices to the base station such that the bit allocation strategy may be dynamically altered based upon, for example, the position and context of both the current data and the FEC data. Based on the current data and the FEC data, bit allocator 216 may determine how many bits to allocate each data packet, as well as how many bits within each packet to allocate to the current data
Claim 15 is similar to claim 4. The claim is rejected based on the same reason.
Claim 10
Claim 1 is included, Abdelwhed discloses wherein the system dynamically modifies patch sizes based on available computational resources while maintaining prediction accuracy ([0026], “… Once quantized, data packets are provided from transmit side 205 to receive side 250 via network 244. Upon obtaining the packet at receive side 250, dequantizer 255 dequantizes the packet. If a single quantizer was used to quantize the packet, a single dequantizer may be used to dequantize the packet. If multiple quantizers were used to quantize the packet, multiple dequantizers may be used to dequantize the packet. For example, if one of the FEC portions contained within the packet was quantized using one or more quantizers that differ from the quantizer used to quantize the current data portion, a corresponding number of dequantizers may be used to dequantize the FEC portions of the packet at receive side 250. Dequantizer 255 decompresses the data contained within the packet using codebook 220 a, which is a receive side version of codebook 220 of transmit side 205. The uncompressed encoded data is provided to jitter buffer 260, which collects encoded data from multiple packets so that it can be processed…”)
Claim(s) 8-10 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Li (U.S. Pub 2023/0397172), in view of Tutuianu (U.S. Patent 10715387 B1), as applied to claim 1 and 12 respectively, and further in view of OTT (U.S. Pub 2025/0023706 )
Claim 8
Claim 1 is included, Li does not explicitly disclose wherein the instructions further cause the system to: encrypt the latent representations using homomorphic encryption before transmission; process the encrypted latent representations without decoding; aggregate encrypted model updates from the plurality of client devices; and update the deep learning core based on the aggregated encrypted model updates.
Ott discloses encrypt data using homomorphic encryption before transmission; process the encrypted latent representations without decoding; aggregate encrypted model updates from the plurality of client devices; and update the deep learning core based on the aggregated encrypted model updates ([0028], “… the multiple endpoints that send homomorphically encrypted data to an aggregation device may encrypt local data…” [0039], “… aggregator device 150 may perform one or more mathematical operations, such as addition, subtraction, division, and/or multiplication in order to aggregate encrypted local parameters…” [0015], “… Homomorphic encryption generally refers to encryption techniques that allow one or more types of mathematical operations to be performed on encrypted data without decryption and without exposing the underlying data…” [0016], “… The aggregator device may perform computations on the encrypted local model parameters received from the edge devices in order to determine global model parameters (which will remain encrypted). The global model parameters, when sent back to the edge devices, can be decrypted using the same homomorphic encryption key or keys used to encrypt the local model parameters in order to produce an unencrypted global model at the edge devices…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate homographic encryption as disclosed by Ott into Li to encrypt the data before transmitting and the base station perform operation such as updating global model without the need of knowing the raw/original data.
Claim 9
Claim 8 is included, Ott discloses to implement privacy-enhancing techniques to the encrypted model updates to prevent extraction of client device information (0054] User context may involve user identity (e.g., a user identifier or category, which may be associated with particular privileges)… a policy may indicate that if a cryptographic request is received from a particular category of user (e.g., administrators, general users, or the like)… then crypto provider 220 should select a cryptographic technique that meets one or more conditions…”)
Claims 19-20 are similar to claim 8-9. The claims are rejected based on the same reasons.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAU HAI HOANG whose telephone number is (571)270-5894. The examiner can normally be reached 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm.
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HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/Primary Examiner, Art Unit 2154
Prosecution Insights