# US 12,184,309 B2 · Dynamic content encoding > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,184,309 B2 - Document ID: US12184309B2 - Status: Granted - Kind: B2 - Application number: 18/077,196 - Filed: 2022-12-07 - Published: 2023-10-26 - Granted: 2024-12-31 - Earliest claimed: 2022-04-26 - Assignees: Apple Inc. - Applicants: Apple Inc. - Inventors: Christian T. Martelock; Ali Sazegari; Eric Bainville - Web page: https://patentgrep.com/patent/US12184309B2 ## CPC - H03M 7/30: Compression (speech analysis-synthesis for redundancy reduction G10L19/00; for image communication H04N); Expansion; Suppression of unnecessary data, e.g. redundancy reduction - G06F 40/211: Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars - H03M 7/3071: {Prediction} - H03M 7/3079: {Context modeling} - H03M 7/42: using table look-up for the coding or decoding process, e.g. using read-only memory {(H03M7/4006 takes precedence)} - H03M: CODING; DECODING; CODE CONVERSION IN GENERAL (using fluidic means F15C4/00; optical analogue/digital converters G02F7/00; coding, decoding or code conversion, specially adapted for particular applications, see the relevant subclasses, e.g. G01D, G01R, G06F, G06T, G09G, G10L, G11B, G11C, H04B, H04L, H04M, H04N; ciphering or deciphering for cryptography or other purposes involving the need for secrecy G09C) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method for encoding text includes grouping text as a sequence of bytes, the text comprising a string of characters, each byte corresponding to a character in the text. For each byte of the sequence of bytes: (a) each bit is processed from most significant bit to least significant bit to generate a context; and (b) a subsequent bit is predicted, using a prediction model, based on the context generated based on previously processed bits, prediction of the prediction model being a combination of predictions of a plurality of sub-models. An encoded bitstream is output based on the predicted bits. The encoded bitstream includes encoded data corresponding to the text. ## Claims ### Claim 1 A method, comprising: grouping text as a sequence of bytes, the text comprising a string of characters, each byte corresponding to a character in the text; for each byte of the sequence of bytes: processing each bit from most significant bit to least significant bit to generate a context; and predicting a subsequent bit, using a prediction model, based on the context generated based on previously processed bits, prediction of the prediction model being a combination of predictions of a plurality of sub-models; and outputting an encoded bitstream based on the predicted bits, the encoded bitstream comprising encoded data corresponding to the text. ### Claim 2 (depends on 1) The method of claim 1, wherein predicting the subsequent bit comprises generating a probability, using each of the plurality of sub-models, that the subsequent bit is 1 using a corresponding pre-determined number of previously processed bits as context, the probability being determined by a hash table lookup for the corresponding sub-model. ### Claim 3 (depends on 1) The method of claim 1, wherein at least one of the plurality of sub-models predicts a subsequent character based on a previously processed string of characters in the text. ### Claim 4 (depends on 1) The method of claim 1, wherein each of the plurality of sub-models is static model and is pre-trained using a known data set. ### Claim 5 (depends on 1) The method of claim 1, wherein the combination of predictions of the plurality of sub-models comprises combining different order predictions using a 3D function, each of the different order predictions obtained by combining a corresponding subset of sub-models among the plurality of sub-models. ### Claim 6 (depends on 5) The method of claim 5, wherein a low order prediction is based on a sub-model that uses one previously processed bit as a context for predicting the subsequent bit. ### Claim 7 (depends on 5) The method of claim 5, wherein combining the predictions of a first subset is performed using a medium order 3D function, and combining the predictions of a second subset is performed using a high order 3D function. ### Claim 8 (depends on 1) The method of claim 1, wherein the outputted encoded bitstream comprises a first encoded portion indicating a size of the data being encoded, a single bit indicating whether the data is compressed, and a third encoded portion encoding the data corresponding to the text. ### Claim 9 A non-transitory computer-readable medium including instructions, which when executed by at least one computing device, cause the at least one computing device to perform operations including: grouping text as a sequence of bytes, the text comprising a string of characters, each byte corresponding to a character in the text; for each byte of the sequence of bytes: processing each bit from most significant bit to least significant bit to generate a context; and predicting a subsequent bit, using a prediction model, based on the context generated based on previously processed bits, prediction of the prediction model being a combination of predictions of a plurality of sub-models; and outputting an encoded bitstream based on the predicted bits, the encoded bitstream comprising encoded data corresponding to the text. ### Claim 10 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein predicting the subsequent bit comprises generating a probability, using each of the plurality of sub-models, that the subsequent bit is 1 using a corresponding pre-determined number of previously processed bits as context, the probability being determined by a hash table lookup for the corresponding sub-model. ### Claim 11 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein at least one of the plurality of sub-models predicts a subsequent character based on a previously processed string of characters in the text. ### Claim 12 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein each of the plurality of sub-models is static model and is pre-trained using a known data set. ### Claim 13 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein the combination of predictions of the plurality of sub-models comprises combining different order predictions using a 3D function, each of the different order predictions obtained by combining a corresponding subset of sub-models among the plurality of sub-models. ### Claim 14 (depends on 13) The non-transitory computer-readable medium of claim 13, wherein a low order prediction is based on a sub-model that uses one previously processed bit as a context for predicting the subsequent bit. ### Claim 15 (depends on 13) The non-transitory computer-readable medium of claim 13, wherein combining the predictions of a first subset is performed using a medium order 3D function, and combining the predictions of a second subset is performed using a high order 3D function. ### Claim 16 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein the outputted encoded bitstream comprises a first encoded portion indicating a size of the data being encoded, a single bit indicating whether the data is compressed, and a third encoded portion encoding the data corresponding to the text. ### Claim 17 A device including an input, an output, a memory and at least one processor communicatively coupled to the memory, the at least one processor configured to: group text as a sequence of bytes, the text comprising a string of characters, each byte corresponding to a character in the text; for each byte of the sequence of bytes: process each bit from most significant bit to least significant bit to generate a context; and predict a subsequent bit, using a prediction model, based on the context generated based on previously processed bits, prediction of the prediction model being a combination of predictions of a plurality of sub-models; and output an encoded bitstream based on the predicted bits, the encoded bitstream comprising encoded data corresponding to the text. ### Claim 18 (depends on 17) The device of claim 17, wherein predicting the subsequent bit comprises generating a probability, using each of the plurality of sub-models, that the subsequent bit is 1 using a corresponding pre-determined number of previously processed bits as context, the probability being determined by a hash table lookup for the corresponding sub-model. ### Claim 19 (depends on 17) The device of claim 17, wherein the combination of predictions of the plurality of sub-models comprises combining different order predictions using a 3D function, each of the different order predictions obtained by combining a corresponding subset of sub-models among the plurality of sub-models. ### Claim 20 (depends on 17) The device of claim 17, wherein the outputted encoded bitstream comprises a first encoded portion indicating a size of the data being encoded, a single bit indicating whether the data is compressed, and a third encoded portion encoding the data corresponding to the text.