DETAILED ACTION
This communication is in response to the amendment filed 6/25/26 in which claims 1 and 9 were amended. Claims 1-20 are currently pending.
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 .
Response to Arguments
Applicant’s amendments have overcome the 101 rejection of claim 9.
Applicant’s arguments with respect to the art rejections of claim 1 have been fully considered. However, Titus is now remapped in conjunction with a new reference (Mainali) to teach the argued limitations.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1, 2, 4-9, 10-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Titus, Alexander J., et al. "SIG-DB: Leveraging homomorphic encryption to securely interrogate privately held genomic databases." PLOS Computational Biology 14.9 (2018): e1006454 (“Titus”) in view of Mainali (US 2019/0260730 A1; published Aug. 22, 2019).
Regarding claim 1, Titus discloses [a] computer-implemented method for processing genetic data which comprise a series of sequence elements which represent, in each case, a biomolecule, (Abstract (“We present an algorithm for the Secure Interrogation of Genomic DataBases (SIG-DB). The SIG-DB algorithm enables databases of genomic sequences to be searched with an encrypted query sequence without revealing the query sequence to the Database Owner or any of the database sequences to the querier.”)) comprising the steps
forming sequence fragments, wherein each sequence fragment comprises a section of the series of sequence elements with a fragment length of at least two sequence elements, (p. 3 (“There are two major components of the SIG-DB protocol. First, the genomic sequences are converted to a storage efficient data structure. To do so, we chose to use k-mers [sequence fragments]… . The use of k-mers is an established method of breaking up genetic sequences without appreciative data loss; the size of the k-mer can be optimized for a specific application.”); see Fig. 1 (the k-mers fragments are TATCAGA, ATCAGAT, and TCAGATA))
applying a coding function1 to each of the sequence fragments in order to generate a plurality of encrypted fragment data items, each being associated with one of the sequence fragments, wherein the coding function is a non-invertible and collision-resistant cryptographic hash function, and (pp. 3-4 (“There are two major components of the SIG-DB protocol. First, the genomic sequences are converted to a storage efficient data structure. To do so, we chose to use k-mers and locality sensitive hashing (LSH) [coding function] ...LSHs are also space efficient data structures that store information as either a 1 or 0 and, unlike Bloom filters, force the data to a preset vector size that is smaller than the original data dimensions. For example, the sequence `TATCAGA' would represent a 1 in a separate location within the LSH than `ATCAGAT' (see Fig 1). The size of the LSH is a trade-off between computational runtime and the likelihood of a false match (i.e. hash collision) in the LSH. For our application, the LSH length is set to be five times the length of the longest sequence of all the sequences being compared. Thus, the probability of a hash collision is 18% [collision-resistant] [15]. This probability can be changed based on user requirements, and specifically, it can be lowered by increasing the size of the LSH (with an increase in runtime), or by using multiple hash functions in conjunction to build a Bloom filter rather than an LSH[16].”))).
Titus teaches that the locality-sensitive hashing (LSH) (“coding function”) is hash-resistant but does not expressly disclose that it is non-invertible (but see Mainali ¶ 155 (“Some of the embodiments described here comprise a context-aware authentication system that protects the confidentiality of location data by transforming the location data into a location-hash with an irreversible [non-invertible] transformation using a locality-sensitive hashing technique. Such Location-hashes only reveal a relative measure of similarity between vectors of location related data without revealing the actual locations or even the actual distances between locations while still allowing to make reliable comparisons of relative degrees of similarity between different pairs of such location related data vectors. I.e., the values of the location-hashes do not give any information about the real distance in meters between the locations nor do they say anything about the individual locations. Consequently, the user does not have to worry about the confidentiality of the location data, as it is impossible to know the actual locations from the location-hashes that may be stored in a log for future usage by the authentication system. Since the confidentiality of the location data is well protected, the user can be more easily convinced to provide a static setting to always allow collecting the location data.”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Titus to incorporate the teachings of Mainali to use non-invertible hashing as the coding function because it would give away any information about the genetic sequence data of the users.
Titus further discloses:
storing the encrypted fragment data in a non-transitory medium to provide stored encrypted fragment data in the non-transitory medium, said stored encrypted fragment data being capable of enabling secure searching without decryption, (p. 3 (LSH fragments are stored in database of database owner), p. 9 (“The overall security of an encrypted data manipulation algorithm is fundamental to its success. The SIG-DB algorithm leverages established homomorphic encryption schemes to ensure that no outside (non-participating) party can learn the details of specific queries; only information about the query request patterns can be learned (We did not conduct a formal security analysis of homomorphic encryption, as it has been done previously[11–13]). It also ensures that no outside party can view the database records directly. Only metadata is exchanged in the form of encrypted LSH magnitudes, and thus individual data entries are never exposed outside the owners’ own security systems.”)) wherein
the step of forming the sequence fragments takes place such that the sections of the series of sequence elements overlap and each sequence element is included in at least two sequence fragments (see Fig. 1 (“In SIG-DB, the k-mers are created using a sliding window of 1 character, as illustrated, with a sequence of length n resulting in `n-k' k-mers.”)).
Regarding claim 2, Titus, in view of Mainali, discloses the invention of claim 1 as discussed above. Titus further discloses wherein
the fragment length of each sequence fragment at least 3 (see Fig. 1 (the k-mers fragments are TATCAGA, ATCAGAT, and TCAGATA)).
Regarding claim 4, Titus, in view of Mainali, discloses the invention of claim 1 as discussed above. Titus further discloses wherein
all the sequence fragments have the same length (see Fig. 1 (the k-mers fragments are TATCAGA, ATCAGAT, and TCAGATA)).
Regarding claim 5, Titus, in view of Mainali, discloses the invention of claim 1 as discussed above. Titus further discloses wherein
the sequence fragments form a plurality of fragment groups of sequence fragments, (see Table 1 (k-mer sequences of length 8, 16, and 32)) wherein
the sequence fragments in each fragment group each have the same length, (see Table 1 (all the sequences of a particular K-mer size have the same length))
the sequence fragments of different fragment groups have different lengths, and (see Table 1 (sequences of different K-mer sizes have different lengths))
the forming the sequence fragments takes place such that in each fragment group the sections of the series of sequence elements overlap and each sequence element is included in at least two sequence fragments (see Fig. 1 (K-mer window size is according to the desired length and each successive k-mer sequence overlaps with the next one)).
Regarding claim 6, Titus, in view of Mainali, discloses the invention of claim 1 as discussed above. Titus further discloses wherein
the encrypted fragment data include hash values (p. 3 (“There are two major components of the SIG-DB protocol. First, the genomic sequences are converted to a storage efficient data structure. To do so, we chose to use k-mers and locality sensitive hashing (LSH) [coding function] ...LSHs are also space efficient data structures that store information as either a 1 or 0 and, unlike Bloom filters, force the data to a preset vector size that is smaller than the original data dimensions. For example, the sequence `TATCAGA' would represent a 1 in a separate location within the LSH than `ATCAGAT' (see Fig 1).”)).
Titus does not expressly disclose genomic sequences includes hash values with a length that amounts to at least 128 bits. However, Titus pp. 3-4 teaches: “The size of the LSH is a trade-off between computational runtime and the likelihood of a false match (i.e. hash collision) in the LSH. For our application, the LSH length is set to be five times the length of the longest sequence of all the sequences being compared. Thus, the probability of a hash collision is 18% [15]. This probability can be changed based on user requirements, and specifically, it can be lowered by increasing the size of the LSH (with an increase in runtime), or by using multiple hash functions in conjunction to build a Bloom filter rather than an LSH[16].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Titus to try a longer hash length of 128 bits at least because increasing the hash size would lower the probability of a hash collision.
Regarding claim 7, Titus, in view of Mainali, discloses the invention of claim 1 as discussed above. Titus further discloses wherein the step of forming the sequence fragments before the application of the coding function comprises
addition, in each case, of a stochastically selected character string to each of the sequence fragments (p. 5 (“Uniformly distributed, random mutations were introduced to query sequences in silico n 5% increments, ranging from 0%-100%.”)).
Regarding claim 8, Titus, in view of Mainali, discloses the invention of claim 1 as discussed above. Titus further discloses wherein
genetic data from a plurality of individuals are processed, wherein the genetic data of each individual comprise a series of sequence elements which represent, in each case, a biomolecule (p. 3 (“The SIG-DB algorithm is intended for use between two parties: a Querier and a Database Owner who is willing-but-unable to share his genomic data without having a way to protect it. For example, these parties may be businesses wanting to keep their proprietary information protected while collaborating, hospitals collaborating on research while considering HIPAA requirements, or an investigator and a genomic data company.”)).
Regarding claim 9, Titus discloses [a] data processing apparatus which is configured for generating and storing encrypted fragment data with the method according to claim 1, comprising
- a fragmenting device (p. 12 SIG-DB algorithm testing (“Our algorithm testing was conducted on an NVIDIA Titan X GPU with 64 GB of memory.”)) which is configured for forming the sequence fragments such that each sequence fragment comprises a section of the series of sequence elements with a fragment length of at least two sequence elements and the sections of the series of sequence elements overlap and each sequence element is included in at least two sequence fragments, (see Fig. 1 (“In SIG-DB, the k-mers are created using a sliding window of 1 character, as illustrated, with a sequence of length n resulting in `n-k' k-mers.”), p. 3 (“There are two major components of the SIG-DB protocol. First, the genomic sequences are converted to a storage efficient data structure. To do so, we chose to use k-mers [sequence fragments]… . The use of k-mers is an established method of breaking up genetic sequences without appreciative data loss; the size of the k-mer can be optimized for a specific application.”); see Fig. 1 (the k-mers fragments are TATCAGA, ATCAGAT, and TCAGATA)))
- a coding device (p. 12 SIG-DB algorithm testing (“Our algorithm testing was conducted on an NVIDIA Titan X GPU with 64 GB of memory.”)) which is configured for generating the plurality of encrypted fragment data by applying a coding function2 to each of the sequence fragments in order to generate a plurality of encrypted fragment data items, each being associated with one of the sequence fragments, wherein the coding function is a non-invertible and collision-resistant cryptographic hash function, and (pp. 3-4 (“There are two major components of the SIG-DB protocol. First, the genomic sequences are converted to a storage efficient data structure. To do so, we chose to use k-mers and locality sensitive hashing (LSH) [coding function] ...LSHs are also space efficient data structures that store information as either a 1 or 0 and, unlike Bloom filters, force the data to a preset vector size that is smaller than the original data dimensions. For example, the sequence `TATCAGA' would represent a 1 in a separate location within the LSH than `ATCAGAT' (see Fig 1). The size of the LSH is a trade-off between computational runtime and the likelihood of a false match (i.e. hash collision) in the LSH. For our application, the LSH length is set to be five times the length of the longest sequence of all the sequences being compared. Thus, the probability of a hash collision is 18% [collision-resistant] [15]. This probability can be changed based on user requirements, and specifically, it can be lowered by increasing the size of the LSH (with an increase in runtime), or by using multiple hash functions in conjunction to build a Bloom filter rather than an LSH[16].”))).
Titus teaches that the locality-sensitive hashing (LSH) (“coding function”) is hash-resistant but does not expressly disclose that it is non-invertible (but see Mainali ¶ 155 (“Some of the embodiments described here comprise a context-aware authentication system that protects the confidentiality of location data by transforming the location data into a location-hash with an irreversible [non-invertible] transformation using a locality-sensitive hashing technique. Such Location-hashes only reveal a relative measure of similarity between vectors of location related data without revealing the actual locations or even the actual distances between locations while still allowing to make reliable comparisons of relative degrees of similarity between different pairs of such location related data vectors. I.e., the values of the location-hashes do not give any information about the real distance in meters between the locations nor do they say anything about the individual locations. Consequently, the user does not have to worry about the confidentiality of the location data, as it is impossible to know the actual locations from the location-hashes that may be stored in a log for future usage by the authentication system. Since the confidentiality of the location data is well protected, the user can be more easily convinced to provide a static setting to always allow collecting the location data.”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Titus to incorporate the teachings of Mainali to use non-invertible hashing as the coding function because it would give away any information about the genetic sequence data of the users.
Titus further discloses:
- a storage device which is configured for storing the encrypted fragment data being capable of enabling secure searching without decryption (p. 12 SIG-DB algorithm testing (“Our algorithm testing was conducted on an NVIDIA Titan X GPU with 64 GB of memory.”), p. 9 (“The overall security of an encrypted data manipulation algorithm is fundamental to its success. The SIG-DB algorithm leverages established homomorphic encryption schemes to ensure that no outside (non-participating) party can learn the details of specific queries; only information about the query request patterns can be learned (We did not conduct a formal security analysis of homomorphic encryption, as it has been done previously[11–13]). It also ensures that no outside party can view the database records directly. Only metadata is exchanged in the form of encrypted LSH magnitudes, and thus individual data entries are never exposed outside the owners’ own security systems.”)).
Regarding claim 10, Titus discloses [a] computer program product which is stored on a non-transitory computer-readable storage medium which when executed by a processor, causes the processor to (p. 12 SIG-DB algorithm testing (“Our algorithm testing was conducted on an NVIDIA Titan X GPU with 64 GB of memory.”)) form the sequence fragments and generate the plurality of encrypted fragment data in a method according to claim 1 (see claim 1 analysis above).
Regarding claim 11, Titus discloses [a] non-transitory computer-readable storage medium on which a computer program product is stored which when executed by a processor cause the processor to (p. 12 SIG-DB algorithm testing (“Our algorithm testing was conducted on an NVIDIA Titan X GPU with 64 GB of memory.”)) form the sequence fragments and generate the plurality of encrypted fragment data in a method according to claim 1 (see claim 1 analysis above).
Regarding claim 12, Titus discloses [a] system comprising: a database stored in a non-transitory memory and configured to store a plurality of searchable, encrypted fragment data which have been generated with a method according to claim 1 (p. 3 (“The SIG-DB algorithm is intended for use between two parties: a Querier and a Database Owner who is willing-but-unable to share his genomic data without having a way to protect it. For example, these parties may be businesses wanting to keep their proprietary information protected while collaborating, hospitals collaborating on research while considering HIPAA requirements, or an investigator and a genomic data company.”)) wherein the system further comprises a processor configured to access and process the encrypted fragment data (p. 12 SIG-DB algorithm testing (“Our algorithm testing was conducted on an NVIDIA Titan X GPU with 64 GB of memory.”)).
Regarding claim 13, Titus discloses [a] method for querying a database containing encrypted fragment data (p. 1 (“We present an algorithm for the Secure Interrogation of Genomic DataBases (SIG-DB). The SIG-DB algorithm enables databases of genomic sequences to be searched with an encrypted query sequence without revealing the query sequence to the Database Owner or any of the database sequences to the Querier.”)) which have been generated and stored with a method according to claim 1, (see claim 1 analysis above) comprising the steps
specifying a search sequence comprising a predetermined series of sequence elements which represent, in each case, a biomolecule, (p. 11 Query preparation (“To build the LSH with the appropriate false positive rate and minimize computational burden, the maximum length of a sequence in the database must be obtained from the Database Owner to serve as a proxy for the number of unique k-mers possible. From this implementation, the LSH is constructed to have a 5:1 ratio of available space to filled locations. For testing purposes, sequences with maximum length of 20,000 base pairs were used, thus LSHs were initialized to have 100,000 available hash locations. For sequences longer than 20,000 base pairs, the first 20,000 bases were used, and sequences shorter than 20,000 base pairs were used in their entirety. Sequence k-mers were hashed into the LSH using the Python hash function.”))
applying the coding function, with which the encrypted fragment data have been generated, on the search sequence for generating an encrypted search sequence, and (p. 11 Query preparation (“Homomorphic encryption was implemented with the Python package phe[19], implementing the Paillier additive homomorphic encryption system (PHE)[12]. Under this system, a public key/private key pair is generated and each element of an LSH is encrypted using the public key. The Paillier cryptosystem is based on integer factorization, and as such our default key size (DEFAULT_SIZE = 3072) is chosen to give a minimum of 128 bits of security.”))
searching for the encrypted search sequence in the stored encrypted fragment data (p. 11 Database searching (“An LSH is created for each database entry using the LSH Constructor, provided by the Querier. The Database Owner executes the comparison between each DB entry LSH and the encrypted Query LSH using the comparison executable provided by the Querier. For each comparison, an encrypted intersection score is calculated by the sum of all the encrypted Query LSH hash locations corresponding to a filled hash entry in the DB entry's LSH. The magnitude of the DB entry LSH is calculated as the sum of hashes in the LSH. The pair {encrypted intersection score, DB entry LSH magnitude} for each entry in the database are then returned to the Querier for evaluation.”)).
Regarding claim 15, Titus, in view of Mainali, discloses the invention of claim 1 as discussed above. Titus further discloses wherein
the encrypted fragment data are stored in a database (p. 3 (“The SIG-DB algorithm is intended for use between two parties: a Querier and a Database Owner who is willing-but-unable to share his genomic data without having a way to protect it.”)).
Regarding claim 16, Titus, in view of Mainali, discloses the invention of claim 1 as discussed above. Titus further discloses wherein
a predetermined series of sequence elements comprises a section of genetic material (see Fig. 1 (nucleotide sequence)).
Regarding claim 17, Titus, in view of Mainali, discloses the invention of claim 1 as discussed above. Titus further discloses wherein
the genetic data represent a nucleotide sequence or an amino acid sequence (see Fig. 1 (nucleotide sequence)).
Regarding claim 18, Titus, in view of Mainali, discloses the invention of claim 9 as discussed above. Titus does not expressly disclose wherein the encrypted fragment data comprise hash values with a length that amounts to at least 128 bits. However, Titus pp. 3-4 teaches: “The size of the LSH is a trade-off between computational runtime and the likelihood of a false match (i.e. hash collision) in the LSH. For our application, the LSH length is set to be five times the length of the longest sequence of all the sequences being compared. Thus, the probability of a hash collision is 18% [15]. This probability can be changed based on user requirements, and specifically, it can be lowered by increasing the size of the LSH (with an increase in runtime), or by using multiple hash functions in conjunction to build a Bloom filter rather than an LSH[16].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Titus to try a longer hash length of 128 bits at least because increasing the hash size would lower the probability of a hash collision.
Claims 19 and 20 are apparatus and method claims corresponding to claim 18 and, therefore, are similarly rejected.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Titus and Mainali as applied to claim 1 above, and further in view of Zhang, Qingpeng, et al. "These are not the k-mers you are looking for: efficient online k-mer counting using a probabilistic data structure." PloS one 9.7 (2014): e101271 (“Zhang”).
Regarding claim 3, Titus, in view of Mainali, discloses the invention of claim 1 as discussed above. Titus further discloses wherein the step of forming the sequence fragments comprises
specifying the fragment length and a start element in the genetic data, and (Fig. 1 (“In SIG-DB, the k-mers are created using a sliding window of 1 character, as illustrated, with a sequence of length n resulting in `n-k' k-mers.”))
providing the sequence fragments, in each case, using the sections of the series of sequence elements with a predetermined fragment length beginning at the start element and at all the subsequent sequence elements (see Fig. 2 (“SIG-DB protocol for (1) hashing sequence into locality sensitive hash (LSH), (2) encrypting and passing LSH,…”)).
Titus does not expressly disclose specifying a start element in the genetic data (but see Zhang 11 Future Conditions (“It is possible to reduce the required memory by dividing k-mer space into multiple partitions and counting k-mers separately for each partition. Partitioning k-mer space into M partitions results in a linear decrease in the number of k-mers under consideration, thus reducing the occupancy by a constant factor M and correspondingly reducing the collision rate. Partitioning k-mer space is a generalization of the systematic prefix filtering approach, where one might first count all k-mers starting with AA, then AC, then AG, AT, CA, etc., which is equivalent to partitioning k-mer space into 16 equal-sized partitions. These partitions can be calculated independently, either across multiple machines or iteratively on a single machine, and the results stored for later comparison or analysis.”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Titus to incorporate the teachings of Zhang to partition the input sequence and initiate windowing at specific elements, at least because doing so would reduce the memory requirements so that the generated K-mers can be efficiently stored for later comparison or analysis. Zhang 11 (“It is possible to reduce the required memory by dividing k-mer space into multiple partitions and counting k-mers separately for each partition.”).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Titus and Mainali as applied to claim 13 above, and further in view of Glick (US 2009/0270277 A1; published Oct. 29, 2009).
Regarding claim 14, Titus, in view of Mainali, discloses the invention of claim 13 as discussed above. Titus teaches assessing a similarity between the query and entries in the database in scenarios with a query sequence longer than the database sequences, see p. 12, but Titus does not expressly disclose wherein
the specifying of the search sequence comprises a shortening of an initial search sequence to a search sequence length that is equal to the fragment length of the sequence fragments from which the encrypted fragment data have been generated (but see Glick ¶ 39 (“With a query longer than K bases, the constituent K-mers are examined in increasing order of their frequency of appearance in the subject DNA sequence. For example, a search for the 12-mer AAAACCCCGGGG using K=4 might involve calculating the positions for CCCC, then comparing each CCCC position against the list of positions for GGGG, then comparing each CCCCGGGG position against the list of positions for AAAA, which in this case would be the most common of the three 4-mers. This strategy of starting with the rarest K-mer can significantly accelerate searches because some K-mers are found less frequently than others and therefore result in fewer comparisons. In chromosome 1, the most common 4-mer (AAAA) appears 56 times more often than the rarest 4-mer (CGCG), and the most common 6-mer (TTTTTT) appears 929 times more often than the rarest 6-mer (CGTACG).”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Titus to incorporate the teachings of Glick to start with the rarest K-mer as the query sequence when the initial query sequence is longer than the longest K-mer used to build the hash sequences in the owner database, at least because doing so would significantly accelerate searching. See Glick ¶ 39.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHID KHAN whose telephone number is (571)270-0419. The examiner can normally be reached M-F, 9-5 est.
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/SHAHID K KHAN/Primary Examiner, Art Unit 2146
1 Applicant describes the coding function as a hash function. See Spec. ¶ 36 (“According to a further, particularly advantageous embodiment of the invention, the coding function is a hash function and the encrypted fragment data are hash vales. The hash function maps sequence fragments, i.e. successions of sequence elements of a freely selectable length, specifically non-invertibly, in each case, to one hash value. The use of the hash function for encryption has particular advantages since hash functions are available and well investigated and are non-invertible so that the decryption of the genetic data from the encrypted fragment data is precluded or extremely difficult.”).
2 Applicant describes the coding function as a hash function. See Spec. ¶ 36 (“According to a further, particularly advantageous embodiment of the invention, the coding function is a hash function and the encrypted fragment data are hash vales. The hash function maps sequence fragments, i.e. successions of sequence elements of a freely selectable length, specifically non-invertibly, in each case, to one hash value. The use of the hash function for encryption has particular advantages since hash functions are available and well investigated and are non-invertible so that the decryption of the genetic data from the encrypted fragment data is precluded or extremely difficult.”).
Prosecution Insights