Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This Office Action is in response to the application 19/007,810 filed 01/02/2025. Claims 1, 8, and 15 are independent claims. Claims 1-20 have been examined and are pending. This Action is made non-FINAL.
Drawings
The drawings were received on 01/02/2025. These drawings are reviewed and accepted by the Examiner.
Information Disclosure Statement
The information disclosure statement (IDS), submitted on 01/02/2025 is being considered by the examiner.
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 1-5, 9-12 and 16-19 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without being integrated into a practical application or being significantly more.
Regarding claims 1, 8, and 15, the claims are directed to an abstract idea as reciting the limitations “determin[ing] … a set of text patterns …;” “partition[ing] … the data packet into a plurality of data blocks;” “determin[ing] that the first data clock comprises a portion that represents sensitive information;” “convert[ing] each letter in the portion of the first data block into a respective mapped letter;” “identify[ing] … a set of repetitive words within the first data block;” “convert[ing] the set of repetive words into a compressed representation of the set of repetitive words;” “generat[ing] an encrypted, compressed first data block by aggregating the compressed representation of the set of repetitive words with a rest of the first data block;” and “aggregate[ing] the encrypted, compressed first data block … comprising appending each data block with a unique header bit-field …” Broadly interpreted, the aforementioned steps are directed to mental processes as said steps could be performed in the human mind or using pencil and paper. Therefore, the claims recite an abstract idea.
Said abstract idea and/or judicial exception is not integrated into a practical application as the claim does not recite any other active steps that utilize determination result into a practical application. It’s noted that the claims recite the steps of “encrypt the data packet with a first encryption key, wherein the first encryption key …” is mathematic relationship,” Said operations are not sufficient to consider that the abstract idea is being interpreted into a practical application. Said operations are recited at a high level of generality in gathering/processing/storing information, which are a form of insignificant extra-solution activity.
It’s also noted that the claims recite additional limitation/elements (i.e., system, memory, processor, etc.,). However, said additional elements are recited at a high-level of generality (i.e., as a generic computing device performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception or abstract idea using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
As mentioned above, although the claims recite additional elements, said elements taken individually or as a combination, do not result in the claim amounting to significantly more than the abstract idea because as the additional elements perform generic computer content distributing functions routinely used in information technology field. As discussed above, the additional elements recited at a high-level of generality such that they amount no more than mere instructions to apply the exception using a generic computer component. Therefore, the claim is directed to non-statutory subject matter.
Regarding claims 2-5, 9-12, and 16-19; claims 2-7, 9-12 and 14-19 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter for the same reasons addressed above as the claims recite an abstract idea and the claims do not positively recite any other operations that could be considered as the abstract idea is being integrated into a practical application or significantly more. It’s noted that claims 4-5, 11-12 and 18-19 recite the steps of “extract[ing] a set of features …” “determi[ing] which words a repeated within the first data block” “resiz[ing] …” “compar[ing] the re” However, said steps are either mental processes or recited at a high level of generality in outputting information and amounts to mere data gathering/processing, which is a form of insignificant extra-solution activity.
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.
Claims 1, 2-3, 6, 8, 9-10, 13, 15, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Huttunen (“Huttunen,” US 2003/0147267), in view of Jain (“Jain,” US 12,393,637), and Sumedrea et al. (“Sumedrea,” US 2025/0005175), and Boesgaard (“Boesgaard ,” US 2020/0396056) , and Lempel et al. (“Lempel, US 2022/0405461) , and further in view of Peng (“Peng,” US 2022/0276855).
Regarding claim 1, Huttunen teaches a system comprising:
a memory configured to store a data packet, wherein the data packet is in form of text (Huttunen: par. 0040, “The PDA 1 comprises ... a ROM memory 4, and a RAM memory 5.”; par. 0041, “The RAM 5 is arranged to store both program files and user data... typically user data files such as text files, images, contacts, spreadsheets, etc."), and
a processor, operably coupled to the memory, and configured to (Huttunen: par. 0040, “The PDA 1 comprises a microprocessor 3, a ROM memory 4, and a RAM memory 5."; par. 0041, “...program code for encrypting and decrypting data... run by the microprocessor 3.):
receive a request to encrypt the data packet (Huttunen: par. 0043] “When a user requests encryption of a file (or some application requests encryption)...".);
in response to receiving the request:
encrypt the data packet with a first encryption key, wherein the first encryption key is generated by a random key generator (Huttunen: par. 0043, "When a user requests encryption of a file... the program generates a temporary key KEYtmp (again using a random number generator) which is used by the symmetric cipher to encrypt the file. A new temporary key KEYtmp is generated for each file or folder to be encrypted. The encrypted file is stored in the RAM 5...".);
Huttunen does not explicitly disclose determine, by a first neural network trained on text patterns, a set of text patterns within the data packet, wherein: the set of text patterns indicates content of various portions of the data packet; each of the set of text patterns is represented by an embedding vector comprising numerical values; partition, based at least in part upon the determined set of text patterns, the data packet into a plurality of data blocks; each data block corresponds to a distinct text pattern from within the data packet; each data block comprises a plurality of letters;
However, in an analogous art, Jain discloses
determine, by a first neural network trained on text patterns, a set of text patterns within the data packet (Jain: Col. 7, lines 55-64, In at least one embodiment, analysis module 106 is a computing resource that processes document content to identify key features. In at least one embodiment, a neural network uses analysis module 106 to extract text patterns and structures. In at least one embodiment, analysis module 106 identifies headings in a document. In at least one embodiment, analysis module 106 detects tables within documents containing headings, tables, images, and/or other visual elements; Col. 10, lines 31-40, A neural network uses this document 204 to identify formatting attributes, such as font size and style, which may be used for distinguishing between different elements. For example, the document parsing system 240 can process a document to identify its structural components or analyze a document to distinguish between different text styles; Col. 17, lines 7-9, In at least one embodiment, neural network is to extract text and structural information from various document formats), wherein:
the set of text patterns indicates content of various portions of the data packet (Jain: Col. 3, line 50 to Col. 3, line 12, “Correspondences between the plurality of statistical measures and a plurality of element types... are identified... The document is parsed into chunks that preserve context... A representation of the document... includes a hierarchical structure..."; Col. 12, lines 26-31, “The document parsing system utilizes a natural language processing algorithm that processes each chunk individually while preserving its contextual metadata... to ensure that the semantic meaning and document structure are maintained throughout the parsing process."); and
each of the set of text patterns is represented by an embedding vector comprising numerical values (Jain: Col. 17, lines 39-55, “Vector storage... uses neural networks to encode textual and structural information into numerical vectors, also known as vector embeddings. Examples include converting a paragraph into a dense vector based on its semantic content... vectors are based on features... a feature extraction module... includes an encoder that encodes input... into one or more feature vectors."; Col. 17, lines 61-62, "These resources enable a neural network to encode textual and structural information into numerical vectors.");
partition, based at least in part upon the determined set of text patterns, the data packet into a plurality of data blocks (Jain: Col. 2, lines 8-24, "The document may be parsed into chunks while preserving its context, using the established hierarchical relationship... the hierarchical structure is employed to ensure that data is segmented and stored in a manner that preserves the interrelationships among various components of the document.”; Col. 3, lines 39-41, “The system chunks the document into meaningful sections based on the identified categories."; Col. 9, lines 9-15, “The document parsing module... breaks down the document into smaller, more manageable chunks... guided by the hierarchical relationships...”; Col. 19, lines 14-21“Parse the document into chunks using the determined hierarchical relationship."), wherein:
each data block corresponds to a distinct text pattern from within the data packet (Jain: Col. 11, lines 42-59, "In at least one embodiment, the document parsing system 240 determines hierarchical relationships between element types based on identified correlations... Examples include structuring a legal document by sections and clauses or organizing a research paper by chapters and sub-sections, with metadata capturing these relationships."; Col. 11, line 65 to Col. 12, line 3, “...the comparison indicates that the type and the additional type correspond to different hierarchical levels within the document's structure. For example, headings... are identified as a higher hierarchical level compared to subheadings, which are further distinguished from paragraph text.”; Col. 19, lines 14-21, “...parse the document into chunks using the determined hierarchical relationship. Parsing involves breaking down the document into smaller, manageable sections or chunks that align with an established hierarchy.”); and
each data block comprises a plurality of letters (Jain: Col., 11, lines 1-6, “A paragraph 228 represents the main body text of the document. The system... analyze[s] text blocks... A neural network processes this information to classify paragraphs."; Col. 17, lines 5-9,“Document parsing system 540 is responsible for analyzing document content... utilizing neural networks to extract text and structural information from diverse document formats...”; Col. 19, lines 14-21, “Parsing involves breaking down the document into smaller, manageable sections or chunks...”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Jain with the method and system of Huttunen to include determine, by a first neural network trained on text patterns, a set of text patterns within the data packet, wherein: the set of text patterns indicates content of various portions of the data packet; and each of the set of text patterns is represented by an embedding vector comprising numerical values; partition, based at least in part upon the determined set of text patterns, the data packet into a plurality of data blocks, wherein: each data block corresponds to a distinct text pattern from within the data packet; and each data block comprises a plurality of letters.. One would have been motivated to facilitate more effective user of documents in machine-learning mo9dels by preserving their hierarchical context and structure (Jain: Col. 5, lines 55-59).
Jain does not explicitly determine, by a second neural network trained on text cues related to sensitive information and based at least in part upon a first text pattern associated with a first data block, that the first data block comprises a portion that represents sensitive information.
However, in an analogous art, Sumedrea discloses
determine, by a second neural network trained on text cues related to sensitive information and based at least in part upon a first text pattern associated with a first data block, that the first data block comprises a portion that represents sensitive information (Sumedrea: par. 0013, A second method involves machine learning (ML) that is focused on named entity recognition (NER), where recent works employ large language models (LLMs). A LLM is a computerized language model consisting of an artificial neural network with many parameters, trained on large quantities of unlabeled text using self-supervised learning or semi-supervised learning.".; par. 0015; “...combine the benefits of pattern matching through regex rules and LLMs powered by NER in a hybrid PII scrubbing system... incorporating LLMs as part of a sensitive data protection pipeline...”; par. 0045, “The actual PII detection/scrubbing LLM... can be a NER-type system that assigns a PII probability to each token in the content..."; par. 0046, “NER involves detecting and categorizing important information in text... person names... social security number... home address... email address... password.”; par. 0047 “Named entities are split up into tokens that it needs to classify. A label is attributed to each token...”; par. 0084, "Each token from the text is classified by the model... the system knows where it extracted the token from …that token is positioned... useful for masking the PIIs... each token gets a distribution of probabilities...").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sumedrea with the method and system of Huttunen and Jain to include determine, by a second neural network trained on text cues related to sensitive information and based at least in part upon a first text pattern associated with a first data block, that the first data block comprises a portion that represents sensitive information. One would have been motivated to improve the ability to accurately identify sensitive information and maximize coverage of sensitive entities in a data processing pipeline (Sumedrea: pars. 0014-0015).
Huttunen and Jain, and Sumedrea do not explicitly disclose convert each letter in the portion of the first data block into a respective mapped letter, wherein the respective mapped letter is determined based at least in part upon a second encryption key;
However, in an analogous art, Boesgaard discloses convert each letter in the portion of the first data block into a respective mapped letter, wherein the respective mapped letter is determined based at least in part upon a second encryption key (Boesgaard: par. 0027, "The inner encryption algorithm converts plaintext into ciphertext... an appropriate cryptographic key is used for decryption. Suitable algorithms include AES and RSA.".; par. 0045, "The compressed plaintext is encrypted using an inner encryption algorithm which may take a key and/or a tweak as input. During decryption, the ciphertext is decrypted using the same key and/or tweak.".; par. 0046, "The format-preserving encryption (FPE) algorithm produces encrypted text while preserving the format of the original plaintext.".; pars. 0086-0088).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Boesgaard with the method and system of Huttunen, Jain, and Sumedrea to include convert each letter in the portion of the first data block into a respective mapped letter, wherein the respective mapped letter is determined based at least in part upon a second encryption key. One would have been motivated to reduce format information leakage and improve the security of format-preserving encryption for variable-size data objects (Boesgaard: pars. 0008-0010, 0019).
Huttunen, Jain, Sumedrea, and Boesgaard do not explicitly disclose identify, based at least in part upon the first text pattern associated with the first data block, a set of repetitive words within the first data block; convert the set of repetitive words into a compressed representation of the set of repetitive words, wherein the compressed representation is in a data structure different from the set of repetitive words; generate an encrypted, compressed first data block by aggregating the compressed representation of the set of repetitive words with a rest of the first data block;
However, in analogous art, Lempel discloses
identify, based at least in part upon the first text pattern associated with the first data block, a set of repetitive words within the first data block (Lempel: par. 0012, “Many passages of text include some degree of repetition of text characters, character strings, whole words, and/or whole sentences, and these repeated segments are typically all encoded separately.”; par. 0043, “If no predicted continuations ... match the next one or more text characters of the target string, then ... identifying a longest matching entry in a compression dictionary that is consistent with the next one or more text characters of the target string.”);
convert the set of repetitive words into a compressed representation of the set of repetitive words, wherein the compressed representation is in a data structure different from the set of repetitive words (Lempel: par. 0015, “Encoded text string 101 is compressed via a text compression process to give a compressed text string 104... the text compression process may losslessly reduce the amount of computer data used... The compressed text string may later be decompressed to recreate the original text string.”; par. 0049, “...compressing the next one or more text characters of the target string as a dictionary-type compressed representation that includes the dictionary index value of the longest matching entry.");
generate an encrypted, compressed first data block by aggregating the compressed representation of the set of repetitive words with a rest of the first data block (Lempel: par. 0015, “...encoded text string 101 is compressed ... to give a compressed text string 104.”; par. 0058, “...the computing device may ultimately output a compressed text string.” in combination of Sumedrea and Boesgaard describe above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lempel with the method and system of Huttunen, Jain, Sumedrea, and Boesgaard to include identify, based at least in part upon the first text pattern associated with the first data block, a set of repetitive words within the first data block; convert the set of repetitive words into a compressed representation of the set of repetitive words, wherein the compressed representation is in a data structure different from the set of repetitive words; generate an encrypted, compressed first data block by aggregating the compressed representation of the set of repetitive words with a rest of the first data block. One would have been motivated to provide text compression techniques can reduce the amount of computer used to represent digital text of arbitrary length (Lempel: par. 0001).
Lempel does not explcitly disclose aggregate the encrypted, compressed first data block with the rest of the plurality of data blocks, wherein aggregating the encrypted, compressed first data block with the rest of the plurality of data blocks comprises appending each data block with a unique header bit-field that indicates a position of a respective data block in a sequence of the plurality of data blocks.
However, in an analogous art, Wang discloses aggregate the encrypted, compressed first data block with the rest of the plurality of data blocks (Peng: par. 0063, “The server may divide a complete upgrade package for vehicle upgrading into a plurality of data blocks... During block division, the server may identify the data blocks based on locations of the data blocks in the upgrade package... so that the data blocks may be subsequently integrated based on identifiers of the plurality of data blocks to obtain the complete upgrade package again... In an embodiment, a data block may be a complete data packet.”, in combination of Sumedrea and Boesgaard), wherein aggregating the encrypted, compressed first data block with the rest of the plurality of data blocks comprises appending each data block with a unique header bit-field that indicates a position of a respective data block in a sequence of the plurality of data blocks (Peng: par. 0064, “For example, a header of each data block obtained through block division may include one or more of a version number, an upgrade package sequence number i, a device number j, and a sequence number k of the data block in an upgrade package i... The sequence number k may occupy 1 to 3 bytes.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Peng with the method and system of Huttunen, Jain, Sumedrea, Boesgaard, and Lempel to include aggregate the encrypted, compressed first data block with the rest of the plurality of data blocks, wherein aggregating the encrypted, compressed first data block with the rest of the plurality of data blocks comprises appending each data block with a unique header bit-field that indicates a position of a respective data block in a sequence of the plurality of data blocks. One would have been motivated to reduce the load on the OAT server by dividing the vehicle upgrade package int a plurality of data blocks that are distributed among terminals (Peng: pars. 0006-0007).
Regarding claim 2, the combination of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng teaches the system of Claim 1. The combination of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng further teaches wherein partitioning the data packet into the plurality of data blocks is further based at least in part upon a text pattern analysis of the data packet (Jain: Col. 2, lines 8-24, "The document may be parsed into chunks while preserving its context, using the established hierarchical relationship... the hierarchical structure is employed to ensure that data is segmented and stored in a manner that preserves the interrelationships among various components of the document."; Col. 3, lines 39-41, "The system chunks the document into meaningful sections based on the identified categories."; Col. 9, lines 9-15, "The document parsing module... breaks down the document into smaller, more manageable chunks... guided by the hierarchical relationships..."; Col. 11, lines 42-59, "The document parsing system determines hierarchical relationships between element types based on identified correlations..."; Col. 11, line 65 to Col. 12, line 3, "...headings... are identified as a higher hierarchical level compared to subheadings... paragraph text..."; Col. 19, lines 14-21, "Parse the document into chunks using the determined hierarchical relationship."
Regarding claim 3, the combination of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng teaches the system of Claim 1. The combination of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng further teaches wherein determining, based at least in part upon the first text pattern associated with the first data block (Sumedrea: par. 0015, “...LLMs powered by NER in a hybrid PII scrubbing system...”; par. 0045, “...the PII detection/scrubbing LLM... assigns a PII probability to each token...”; par. 0046, "...the PSM system maps the PII identification task to Named Entity Recognition (NER)...”), that the first data block comprises the portion that represents sensitive information comprises applying a binary mask to the first data block to identify portions that represent sensitive information (Sumedrea: par. 0047, “Named entities are split into tokens... A label is attributed to each token... B-, I-, or O.”; par. 0062, “...the PSM system knows the position of each token relative to the PII words..."; par. 0084, ‘Each token from the text is classified by the model... the PSM system knows where that token is positioned in the sentence based on numerical indices... This is useful for masking the PIIs the PSM system identifies and replacing them in the text at the correct position."; par. 0085, " Based on these probabilities, as well as the posi-tions of the tokens the PSM system 104 can apply a greedy decoding step in order to map the tokens that the PSM system 104 classifies to words and thus label the words as PIIs.”), wherein the binary mask isolates the portions representing sensitive information from other portions the first data block (Sumedrea: par. 0023, “...remove the PII associated with the identified scrubbing candidates...”; par. 0025; par. 0084, “...masking the identified PIIs... replacing them in the text at the correct position...”; par. 0114, “...generate a copy of the record where the PII is absent from the copy..."; par. 0017, scrubbing a record may include removing PII from a first record, duplicating the first record to create a second record ( sometimes referred to as scrubbed record) and removing PII from the second record, and/or generating a second record that includes the data of the first record but not the PII of the first record; par. 0130,).
Regarding claim 6, the combination of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng teaches the system of claim 1. The combination of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng further teaches, wherein the processor is further configured to:
receive a second request to decrypt and decompress the encrypted, compressed data packet (Huttunen: par. 0041, “The program has two modes of operation, a first encryption and decryption mode and a second encryption only mode.”; par. 0043, “Files previously encrypted with the symmetric cipher can be decrypted...”; Abstract, pars. 0007, 0008, 0029.); and
in response to receiving the second request (Huttunen: par. 0043, "When a user requests encryption of a file..." and "Files previously encrypted... can be decrypted...". ):
retrieve the encrypted, compressed data packet (Huttunen: par. 0043, The encrypted file is stored in the RAM 5 [] files previously encrypted with the symmetric cipher can be decrypted . ");
decrypt the encrypted, compressed data packet using a first decryption key (Huttunen: par. 0042, “KEYsym ... is stored...”, par. 0043, “The temporary key KEYtmp is encrypted using the symmetric key KEYsym...” and “the temporary keys can be decrypted and made available.” par. 0003);
identify, based at least in part upon header bit-fields associated with the plurality of data blocks, the position of each respective data block within the sequence of the plurality of data blocks (Peng: par. 0064, “...a header of each data block... may include... a sequence number k of the data block...”; par. 0045, “...the server may identify the data blocks based on locations of the data blocks... so that the data blocks may subsequently be integrated...”.);
generate a decrypted, decompressed first data block by:
decompressing the first data block by converting the compressed representation of the set of repetitive words back into the set of repetitive words (Lempel: par. 0064 “...method 700 for text decompression is illustrated. For the purposes of decompression, the 'target string' includes any text characters that have not yet been decompressed.”; par. 0065, “...method 700 includes recognizing a compressed representation that represents a next one or more text characters of a target string to be decompressed...”; par. 0067, “...method 700 may be repeated any number of times to decompress each of the plurality of compressed representations to recreate the larger text string.”; par. 0068, “...the associated ranks of predicted continuations... and the dictionary index values... can be reproduced during decompression to restore the original text characters.”; Sea also pars. 0071-0079); and
converting each mapped letter in the portion of the first data block back into a respective original letter based at least in part upon a second decryption key (Boesgaard: par. 0045, “Format-preserving decryption is performed by first compressing or encoding the ciphertext and then decrypting the compressed or encoded version of the ciphertext using the same key and/or tweak as used during format-preserving encryption. The decrypted version of the ciphertext is then decompressed or decoded to obtain the plaintext.”; par. 0030 “...for decryption purposes, a decompression step is necessarily used... Likewise, for decryption, a compression step is necessarily utilized...”.; par. 0031, “...for decryption purposes, a decoding step is necessarily used, which is the inverse of the encoding procedure applied for encryption...”.);
reconstruct the data packet by aggregating the decrypted, decompressed first data block with the rest of the plurality of data blocks (Peng: par. 0063, “...the data blocks may subsequently be integrated... to obtain the complete upgrade package again.” in combination teaching of Huttunen, Lempel, anf Boesgaard).
Regarding claim 8, claim 8 is directed to a method associated with the system claimed in claim 1; claim 8 is similar in scope to claim 1, and is therefore rejected under similar rationale.
Regarding claim 9, claim 9 is similar in scope to claim 2, and is therefore rejected under similar rationale.
Regarding claim 10, claim 10 is similar in scope to claim 3, and is therefore rejected under similar rationale.
Regarding claim 13, claim 13 is similar in scope to claim 6, and is therefore rejected under similar rationale.
Regarding claim 15, claim 15 is directed to a non-transitory computer-readable medium storing instructions that when executed by a processor (Huttunen: par. 0040) associated with the method claimed in claim 15; claim 15 is similar in scope to claim 1, and is therefore rejected under similar rationale.
Regarding claim 16, claim 16 is similar in scope to claim 2, and is therefore rejected under similar rationale.
Regarding claim 17, claim 17 is similar in scope to claim 3, and is therefore rejected under similar rationale.
Regarding claim 20, claim 20 is similar in scope to claim 6, and is therefore rejected under similar rationale.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Huttunen (“Huttunen,” US 2003/0147267), in view of Jain (“Jain,” US 12,393,637), and Sumedrea et al. (“Sumedrea,” US 2025/0005175), and Boesgaard (“Boesgaard ,” US 2020/0396056) , and Lempel et al. (“Lempel, US 2022/0405461) , and Peng (“Peng,” US 2022/0276855), further in view of Russell et al. (“Russell,” US 2025/0348527)
Regarding claim 4, the combination of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng teaches the system of Claim 1. The combnation of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng further teaches, wherein identifying, based at least in part upon the first text pattern associated with the first data block, the set of repetitive words within the first data block comprises: determining, based at least in part upon the set of features, which words are repeated within the first data block (Lempel: par. 0001, “Text compression techniques often take advantage of redundancy in the text to be compressed.”; par. 0003, “...If the one or more predicted continuations include a matching predicted continuation... the next one or more text characters are compressed... If no predicted continuations match... a longest matching entry in a compression dictionary is identified...”; par. 0012. “...whole words and/or whole sentences , and these repeated segment are typically all encoded separately ...”.).
Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng do not explicitly teach extracting a set of features from the first data block based on at least one of a word tokenization or a sentence tokenization , wherein: the set of features indicates a frequency of occurrence of each word within the first data block ; and the set of features is represented by a feature vector comprising numerical values;
However, in an analogous art, Russell discloses
extracting a set of features from the first data block based on at least one of a word tokenization or a sentence tokenization ( Russell: par. 0028 "”..the base summarization algorithm 152 may perform word segmentation, sentence segmentation, word tokenization, sentence tokenization, and sentiment analysis on a given text 150; par. 0032, “...the target summarization algorithm 158 may perform word segmentation, sentence segmentation, word tokenization, sentence tokenization, and sentiment analysis...”; par. 0044, “...the server 140 may extract a second set of features 226 from the text 150a, where the features 226 may indicate... tokenized words, tokenized sentences...”), wherein:
the set of features indicates a frequency of occurrence of each word within the first data block (Russell: part. [0044], “...the features 226 may indicate content, context, word frequency, tokenized words, tokenized sentences, sentiment, word meaning, and other linguistic features of the text...”); and
the set of features is represented by a feature vector comprising numerical values (Russell: par. [0044]; “...the server 140 may generate a second feature vector 228 for the text 150a, where the second feature vector 228 includes numerical values representing the features 226.”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Russell with the method and system of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng to include extracting a set of features from the first data block based on at least one of a word tokenization or a sentence tokenization, wherein: the set of features indicates a frequency of occurrence of each word within the first data block; and the set of features is represented by a feature vector comprising numerical values. One would have been motivated to improve text processing by extracting numerical feature vectors representing linguistic features, including word frequency and tokenized words or sentences, thereby improving the analysis of textual characteristics for subsequent text processing (Russell: pars. [0003], [0006], [0008]).
Regarding claim 11, claim 11 is similar in scope to claim 4, and is therefore rejected under similar rationale.
Regarding claim 18, claim 18 is similar in scope to claim 4, and is therefore rejected under similar rationale.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Huttunen (“Huttunen,” US 2003/0147267), in view of Jain (“Jain,” US 12,393,637), and Sumedrea et al. (“Sumedrea,” US 2025/0005175), and Boesgaard (“Boesgaard ,” US 2020/0396056) , and Lempel et al. (“Lempel, US 2022/0405461) , and (“Peng,” US 2022/0276855), and further in view of Douglis et al. (“Douglis,” US 8,639,669).
Regarding claim 5, the combination of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng teaches the system of Claim 1. Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng do not explicitly disclose, wherein the processor is further configured to:
determine that a size of the first data block does not correspond to a size of a second data block; and
resize at least one of the first data block or the second data block such that the size of the first data block corresponds to the size of the second data block.
However, in an analogous art, Douglis discloses
determine that a size of the first data block does not correspond to a size of a second data block (Douglis: Abstract “...the first data chunks have been partitioned according to a first chunk size. A second chunk size is calculated...”; Col. 5, lines 24-38 "...a request to replicate or migrate data chunks from a first storage system having a first average chunk size to a second storage system having a second average chunk size..." "...chunk evaluation unit 105 is configured to determine boundaries or chunk sizes of resized chunks...": Col. 10, lines 35-54 ,; and
resize at least one of the first data block or the second data block such that the size of the first data block corresponds to the size of the second data block (Douglis: Col. 5, lines 24-38, "...The data chunks are then resized..." "...all data chunks stored in the second storage system have similar chunk sizes for efficient storage and/or performance..."; Col. 10, lines 35-54 , "...the associated data chunks are resized to form a resized chunk." (Col. 5, lines 24-38).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Douglis with the method and system of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng to include determine that a size of the first data block does not correspond to a size of a second data block; and resize at least one of the first data block or the second data block such that the size of the first data block corresponds to the size of the second data block. One would have been motivated to resize data blocks to improve processing efficiency while balancing the overhead associated with different block sizes (Douglis : Col. 1, lines 21-57).
Regarding claim 12, claim 12 is similar in scope to claim 5, and is therefore rejected under similar rationale.
Regarding claim 19, claim 19 is similar in scope to claim 5, and is therefore rejected under similar rationale.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Huttunen (“Huttunen,” US 2003/0147267), in view of Jain (“Jain,” US 12,393,637), and Sumedrea et al. (“Sumedrea,” US 2025/0005175), and Boesgaard (“Boesgaard ,” US 2020/0396056) , and further in view of Lempel et al. (“Lempel, US 2022/0405461) , and Peng (“Peng,” US 2022/0276855), and Massand (“Massand,” US 2008/0301193), further in view of Wang et al. (“Wang,” US 2022/0188658).
Regarding claim 7, the combination of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng teaches the system of claim 7. Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng do not explicitly disclose wherein the processor is further configured to: compare the reconstructed data packet with an original data packet; determine that the reconstructed data packet deviates from the original data packet;
However, in an analogous art, Massand discloses
compare the reconstructed data packet with an original data packet (Massand: par. 0010, “A document comparison engine 303 may compare an original document 301 ... to a modified document 302.” And “The comparison output document provides indications of what has changed between the original document and the modified document.”);
determine that the reconstructed data packet deviates from the original data packet (Massand: par. 0010, “...provides indications of what has changed between the original document and the modified document.”); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Massand with the method and system of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, and Peng to include compare the reconstructed data packet with an original data packet; determine that the reconstructed data packet deviates from the original data packet. One would have been motivated to provide a comparison system capable of identifying the differences between an original document and another document, including compound documents, by generating comparison results that indicate what has changed between the documents (Massands: par. [0010]–[0011], [0018]).
Massand does not explicitly disclose in response to determining that the reconstructed data packet deviates from the original data packet, revise one or more parameters associated with a hybrid machine learning compression algorithm.
However, in an analogous art, Wang discloses
determining that the reconstructed data packet deviates from the original data packet, revise one or more parameters associated with a hybrid machine learning compression algorithm (Wang: par. 0042, “Automatic machine learning can carry out automatic knowledge distillation based on a feedback loop in an iterative manner. The present application studies the common compression architecturefor generating various pre-trained language models by knowledge distillation based on meta-learning [] By changing the coding vector input ... jointly training the structure generator and the corresponding distillation structure [] a structure generator that can generate weights for different distillation structures can be acquire [] an optimal compression structure is searched [] the compressed model is optimized...").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang with the method and system of Huttunen, Jain, Sumedrea, Boesgaard, Lempel, Peng, and Wang to include in response to determining that the reconstructed data packet deviates from the original data packet, revise one or more parameters associated with a hybrid machine learning compression algorithm. One would have been motivated to automatically optimize a machine-learning compression framework by updating the structure generator and searching for an optimal structure with improved accuracy (Wang: pars. 0009-0010, 0036).
Regarding claim 14, claim 14 is similar in scope to claim 17, and is therefore rejected under similar rationale.
Conclusion
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/Canh Le/
Examiner, Art Unit 2439
July 11th, 2026
/LUU T PHAM/Supervisory Patent Examiner, Art Unit 2439
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