# US 10,120,873 B2 · Survivable cloud data storage and transport

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- Number: US 10,120,873 B2
- Document ID: US10120873B2
- Status: Granted
- Kind: B2
- Application number: 14/712,145
- Filed: 2015-05-14
- Published: 2015-09-03
- Granted: 2018-11-06
- Earliest claimed: 2015-05-14
- Assignees: Spatial Digital Systems, Inc.
- Applicants: Spatial Digital Systems, Inc.
- Inventors: Juo-Yu Lee; Donald C.D. Chang; Steve K. Chen
- Web page: https://patentgrep.com/patent/US10120873B2

## CPC

- G06F 16/1744: {using compression, e.g. sparse files}
- G06F 16/1727: {Details of free space management performed by the file system (saving storage space on storage systems G06F3/0608; management of blocks in storage devices G06F3/064)}
- G06F 21/6209: {to a single file or object, e.g. in a secure envelope, encrypted and accessed using a key, or with access control rules appended to the object itself}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Presented is a multi-channel data process to utilize wavefront multiplexing for data storage and data stream transport with redundancy on cloud or in a distribution network. This processing features additional applications for multi-media recording and data communications via transponding platforms including satellites, unmanned air vehicles (UAVs), or others for better survivability and faster accessing. Multiple concurrent data streams are pre-processed by a wavefront multiplexer into multiple sub-channels or wavefront components, where signals from respective data streams are replicated into sub-channels. These replicated data streams are linked via a unique complex weighting vector (amplitude and phase or their equivalents), or “wave-front”, which are also linked by various spatially independent wavefronts. Additionally, probing data streams are embedded and linked via some of the independent wavefronts. Aggregated data streams in sub-channels are unique linear combinations of all input data streams. Generating redundancy for stored or transported data are options in the WF muxing process. Post-processing via a wavefront de-multiplexer converts various sub-channel data streams back to the original data streams, utilizing the embedded known priori data streams as additional knowledge for reducing degrees of freedom to restore orthogonality or independency among multiple wavefronts.

## Claims

### Claim 1

A computer-implemented method comprising[H]:
transforming M input data sets concurrently into L wavefront components (WFCs) as output data sets via a wavefront muxing processor having M input ports and L output ports, where L and M are positive integers, L is greater than or equal to M; and
storing the L WFCs to L data storages distributed in I locations in user's registered cloud space and L−I user's storage devices, I being a positive integer smaller than or equal to L,
wherein the M input data sets and the L output data sets are connected to the M input ports and the L output ports, respectively,
wherein each of the L WFCs is a linear combination of the M input data sets,
wherein the transforming comprises outputting a first weighted sum of the M input data sets as a first output, and outputting a second weighted sum of the M input data sets as a second output,
wherein a first input of the wavefront muxing processor contributes to a first aggregated numerical value of the first weighted sum, and contributes to a second aggregated numerical value of the second weighted sum, and
wherein L−M corresponds to a degree of redundancy related to number of extra data sets.

### Claim 2 (depends on 1)

The method of claim 1, further comprising segmenting one data stream into the M input data sets.

### Claim 3 (depends on 1)

The method of claim 1 wherein at least one of the M input data sets is a known a priori data stream.

### Claim 4 (depends on 1)

The method of claim 1 wherein at least one of the M input data sets is zero.

### Claim 5 (depends on 1)

The method of claim 1, wherein the transforming comprises performing a mathematical function of an orthogonal matrix transform.

### Claim 6 (depends on 1)

The method of claim 1, wherein the transforming comprises performing a Hadamard transform.

### Claim 7 (depends on 1)

The method of claim 1, wherein the transforming comprises performing a Fourier transform.

### Claim 8 (depends on 1)

The method of claim 1, wherein the transforming comprises performing a mathematical function of a non-orthogonal and full rank matrix transform.

### Claim 9

A computer-implemented method comprising:
retrieving L wavefront components (WFCs) from L data storages distributed in I locations in user's registered cloud space and L−I user's storage devices, L and I being positive integers and I being smaller than or equal to L; and
transforming the retrieved L WFCs to M output data sets concurrently via a wavefront demuxing processor having L input ports and M output ports;
where M is a positive integer and L is greater than or equal to M,
wherein the retrieved L WFCs and the M output data sets are connected to the L input ports and the M output ports, respectively,
wherein each of the M output data sets is a linear combination of the retrieved L WFCs,
wherein the transforming comprises outputting a first weighted sum of the retrieved L WFCs as a first output, and outputting a second weighted sum of the retrieved L WFCs as a second output,
wherein a first input of said wavefront demuxing processor contributes to a first aggregated numerical value of the first weighted sum, and contributes to a second aggregated numerical value of the second weighted sum, and
wherein L−M corresponds to a degree of redundancy related to number of extra data sets.

### Claim 10 (depends on 9)

The method of claim 9, further comprising de-segmenting the M output data sets into one output stream.

### Claim 11 (depends on 9)

The method of claim 9 wherein at least one of the M output data sets corresponds to a known a priori data stream.

### Claim 12 (depends on 9)

The method of claim 9 wherein at least one of the M output data sets corresponds to zero.

### Claim 13 (depends on 9)

The method of claim 9, wherein the transforming comprises performing a mathematical function of an inverse orthogonal matrix transform.

### Claim 14 (depends on 9)

The method of claim 9, wherein the transforming comprises performing an inverse Hadamard transform.

### Claim 15 (depends on 9)

The method of claim 9, wherein the transforming comprises performing an inverse Fourier transform.

### Claim 16 (depends on 9)

The method of claim 9, wherein the transforming comprises performing a mathematical function of an inverse non-orthogonal and full rank matrix transform.