DETAILED ACTION
This Office Action is in response to the communication filed on 7/18/2024.
Claims 1-15 are pending.
Claims 1-15 are rejected.
The Examiner cites particular sections in the references as applied to the claims below for the convenience of the applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant(s) fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
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 .
Claim Objections
Claim 10 is objected to because of the following informalities: “further comparing a user interface” it is not clear what is being compared.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 9, and 15 contain the phrase “minimal generated distance”. The specification do not provide boundaries for determining what distance is considered “minimal”, therefore the scope of the claims are not clear. Dependent claims 2-8 and 10-15 are also rejected under the same rationale set forth above.
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 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 nonobviousness.
Claims 1-7 and 9-15 are rejected under 35 U.S.C. 103 as being unpatentable over Shainski (U.S. 20200228308) in view of Hirano (U.S. 20180278410).
Regarding claims 1 and 9,
Shainski discloses: A method for encrypted similarity searching of a database in a server, comprising: (Shainski [Abstract] A system and method)
providing a database in a server, the database comprising a plurality of homomorphically-encrypted fixed-length data fingerprints, each fixed-length data fingerprint representing a data file in the database, wherein the plurality of homomorphically-encrypted fixed-length data fingerprints are randomly ordered in a fixed-size database table; (Shainski [0005]; Fully Homomorphic Encryption (FHE) cryptosystems; [0010-0014] comparing first and second data. The first data element may be the search query and the second data element the target data to be searched (homomorphically-encrypted fixed-length data fingerprint)… compare the homomorphically encrypted first token signature representing the first data element and an unencrypted or homomorphically encrypted second token signature representing a second data element and generate a homomorphically encrypted comparison of the first and second token signatures); [0022] By utilizing homomorphic encryption, ciphertext can be compared to ciphertext, or ciphertext can be compared to plaintext, such that, a data provider or query provider may be able to compare (1) a homomorphically encrypted search query and homomorphically encrypted target data being searched)
receiving an encrypted query from a remote system to be queried against the database in a similarity search in the server; (Shainski [0010-0014] The first data element may be the search query (encrypted query) and the second data element the target data to be searched… compare the homomorphically encrypted first token signature representing the first data element and an unencrypted or homomorphically encrypted second token signature representing a second data element and generate a homomorphically encrypted comparison of the first and second token signatures); [0022] By utilizing homomorphic encryption, ciphertext can be compared to ciphertext, or ciphertext can be compared to plaintext, such that, a data provider or query provider may be able to compare (1) a homomorphically encrypted search query and homomorphically encrypted target data being searched)
generating in the server, from the received query, a fixed-length query data fingerprint in the database; (Shainski [0010-0014]; [0061] In scenario (1), e.g., when the second data element 111 contains secret data, the second data provider 150 may be configured to homomorphically encrypt the second token signature 115 using a public homomorphic encryption key to generate a homomorphically encrypted second token signature 117 representing the second data element)
homomorphically encrypting the fixed-length query data fingerprint in the server; (Shainski [0010-0014]; [0061] In scenario (1), e.g., when the second data element 111 contains secret data, the second data provider 150 may be configured to homomorphically encrypt the second token signature 115 using a public homomorphic encryption key to generate a homomorphically encrypted second token signature 117 representing the second data element)
generating a fixed-size query data fingerprint table in the server, wherein the size of the fixed-size query data fingerprint table is the same as the size of the fixed-size database table; (Shainski [0033-0037] The target data being searched may be a file to be searched itself, such as documents, images, or videos, or may refer to data located within such files, such as a field, column, or row within a document, or may be metadata of any of these data... the data elements may be transformed into a set of tokens...[0037] The data elements may be transformed into a set of token)
comparing in the server, using the fixed-size query data fingerprint table and the fixed-size database table, the homomorphically-encrypted fixed-length query data fingerprint to every homomorphically-encrypted fixed-length data fingerprint, (Shainski [0010-0014] compare the homomorphically encrypted first token signature representing the first data element and an unencrypted or homomorphically encrypted second token signature representing a second data element and generate a homomorphically encrypted comparison of the first and second token signatures); [0022] By utilizing homomorphic encryption, ciphertext can be compared to ciphertext, or ciphertext can be compared to plaintext, such that, a data provider or query provider may be able to compare (1) a homomorphically encrypted search query and homomorphically encrypted target data being searched)
identifying in the server, using (Shainski [0010-0014] compare the homomorphically encrypted first token signature representing the first data element and an unencrypted or homomorphically encrypted second token signature representing a second data element and generate a homomorphically encrypted comparison of the first and second token signatures); [0022] By utilizing homomorphic encryption, ciphertext can be compared to ciphertext, or ciphertext can be compared to plaintext, such that, a data provider or query provider may be able to compare (1) a homomorphically encrypted search query and homomorphically encrypted target data being searched… This is because homomorphic encryption provides an injective or one-to-one (1:1) mapping between operations on plaintext and operations on ciphertext. Accordingly, a search that is a comparison between the query and target data, performed between a homomorphically encrypted ciphertext and a plaintext (or between two ciphertexts), generates a homomorphically encrypted comparison)
reporting the identified one or more data files in the database in the server. (Shainski [0010-0014]; [0022]; [0048] transmit the homomorphically encrypted comparison to a trusted third party, which may be the first data provider, or to another external party or device, to decrypt the homomorphically encrypted comparison)
While Shainski discloses comparisons which generate mappings, it does not explicitly disclose: wherein each comparison generates a distance;
identifying… using the generated distance between... one or more data files in the database having a minimal generated distance
However, in the same field of endeavor Hirano discloses: wherein each comparison generates a distance; (Hirano [0006]; [0021-0023]; [0064-0066]; [00128-0186]; [0210-0234, Fig. 9]; [0311-0321] teaches similarity search techniques capable of efficiently calculating the Hamming distance and generating an encryption search result representing whether the similarity degree is not more than the threshold value or not using the encryption similarity)
using the generated distance between… one or more data files in the database having a minimal generated distance (Hirano [0006]; [0021-0023]; [0064-0066]; [00128-0186]; [0210-0234, Fig. 9]; [0311-0321] similarity search techniques capable of efficiently calculating the Hamming distance and generating an encryption search result representing whether the similarity degree (minimum distance) is not more than the threshold value or not using the encryption similarity)
Shainski and Hirano are analogous art because they are from the same field of endeavor of similarity searching.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Shainski and Hirano before him or her, to modify the method of Shainski to include the Euclidean distance of Hirano because it will allow results to include objects which have a specific thresholds of similarity.
The motivation for doing so would be [“efficiently calculating the Hamming distance using special cryptographic techniques referred to as homomorphic encryptions capable of operations while keeping an encrypted state”] (Paragraph 0021 by Hirano)].
Therefore, it would have been obvious to combine Shainski and Hirano to obtain the invention as specified in the instant claim.
In addition, Regarding Claim 9, A system (Shainski [Abstract] A system and method) a database and a processor (Shainski [0021], []0066][-[0067])
Regarding claims 2 and 11,
Shainski in view of Hirano discloses: The method of claim 1, further comprising the step of generating the plurality of homomorphically-encrypted fixed-length data fingerprints in the server. (Shainski [0010-0014]; [0021-0025]; [0058-0069, 0072-0082, 0083-0093; Figs. 1 and 4] teaches the that the second data provider generates and homomorphically encrypts tokens during processes 113[Wingdings font/0xE0]115[Wingdings font/0xE0]117)
Regarding claims 3 and 12,
Shainski in view of Hirano discloses: The method of claim 1, further comprising the step of generating the fixed-size database table comprising the plurality of homomorphically-encrypted fixed-length data fingerprints in the server, (Shainski [0010-0014]; [0021-0025]; [0058-0069, 0072-0082, 0083-0093; Figs. 1 and 4] teaches the that the second data provider generates and homomorphically encrypts tokens during processes 113[Wingdings font/0xE0]115[Wingdings font/0xE0]117) wherein the plurality of homomorphically-encrypted fixed-length data fingerprints are randomly ordered within the fixed-size database table. (Shainski [0043] teaches that the tokens for the query and target data may be concatenated in random or different orders)
Regarding claims 4 and 13,
Shainski in view of Hirano discloses: The method of claim 1, wherein the (Shainski [0010-0014]; [0039-0043]; [0058-0069, 0072-0082, 0083-0093] teaches generating results (populating) according to the encrypted comparison results)
Additionally, Hirano discloses: distance between a query and a fingerprint
a distance table, and wherein the distance table is of the same size as the fixed-size query data fingerprint table and the fixed-size database table. (Hirano [0006]; [0021-0023]; [0064-0066]; [0128-0186]; [0210-0234, Fig. 9]; [0311-0321] teaches similarity search techniques capable of efficiently calculating the Hamming distance and generating an encryption search result representing whether the similarity degree is not more than the threshold value or not using the encryption similarity where a distance table is calculated using the homomorphic encryption specified in example 1 and 2)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify with Hirano for similar reasons as cited in claim 1.
Regarding claims 5 and 14,
Shainski in view of Hirano discloses: The method of claim 1, wherein the generated (Shainski [0062] teaches generate at least one homomorphically encrypted comparison 119 of the first and second token signatures)
While Shainski teaches mapping, Shainski does not explicitly teach distance
However, in the same field of endeavor Hirano teaches that the comparison can be a distance (Hirano [0006]; [0021-0023]; [0064-0066]; [0128-0186]; [0210-0234, Fig. 9]; [0311-0321] teaches similarity search techniques capable of efficiently calculating the Hamming distance and generating an encryption search result representing whether the similarity degree is not more than the threshold value or not using the encryption similarity where a distance table is calculated using the homomorphic encryption specified in example 1 and 2)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify with Hirano for similar reasons as cited in claim 1.
Regarding claim 6,
Shainski in view of Hirano discloses: The method of claim 1, wherein the reporting further comprises
Hirano additionally discloses: the generated distance between the query and the identified one or more data files in the database. (Hirano [0006]; [0021-0023]; [0064-0066]; [0128-0186]; [0210-0234, Fig. 9]; [0311-0321] teaches similarity search techniques capable of efficiently calculating the Hamming distance and generating an encryption search result representing whether the similarity degree is not more than the threshold value or not using the encryption similarity where a distance table is calculated using the homomorphic encryption specified in example 1 and 2)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify with Hirano for similar reasons as cited in claim 1.
Regarding claim 7,
Shainski in view of Hirano discloses: The method of claim 1, wherein the encrypted query is an updated version of a database, and wherein each fixed-length data fingerprint represents a version of a database or a version of a data file in the database. (Shainski [0010-0014]; [0039-0043]; [0058-0069, 0072-0082, 0083-0093] teaches updating the database with the latest version which is locally stored)
Regarding claim 10,
Shainski in view of Hirano discloses: The system of claim 9, further comparing a user interface configured to provide the generated report. (Shainski [0077-0079] Data provider computers 340 and 350 may include one or more output devices 344 and 354 (e.g., a monitor or screen) for displaying data (generated report) to a user provided by or for computation host server(s) 310.
Regarding claim 15,
Shainski in view of Hirano discloses: The system of claim 9, wherein the minimal generated distance is determined by a predetermined setting or is user defined. (Hirano [0006]; [0021-0023]; [0064-0066]; [00128-0186]; [0210-0234, Fig. 9]; [0311-0321] teaches a threshold value of the hamming distance/degree of similarity, which is determined before it is used)
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Shainski (U.S. 20200228308) in view of Hirano (U.S. 20180278410), and in further view of Zadeh (U.S. 20200184278).
Regarding claim 8,
Shainski in view of Hirano discloses: The method of claim 1,
Shainski discloses: wherein the encrypted query is an updated version of a (Shainski [0010-0014]; [0039-0043]; [0058-0069, 0072-0082, 0083-0093] teaches updating the database with the latest version which is locally stored)
Shainski in view of Hirano does not explicitly disclose reference genome
However in the same field of endeavor Zadeh discloses: reference genome (Zadeh [2035] teaches that the information being queried and indexed can be biometrics, gene or DNA sequence, medical data, medical history, medical knowledge, chemical formulas)
Shainski in view of Hirano and Zadeh are analogous art because they are from the same field of endeavor similarity searching.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Shainski in view of Hirano and Zadeh before him or her, to modify the method of Shainski in view of Hirano to include the genome references of Zadeh because it will genome references to be searched.
The motivation for doing so would be [because it will allow for extremely efficient pattern recognition… to be performed on… “biometrics, gene or DNA sequence, medical data, medical history, medical knowledge, chemical formulas”] (Paragraph [Abstract, 2035] by Zadeh)].
Therefore, it would have been obvious to combine Shainski in view of Hirano and Zadeh to obtain the invention as specified in the instant claim.
ADDITIONALLY/ALTERNATIVELY
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Yavuz (U.S. 20190340381), in view of Shainski (U.S. 20200228308), and in further view of Zadeh (U.S. 20200184278).
Regarding claims 1 and 9,
Yavuz discloses: A method for encrypted similarity searching of a database in a server, comprising: (Shainski [0002] systems and methods)
providing a database in a server, the database comprising a plurality of homomorphically-encrypted fixed-length data fingerprints, each fixed-length data fingerprint representing a data file in the database, wherein the plurality of homomorphically-encrypted fixed-length data fingerprints are randomly ordered in a fixed-size database table; (Yavuz [0003-0015] teaches Symmetric Searchable Encryption (SSE) and Dynamic-SSE (DSSE), for performing searches in an encrypted search index (fingerprint) and updating the encrypted search index… The server receives the encrypted search terms/query…; [0026-0035] teaches that the encrypted index (encrypted fingerprint) can be a predetermined/fixed length “trapdoor function” to avoid patterns in the length which provides collision resistance; [0011, 0029, 0043-0055] teaches that the indexes (fingerprints) can be arranged in a random order)
receiving an encrypted query from a remote system to be queried against the database in a similarity search in the server; (Yavuz [0003-0015, 0047-0048, 0061-0063, 0075-0077; Figs. 2, 3, 4, 5] teaches a client sending an encrypted search query to a server in order to have the server search a database for matching (similar) encrypted indices)
generating in the server, from the received query, a fixed-length query data fingerprint in the database; (Yavuz [0003-0015, 0065-0076] teaches adding new files and/or keywords to the encrypted search indices (DSSE); The client sends the update to the server and the server adds (generates) the new fingerprint to the database)
homomorphically encrypting the fixed-length query data fingerprint in the server; (Yavuz [0003-0015] teaches homomorphically encrypting the fingerprint which is located “in the server” after it is transferred to the server)
generating a fixed-size query data fingerprint table in the server, wherein the size of the fixed-size query data fingerprint table is the same as the size of the fixed-size database table; (Yavuz [0003-0015, 0042-0058, Figs. 3, 4, 5] teaches the generating of the tables, which are fixed size “2N×2N table of random binary values”, which is the same size as the encrypted data structure and is subsequently located “in the server”, the number of rows and columns correspond to the keywords and identifiers in the search table; [0055-0071] teaches that the query contains the same number of rows and columns as previously encoded (i.e. the server will find tables with thew correct number of rows then search tables with the correct numbers of rows to identify the tables with the correct number of rows and correct number of columns))
comparing in the server, using the fixed-size query data fingerprint table and the fixed-size database table, the homomorphically-encrypted fixed-length query data fingerprint to every homomorphically-encrypted fixed-length data fingerprint, (Yavuz [0003-0015, 0027-0028, 0042-0046, 0051-0059, 0069-0077, Figs. 2, 3, 4, 5] teaches matching search query to index where all rows/columns of a specific length are searched)
identifying in the server, using (Yavuz [0003-0015, 0027-0028, 0042-0046, 0051-0059, 0069-0077, Figs. 2, 3, 4, 5] teaches matching search query to index where all rows/columns of a specific length are searched)
reporting the identified one or more data files in the database in the server. (Yavuz [0033, 0038, 0044, 0064, 0067] teaches transmitting search results to the trusted client)
While Yavuz discloses symmetric encryption, it does not explicitly disclose homomorphically encrypting However, in the same field of endeavor Shainski discloses homomorphically encrypting (Shainski [0005] Fully Homomorphic Encryption (FHE) cryptosystems)
Regarding Claim 9,
Yavuz discloses: A system (Shainski [0002] systems and methods)
Yavuz and Shainski are analogous art because they are from the same field of endeavor Secure searching of databases.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Yavuz and Shainski before him or her, to modify the method of Yavuz to include the homomorphic encryption of Shainski because it will allow a third party to evaluate any computation on encrypted data without learning anything about it.
The motivation for doing so would be [“Fully Homomorphic Encryption (FHE) cryptosystems allow a third party to evaluate any computation on encrypted data without learning anything about it, such that only the legitimate recipient of the homomorphic calculation will be able to decrypt it using the recipient's secret key.”] (Paragraph 0005 by Shainski)].
Therefore, it would have been obvious to combine Yavuz and Shainski to obtain the invention as specified in the instant claim.
While Yavuz in view of Shainski discloses comparing a query to a database’s index, it does not explicitly disclose: wherein each comparison generates a distance;
Additionally, Yavuz discloses identifying a result, it does not explicitly disclose: identifying… using the generated distance between... one or more data files in the database having a minimal generated distance
However, in the same field of endeavor Zadeh discloses: wherein each comparison generates a distance; (Zadeh [1443-1489]; [1866-1967, 1878, 1913-1932]; [2191-2259]; [2281-2289]; [2386-2391] teaches pattern recognition or classification, we use clustering tree, e.g., with Euclidean distance or Hamming distance, or use Fuzzy Membership Roster Method to determine the closeness degree of search results for information retrieval)
using the generated distance between… one or more data files in the database having a minimal generated distance (Zadeh [1443-1489]; [1866-1967, 1878, 1913-1932]; [2191-2259]; [2281-2289]; [2386-2391] teaches for matching, the system uses a minimum distance classifier and the Euclidean distance to determine closeness and correlation between two objects)
Yavuz in view of Shainski and Zadeh are analogous art because they are from the same field of endeavor of similarity searching.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Yavuz in view of Shainski and Zadeh before him or her, to modify the method of Yavuz in view of Shainski to include the Euclidean distance of Zadeh because it will allow for extremely efficient pattern recognition.
The motivation for doing so would be [“to reduce the size and get the common features for batch processing, faster search, faster data extraction, better data presentation, and more efficient data storage”] (Paragraph 1360 by Zadeh)].
Therefore, it would have been obvious to combine Yavuz in view of Shainski and Zadeh to obtain the invention as specified in the instant claim.
Regarding claims 2 and 11,
Yavuz in view of Shainski and Zadeh discloses: The method of claim 1, further comprising the step of generating the plurality of homomorphically-encrypted fixed-length data fingerprints in the server. (Yavuz [0003-0015, 0065-0076] teaches adding new files and/or keywords to the encrypted search indices (DSSE); The client sends the update to the server and the server adds (generates) the new (homomorphically encrypted) fingerprint to the database)
Regarding claims 3 and 12,
Yavuz in view of Shainski and Zadeh discloses: The method of claim 1, further comprising the step of generating the fixed-size database table comprising the plurality of homomorphically-encrypted fixed-length data fingerprints in the server, (Yavuz [0003-0015, 0065-0076] teaches adding new files and/or keywords to the encrypted search indices (DSSE); The client sends the update to the server and the server adds (generates) the new (homomorphically encrypted) fingerprint to the database) wherein the plurality of homomorphically-encrypted fixed-length data fingerprints are randomly ordered within the fixed-size database table. (Yavuz [0011, 0029, 0043-0055] teaches that the indexes (fingerprints) can be arranged in a random order
Regarding claims 4 and 13,
Yavuz in view of Shainski and Zadeh discloses: The method of claim 1, wherein the (Yavuz [0003-0015] teaches Symmetric Searchable Encryption (SSE) and Dynamic-SSE (DSSE), for performing searches in an encrypted search index (fingerprint) and updating the encrypted search index… The server receives the encrypted search terms/query…; [0026-0035] teaches that the encrypted index (encrypted fingerprint) can be a predetermined/fixed length “trapdoor function” to avoid patterns in the length which provides collision resistance; [0011, 0029, 0043-0055] teaches that the indexes (fingerprints) can be arranged in a random order)
Additionally, Zadeh discloses: distance between a query and a fingerprint
a distance table, and wherein the distance table is of the same size as the fixed-size query data fingerprint table and the fixed-size database table. (Zadeh [1443-1489]; [1866-1967, 1878, 1913-1932]; [2191-2259]; [2281-2289]; [2386-2391] teaches pattern recognition or classification, we use clustering tree, e.g., with Euclidean distance or Hamming distance, or use Fuzzy Membership Roster Method to determine the closeness degree of search results for information retrieval; [1381] teaches that in relational databases, data can be indexed; [2654] In one embodiment, as for example depicted in FIG. 231, the clusters of data in the multi-dimensional (d) (e.g., a highly dimensional) feature space are formed to facilitate fast indexing and search. In such a situation, it may be more efficient to index (e.g., a nested index) based on clusters as opposed to indexing based on highly dimensional features. For example, for a given feature set associated with an image or object in an image, there may not be an exact match found in the database, and similar objects (in feature space) with close feature distance may be searched for)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify with Zadeh for similar reasons as cited in claim 1.
Regarding claims 5 and 14,
Yavuz in view of Shainski and Zadeh discloses: The method of claim 1,
Additionally Shainski teaches: wherein the generated distance is homomorphically encrypted. (Shainski [0062] teaches generate at least one homomorphically encrypted comparison 119 of the first and second token signatures)
Zadeh teaches that the comparison can be a distance (Zadeh [1443-1489]; [1866-1967, 1878, 1913-1932]; [2191-2259]; [2281-2289]; [2386-2391] teaches pattern recognition or classification, we use clustering tree, e.g., with Euclidean distance or Hamming distance, or use Fuzzy Membership Roster Method to determine the closeness degree of search results for information retrieval;
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify with Zadeh for similar reasons as cited in claim 1.
Regarding claim 6,
Yavuz in view of Shainski and Zadeh discloses: The method of claim 1, wherein the reporting further comprises
Zadeh additionally discloses: the generated distance between the query and the identified one or more data files in the database. (Zadeh [2654] In one embodiment, as for example depicted in FIG. 231, the clusters of data in the multi-dimensional (d) (e.g., a highly dimensional) feature space are formed to facilitate fast indexing and search. In such a situation, it may be more efficient to index (e.g., a nested index) based on clusters as opposed to indexing based on highly dimensional features. For example, for a given feature set associated with an image or object in an image, there may not be an exact match found in the database, and similar objects (in feature space) with close feature distance may be searched for)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify with Zadeh for similar reasons as cited in claim 1.
Regarding claim 7,
Yavuz in view of Shainski and Zadeh discloses: The method of claim 1, wherein the encrypted query is an updated version of a database, and wherein each fixed-length data fingerprint represents a version of a database or a version of a data file in the database. (Yavuz [0003-0015, 0053, 0072-0078] teaches version control and Dynamic-SSE (DSSE) teaches that the query can optionally be an update to the database)
Regarding claim 8,
Yavuz in view of Shainski and Zadeh discloses: The method of claim 1,
Additionally, Zadeh discloses: wherein the encrypted query is an updated version of a reference genome, and wherein each fixed-length data fingerprint represents a reference genome. (Zadeh [2035] teaches that the information being queried and indexed can be biometrics, gene or DNA sequence, medical data, medical history, medical knowledge, chemical formulas)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify with Zadeh for similar reasons as cited in claim 1.
Regarding claim 10,
Yavuz in view of Shainski and Zadeh discloses: The system of claim 9, further comparing a user interface configured to provide the generated report. (Yavuz [0015, 0034] teaches a network adapter that communicatively couples the trusted client which is used to receive the results (generated report))
Regarding claim 15,
Yavuz in view of Shainski and Zadeh discloses: The system of claim 9, wherein the minimal generated distance is determined by a predetermined setting or is user defined. (Zadeh [0384-0399] Often, the reference-user's insights will be used to prospectively update the analytics, such as the distance function)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify with Zadeh for similar reasons as cited in claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
Gentry 2019-6-18 (US 20200403781) teaches using homomorphic encryption at the bit level.
Gentry September 2009 (A FULLY HOMOMORPHIC ENCRYPTION SCHEME) teaches searching over homomorphically encrypted data.
Katuwal 2020-09-25 (US 20210098092) teaches a search system can use a distributed ledger to securely correlate similar instances of medical data using hashes.
Cousins 2021-10-19 (US 20220121770) teaches An efficient search of a target string by a query string in homomorphically encrypted space.
KONINKLIJKE PHILIPS N.V. - Pletea 2017-6-16 (US 20190333607) teaches anonymization of genetic data where the anonymization techniques include “searchable encryption”, “homomorphic encryption” and “secure multiparty computation”.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS A CARNES whose telephone number is (571)272-4378. The examiner can normally be reached Monday-Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shewaye Gelagay can be reached at (571) 272-4219. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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THOMAS A. CARNES
Examiner
Art Unit 2436
/THOMAS A CARNES/ Examiner, Art Unit 2436 /SHEWAYE GELAGAY/Supervisory Patent Examiner, Art Unit 2436