PATENT CLAIM ANALYSIS

Application Number: 15899626
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-07
Patent Classification: ["714", "764000"]

Abstract:
A method includes retrieving a read threshold number of integrity check value list (ICVL) encoded data slices of a set of ICVL encoded data slices. The method further includes determining whether an appended ICVL of each ICVL encoded data slice of the read threshold number of ICVL encoded data slices substantially match. When the appended ICVL of one of the ICVL encoded does not substantially match the appended ICVL of other ICVL encoded data slices, the method further includes determining a likely cause for the mismatch. When the likely cause is missing a revision update, the method further includes initiate rebuilding of the encoded data slice portion. The method further includes generating an integrity check value for the rebuilt encoded data slice and updating the integrity check value list. The method further includes appending the updated integrity check value list to the rebuilt encoded data slice.

Claim (Index 9):
A computing device comprising:\n an interface configured to interface and communicate with a dispersed storage network (DSN); memory that stores operational instructions; and processing circuitry operably coupled to the interface and to the memory, wherein the processing circuitry is configured to execute the operational instructions to:\n retrieve a read threshold number of integrity check value list (ICVL) encoded data slices (EDSs) of a set of ICVL EDSs from at least some storage units (SUs) of a set of SUs based on a request; \n determine whether an appended ICVL of each ICVL EDS of the read threshold number of ICVL EDSs substantially match; \n generate a set of integrity check values for a set of EDSs, wherein a data segment of a data object is dispersed storage error encoded to produce the set of EDSs; \n generate an ICVL from the set of integrity check values; \n append the ICVL to each EDS of the set of EDSs to produce the set of ICVL EDSs; \n send the set of ICVL EDSs to the set of SUs of the DSN for storage therein; \n based on determination that the appended ICVL of one of the ICVL EDSs of the read threshold number of ICVL EDSs does not substantially match the appended ICVL of other ICVL EDSs of the read threshold number of ICVL EDSs:\n determine a likely cause for the appended ICVL of the one of the ICVL EDSs not substantially matching the appended ICVL of the other ICVL EDSs; and \n based on determination that the likely cause is an inaccurate ICVL appended to the one of the ICVL EDSs:\n calculate a new integrity check value for an EDS portion of the one of the ICVL EDSs; \n extract an integrity check value from the appended ICVL of one of the other ICVL EDSs to produce an extracted integrity check value; \n compare the new integrity check value with the extracted integrity check value; and \n based on determination that the new integrity check value substantially matches the extracted integrity check value, utilize the EDS in decoding of the read threshold number of EDSs to recover the data segment of the data object; and \n \n \n based on determination that the appended ICVL of one of the ICVL EDSs of the read threshold number of ICVL EDSs substantially matches the appended ICVL of other ICVL EDSs of the read threshold number of ICVL EDSs, process the request.

Metadata:
- Claim Count in Document: 25.0
- Percentile: 88.0
- Lexical Diversity: 2.78571
- Patent Class: 714.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15357293', '15842560', '15840397', '15363622', '15212666']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2992073261340096
- 35 USC 102 Novelty (BERT): 0.5481641822681206
- Combined Prediction Score: 0.3241030117474207
- Mean Citation Score: 330.6532880000001
- Max Citation Score: 410.32236
- Similarity Product: 339.2027224534893

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test