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 .
DETAILED ACTION
This Office Action is in response to the Application 18/851,681filed on 09/27/2023; Claims 1-17 have been amended; Claims 18-20 have been added; Claims 1, 16, and 17 are independent claims. Claims 1-20 have been examined and are pending. This Action is Non-FINAL.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 22164700.1, filed on Mar. 28, 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/27/2024 is being considered by the examiner.
Drawing Objections
The drawing (figures 1, 2a-2, 3a-3c, 4a-4e, 5, 6a-6b, 7, 8a-8b, 9a-9b) are objected to because there is no short description for labels. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Regarding claim 16, the claim in this application is given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word "means" (or "step") in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Absence of the word "means" (or "step") in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word "means" (or "step") are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word "means" (or "step") are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word "means," but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a communication interface configured for receiving / a database interface configured for accessing / a processor system configured for searching” recited in claim 16. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
Claim 1 is objected to because of the following informalities:
Regarding claim 1; claim 1 recites the “PUF” in line 4. the acronym CPU should be spelled out in full as its first occurrence. Appropriate correction is required.
Regarding claim 16; claim 16 recites the “PUF” in line 4. the acronym CPU should be spelled out in full as its first occurrence. 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 4, 15, and 20 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.
Regarding claim 4, Claim 4 recites the limitation “the dependent thresholds” in line 2. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 15, Claim 15 recites the limitation "the external PUF string” in lines 3. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 20, Claim 20 recites the limitation “the dependent thresholds” in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U. S. C. 101 as being directed to an abstract idea without being integrated into a practical application or significantly more.
Regarding claims 1, 16, and 17, the claims are directed to an abstract idea as reciting the limitations “searching …. for a matching enrollment PUF string in a database, ..” The aforementioned step is “mental process/mathematical calculation/re-arranging human activities” as broadly interpreted said steps could be performed in the human mind or with pen and paper. Therefore, the claim recites an abstract idea.
Said abstract idea is not integrated into a practical application as the claim does not recite any other active steps that utilize determination result into a practical application. It’s noted that the claims recite the steps of “receiving a PUF string …” The recording step is recited at a high level of generality (i.e., as a general means of recording/gathering data for use in the determining and deriving steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
The claims do not include additional elements that are sufficient to amount to significantly. It’s noted that the claims recite additional elements (i.e., processor system). However, said additional elements are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of deriving and/or determining a degree etc.,) such that it amounts no more than mere instructions to apply the exception or abstract idea using a generic computer component.
As discussed above, the additional elements recited at a high-level of generality such that they amount no more than mere instructions to apply the exception using a generic computer component. Therefore, the claim is directed to non-statutory subject matter.
Regarding claims 2-15 and 18-20, claims 2-15 and 18-20 are also rejected under 35 USC 101 as being directed to an abstract idea without being integrated into a practical application or significantly more as discussed above. It’s noted that claim 11 recites the step of “reporting information …,” and “adding the received PUF string …,” are mental process; and “sending information ..,” and “logging information …;” are amounts to mere data gathering, which is a form of insignificant extra-solution activity. However, said additional elements are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of sending/receiving/computing/ logging/ and/or determining a degree etc.,) such that they amount no more than mere instructions to apply the exception or abstract idea using a generic computer component. Therefore, claims 2-15 and 18-20 are also rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3, 5, 12-13, 16, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Paral et al. (“Paral,” US 2012/0183135) in view of Braithwaite et al. (“Braithwaite,” US 2004/0193893), and further in view of Peirce et al. (“Peirce,” US 7,545,962).
Regarding claim 1, Paral teaches a method comprising:
receiving a PUF string, the PUF string having been obtained by measuring an external PUF device (Paral: par. 0003. “Silicon PUFs generate signatures based on device manufacturing variations [], Given a challenge as input, a PUF outputs a response that is unique to the manufacturing instance of the PUF circuit; par. 0006, employing one or more challengeable Physical Unclonable Function (PUF) circuit elements together form the device that produces the PUF response; par. 0043, PUF output Blender 212).
searching, by a processor system, for a matching enrollment PUF string in a database (Paral: pars. 0003-0004, “PUF outputs a response”; par. 0004, the silicon device is authenticated if the regenerated response is "close enough" in Hamming distance to the provisioned response; par. 0045, Tolerant Pattern Match Detector 240: The detector fires if the pattern in the Pattern Shift Register is within the threshold T of the selected pattern in the Persistent Helper data Store ; par. 0046, Persistent Helper data Store 250 and Pattern Selector: Patterns for each round are stored in the Helper data Store during provisioning);
the database comprising multiple previously obtained enrollment PUF strings of multiple external PUF devices (Paral: par. 0046, "Patterns for each round are stored in the Helper data Store during provisioning"; par. 0048, The key generator works in rounds. A round is an instance of generating O bits (O=L+W) of continuous, blended PUF data; there are L possible patterns of width W. found in such data; par. [0025], "The generation of keys can be made faster and the security-level raised by increasing the number of PUFs"; par. [0006], "employing one or more challengeable Physical Unclonable Function (PUF) circuit elements") said searching comprising iteratively determining if a Hamming distance between the received PUF string and an enrollment PUF string retrieved from the database is below a threshold (Paral: par. 0048, The key generator works in rounds. A round is an instance of generating O bits (O=L+W) of continuous, blended PUF data; there are L possible patterns of width W.; par. 0004, the silicon device is authenticated if the regenerated response is "close enough" in Hamming distance to the provisioned response; par. [0045], "selected pattern in the Persistent Helper data Store"; par. 0045, The detector fires if the pattern in the Pattern Shift Register is within the threshold T of the selected pattern in the Persistent Helper data Store).
Paral discloses said searching comprising iteratively determining if a Hamming distance between the received PUF string and an enrollment PUF string retrieved from the database is below a threshold but does not explicitly disclose “at least until a matching enrollment PUF string is found.”
However, in an analogous art, Braithwaite discloses application-specific biometric templates, wherein iteratively determining at least until matching template in database is found (Braithwaite: par. [0022], If the system is an identification system the recognition template is compared with a template in the enrollment (template) database. At step 182, if the enrollment template and the recognition template match, authentication is successful. If the templates do not match, at step 186, the system checks to see if there are more templates in the database. If there are more templates in the database, processing returns to step 178 and the next template in the database is retrieved, and the process is repeated. If all the templates have been compared to the recognition template and no match has been found, authentication fails (step 190)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Braithwaite with the method and system of Paral to include “at least until a matching enrollment PUF string is found.” One would have been motivated to do so because Braithwaite teaches that in identification systems, "secondary identifying information is not required to retrieve a specific enrollment template from a database" and "the recognition template is compared against all templates in an enrollment database" (Braithwaite: par. [0004]). This enables authentication when the device does not first identify itself, and allows establishment of "a centralized authentication server, for use by a number of applications" (Braithwaite: par. [0005]).
Paral teaches using a threshold T for Hamming distance comparison but does not explicitly wherein the threshold depends on the specific retrieved enrollment PUF string and/or the received PUF string.
However, in an analogous art, Peirce teaches a threshold-based authentication system wherein the threshold depends on specific enrollment data. Specifically, Peirce teaches that enrollment attributes (device, quality algorithm, template generation algorithm) are associated with the enrollment template and are used to select the most appropriate ROC table to compute the confidence levels achieved from the matching scores (Peirce: Col. 8, lines 9-15, The enrollment attributes (device, quality algorithm, template generation algorithm) are associated with the enrollment template. These along with the verification template attributes, and the environment/population factors are used to select the most appropriate ROC table to compute the confidence levels achieved from the matching scores). Peirce further teaches "a different ROC threshold table may be used for each possible factor change" depending on "the enrollment/verification device combination" (Peirce: Col. 6, lines 27-31, Within the system, a different ROC threshold table may be used for each possible factor change. The system supports multiple ROCs depending on the enrollment/verification device combination, the population and the environment etc.) and "user-specific thresholds" might be recommended (Peirce: Col. 10, lines 47-48, user-specific thresholds might be recommended for some of these categories of users).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Peirce with the method and system of Paral and Braithwaite to include (b3) wherein the threshold depends on the specific retrieved enrollment PUF string and/or the received PUF string. One would have been motivated to do so because Peirce teaches that using a single static threshold for all enrollments is problematic since "no account is taken of the specifics of the enrolment process, the specifics of the authentication process or other parameters" (Peirce: Col. 3, lines 25-27). Peirce explicitly teaches that using enrollment-specific thresholds "will yield superior and more accurate results, and avoid the problems associated with static thresholds" (Peirce: Col. 8, lines 15-17). Peirce further explains that using static thresholds causes "a higher false non-match rate (FNMR) and degrade overall biometric performance" for some enrollments, while causing "more false accepts which is very undesirable" for others (Peirce: Col. 6, lines 40-50). Therefore, a person of ordinary skill in the art would have recognized that applying Peirce’s enrollment specific threshold selection to PUF authentication would predictably yield the same "superior and more accurate results" by reducing false acceptance rate and false rejection rate.
Regarding claim 3, the combination of Paral, Braithwaite, and Peirce teaches the method of claim 1. The combination of Paral, Braithwaite, and Peirce further teaches comprising
repeatedly retrieving an enrollment PUF string from the database (Paral: pars. 0048, 0004, and 0045), and
determining the threshold from the retrieved enrollment PUF string, or
retrieving the threshold from the database, the threshold being stored in the database associated with the enrollment PUF string (Peirce: Col. 8, lines 9-15, The enrollment attributes (device, quality algorithm, template generation algorithm) are associated with the enrollment template. These along with the verification template attributes, and the environment/population factors are used to select the most appropriate ROC table to compute the confidence levels achieved from the matching scores).
Regarding claim 5, the combination of Paral, Braithwaite, and Peirce teaches the method of claim 1. The combination of Paral, Braithwaite, and Peirce further teaches enrollment-dependent threshold selection in biometric authentication, where different enrollment/device combinations receive different thresholds based on their characteristics (Peirce: Col. 8, lines 9-15, Col. 6, lines 27-31; Col. 10, lines 47-48, "user-specific thresholds"; Paral: pars. 0048, 0004, 0045). One of ordinary skill in the art use enrollment-dependent thresholds for PUF strings. Peirce's ROC-based threshold selection results in multiple possible threshold values for different enrollments. The combination Paral, Braithwaite, and Peirce does not explicitly teach limiting the thresholds to only two values (an upper threshold and a lower threshold) with enrollments grouped accordingly.
However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination to use only two threshold values (an upper threshold and a lower threshold), wherein a part of the enrollment PUF strings in the database are associated with the upper threshold and a part of the enrollment PUF strings are associated with the lower threshold. Peirce teaches that different enrollments may require different thresholds based on quality characteristics. One of ordinary skill in the art would recognize that simplifying multiple threshold values to only two categories (upper and lower) is an obvious design choice that reduces system complexity while preserving the benefit of enrollment-dependent threshold selection. This represents choosing from a finite number of identified, predictable solutions (i.e., using 2, 3, 4, or more threshold levels) with a reasonable expectation of success. One of ordinary skill in the art would be motivated to use only two thresholds to simplify implementation and analysis while still accommodating enrollments of varying quality - grouping higher quality enrollments with the lower threshold (allowing fewer bit differences) and lower quality enrollments with the upper threshold (allowing more bit differences).
Regarding claim 12, the combination of Paral, Braithwaite, and Peirce teaches the method of claim 1. The combination of Paral, Braithwaite, and Peirce further teaches comprising determining a fixed identifier for the external PUF device (Braiwaite: par. 0020, "An appropriate database key value, such as an index number or identification number, is concatenated to the biometric template and is stored in a separate template database... When a matching template is found its concatenated identification number or database key is then used to retrieve the corresponding information from the secondary information database.").
Regarding claim 13, the combination of Paral, Braithwaite, and Peirce teaches the method of claim 12. The combination of Paral, Braithwaite, and Peirce further teaches wherein determining the fixed identifier comprises at least one of: if a matching enrollment PUF string is found, applying a function to at least part of the matching enrollment PUF string, the fixed identifier comprising at least part of the function output, if a matching enrollment PUF string is found, retrieving a fixed identifier associated with the matching enrollment PUF string from the database (Braiwaite: par. 0020, "An appropriate database key value, such as an index number or identification number, is concatenated to the biometric template and is stored in a separate template database... When a matching template is found its concatenated identification number or database key is then used to retrieve the corresponding information from the secondary information database." ), and/or if a matching enrollment PUF string is not found, adding the received PUF string to the database, and if a matching enrollment PUF string is not found, generating a fixed identifier and storing the fixed identifier in association with the received PUF string in the database.
Regarding claim 16, Paral discloses a device comprising:
a communication interface configured for receiving a PUF string, the PUF string having been obtained by measuring an external PUF device (Paral: par. 0003: "Given a challenge as input, a PUF outputs a response that is unique to the manufacturing instance of the PUF circuit"”; par. 0004: "the silicon device is authenticated if the regenerated response is close enough in Hamming distance to the provisioned response"; par. 0045: "Tolerant Pattern Match Detector 240: The detector fires if the pattern in the Pattern Shift Register is within the threshold T of the selected pattern in the Persistent Helper data Store"; par. 0006:"employing one or more challengeable Physical Unclonable Function (PUF) circuit elements"),
a database interface configured for accessing a database, the database comprising multiple previously obtained enrollment PUF strings of multiple external PUF devices (Paral: par. 0045 "the selected pattern in the Persistent Helper data Store"; par. 0046: "Patterns for each round are stored in the Helper data Store during provisioning" par. 0048: "there are L possible patterns of width W" ; par. 0025, "The generation of keys can be made faster and the security-level raised by increasing the number of PUFs", par. 0006: "employing one or more challengeable Physical Unclonable Function (PUF) circuit elements") and
a processor system configured for searching for a matching enrollment PUF string in the database (Paral: pars. 0003-0004, “PUF outputs a response”; par. 0045:"Tolerant Pattern Match Detector 240: The detector fires if the pattern in the Pattern Shift Register is within the threshold T of the selected pattern in the Persistent Helper data Store"; par. 0046, Persistent Helper data Store 250 and Pattern Selector: Patterns for each round are stored in the Helper data Store during provisioning.), said searching comprising iteratively determining if a Hamming distance between the received PUF string and an enrollment PUF string retrieved from the database is below a threshold (Paral: par. 0048, The key generator works in rounds. A round is an instance of generating O bits (O=L+W) of continuous, blended PUF data; there are L possible patterns of width W; par. 0004, the silicon device is authenticated if the regenerated response is "close enough" in Hamming distance to the provisioned response; par. [0045], "selected pattern in the Persistent Helper data Store"; par. 0045, The detector fires if the pattern in the Pattern Shift Register is within the threshold T of the selected pattern in the Persistent Helper data Store).
Paral discloses said searching comprising iteratively determining if a Hamming distance between the received PUF string and an enrollment PUF string retrieved from the database is below a threshold but does not explicitly disclose “at least until a matching enrollment PUF string is found.”
However, in an analogous art, Braithwaite discloses application-specific biometric templates, wherein iteratively determining at least until matching template in database is found (Braithwaite: par. [0022], If the system is an identification system the recognition template is compared with a template in the enrollment (template) database. At step 182, if the enrollment template and the recognition template match, authentication is successful. If the templates do not match, at step 186, the system checks to see if there are more templates in the database. If there are more templates in the database, processing returns to step 178 and the next template in the database is retrieved, and the process is repeated. If all the templates have been compared to the recognition template and no match has been found, authentication fails (step 190)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Braithwaite with the method and system of Paral to include “at least until a matching enrollment PUF string is found.” One would have been motivated to do so because Braithwaite teaches that in identification systems, "secondary identifying information is not required to retrieve a specific enrollment template from a database" and "the recognition template is compared against all templates in an enrollment database" (Braithwaite: par. [0004]). This enables authentication when the device does not first identify itself, and allows establishment of "a centralized authentication server, for use by a number of applications" (Braithwaite: par. [0005]).
Paral teaches using a threshold T for Hamming distance comparison but does not explicitly wherein the threshold depends on the specific retrieved enrollment PUF string and/or the received PUF string.
However, in an analogous art, Peirce teaches a threshold-based authentication system wherein the threshold depends on specific enrollment data. Specifically, Peirce teaches that enrollment attributes (device, quality algorithm, template generation algorithm) are associated with the enrollment template and are used to select the most appropriate ROC table to compute the confidence levels achieved from the matching scores (Peirce: Col. 8, lines 9-15, The enrollment attributes (device, quality algorithm, template generation algorithm) are associated with the enrollment template. These along with the verification template attributes, and the environment/population factors are used to select the most appropriate ROC table to compute the confidence levels achieved from the matching scores). Peirce further teaches "a different ROC threshold table may be used for each possible factor change" depending on "the enrollment/verification device combination" (Peirce: Col. 6, lines 27-31, Within the system, a different ROC threshold table may be used for each possible factor change. The system supports multiple ROCs depending on the enrollment/verification device combination, the population and the environment etc.) and "user-specific thresholds" might be recommended (Peirce: Col. 10, lines 47-48, user-specific thresholds might be recommended for some of these categories of users).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Peirce with the method and system of Paral and Braithwaite to include wherein the threshold depends on the specific retrieved enrollment PUF string and/or the received PUF string. One would have been motivated to do so because Peirce teaches that using a single static threshold for all enrollments is problematic since "no account is taken of the specifics of the enrolment process, the specifics of the authentication process or other parameters" (Peirce: Col. 3, lines 25-27). Peirce explicitly teaches that using enrollment-specific thresholds "will yield superior and more accurate results, and avoid the problems associated with static thresholds" (Peirce: Col. 8, lines 15-17). Peirce further explains that using static thresholds causes "a higher false non-match rate (FNMR) and degrade overall biometric performance" for some enrollments, while causing "more false accepts which is very undesirable" for others (Peirce: Col. 6, lines 40-50). Therefore, a person of ordinary skill in the art would have recognized that applying Peirce’s enrollment specific threshold selection to PUF authentication would predictably yield the same "superior and more accurate results" by reducing false acceptance rate and false rejection rate.
Regarding claim 17, claim 17 is directed to a non-transitory computer readable medium comprising stored instructions, which when executed by a processor system associated with the method claimed in claim 1; claim 17 is similar in scope to claim 1, and is therefore rejected under similar rationale.
Regarding claim 19, claim 19 is similar in scope to claim 3, and is therefore rejected under similar rationale.
Claims 2 and 18 rejected under 35 U.S.C. 103 as being unpatentable over Paral et al. (“Paral,” US 2012/0183135) in view of Braithwaite et al. (“Braithwaite,” US 2004/0193893), and Peirce et al. (“Peirce,” US 7,545,962), and further in view of Lo (“Lo,” US 7,257,241).
Regarding claim 2, the combination of Paral, Braithwaite, and Peirce teaches the method of claim 1. The combination of Paral, Braithwaite, and Peirce teaches wherein the threshold and the retrieved enrollment PUF string but does not explicitly disclose “the threshold is determined by applying a function to the retrieved enrollment PUF string and/or the received PUF string.”
However, in an analogous art, Lo teaches a biometric authentication methos wherein “the threshold is determined by applying a function to the retrieved enrollment data (Lo: Col. 7, lines 48-49, "the number of minutiae in the set of minutiae for the enrolled print (e.g., t_nmin)". This teaches analyzing the content of enrollment data (counting features therein), which corresponds to analyzing the retrieved enrollment PUF string under BRI because both represent extracting characteristic information from stored enrollment data; Col. 7, lines 49-51, "BiasS is determined as a function of the difference between the number of minutiae for the two prints"- "function" to describe the mathematical operation applied to enrollment data characteristics; Col. 8, lines 4-5, "The verification threshold T may be set to f(Tf)+biasS, where f( ) is a function" - the final threshold value is determined by applying a function that incorporates enrollment data characteristics (via biasS derived from t_nmin)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lo with the method and system of Paral, Braithwaite, and Peirce to include “the threshold is determined by applying a function to the retrieved enrollment PUF string and/or the received PUF string.” One would have been motivated to do so because Lo teaches that a single fixed threshold …may not be optimal since matching scores vary widely due to differences in the matching features …extracted from the two prints (Lo: Col. 2, lines 37-47).
Regarding claim 18, claim 18 is similar in scope to claim 2, and is therefore rejected under similar rationale.
Claims 4 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Paral et al. (“Paral,” US 2012/0183135) in view of Braithwaite et al. (“Braithwaite,” US 2004/0193893), and Peirce et al. (“Peirce,” US 7,545,962), and in view of Zhdanov et al. (“Zhdanov,” US 10,025,831).
Regarding claim 4, the combination of Paral, Braithwaite, and Peirce teaches the method of claim 1. The combination of Paral, Braithwaite, and Peirce further teaches dependent thresholds used in searching (Peirce: Col. 8, lines 9-15; teaching enrollment attributes associated with the enrollment template and used to select threshold from stored ROC tables; Peirce: Col. 10, lines 47-48, user-specific thresholds might be recommended for some of these categories of users.) but the combination of Paral, Braithwaite, and Peirce does not explicitly teaches
an upper threshold is defined at least as large as “the dependent thresholds”
determining if the Hamming distance between the received PUF string and an enrollment PUF string retrieved from the database is below the upper threshold
if so, determining the specific threshold depending on the specific retrieved enrollment PUF string, he enrollment PUF string matching if the Hamming distance between the received PUF string and retrieved enrollment PUF string is below the specific threshold.
However, in an analogous art, Zhdanov discloses teaches a two-stage threshold filtering process for biometric database searching:
determining if comparison metric between the received biometric sample and an biometric string retrieved from the database is below the upper threshold (Zhdanov: Col, 8, lines 45-50, teaching "each score in the set of biometric samples is compared against the threshold value. If the score is less than the threshold value, the sample moves to block 314 where it is discarded. If the score for a specific sample is greater than or equal to the threshold value it is added to the shortlist"), and
if so, determining the specific threshold depending on the specific retrieved enrollment PUF string (Zhdanov: Col. 8, lines 51-55, teaching "In the second phase of the exemplary, model two dataset 64 is used against the first shortlist to generate a second shortlist, where model two dataset comprises higher resolution data than model one dataset"; Col. 5, lines 38-41, teaching "The next smallest size model is used for the second scoring round... resulting in an increased accuracy with the increase in resolution"; Peirce: Col. 8, lines 9-15; teaching enrollment attributes associated with the enrollment template and used to select threshold from stored ROC tables; Peirce: Col. 10, lines 47-48, user-specific thresholds might be recommended for some of these categories of users).
the enrollment PUF string matching if the Hamming distance between the received PUF string and retrieved enrollment PUF string is below the specific threshold (Zhdanov: Col. 4, lines 55-59, teaching "Increasingly detailed datasets are used to generate increasingly reduced shortlists until the nth phase, where the nth model dataset 66 is compared against the (n−1) shortlist, to generate the nth shortlist which, in one embodiment, is the final result set"; Col. 5, lines 57-58, teaching "the desired number of samples is a single match").
Regarding to limitation “an upper threshold is defined at least as large as the dependent thresholds”. Zhdanov teaches threshold value T is computed adaptively based on the desired shortlist size par. 0048), and "samples are scored and kept only if their comparison score is above a threshold elimination score"; the first-stage threshold must be set to retain all potential true matches for subsequent stages (par. 0029).
While Zhdanov does not explicitly recite that the upper threshold is "at least as large as" the enrollment-specific thresholds, one of ordinary skill in the art would recognize this as a necessary design constraint for the two-stage filtering system to function correctly. Specifically:
- If Upper_Threshold were set SMALLER than any enrollment-specific threshold (Specific_Threshold), then Stage 1 would incorrectly eliminate true matches that would have passed Stage 2. This would cause false rejections, defeating the purpose of the two-stage system. Therefore, Upper Threshold ≥ max (all Specific_Threshold) is a REQUIRED relationship, not merely an optimization choice. One of ordinary skill in the art implementing Zhdanov' two-stage threshold system with Peirce's enrollment-specific thresholds would inherently set the upper threshold at least as large as all enrollment-specific thresholds to ensure no true matches are incorrectly rejected in Stage 1. This represents applying known design constraints to achieve predictable results.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Zhdanov with the method and system of Paral, Braithwaite, and Peirce to include “an upper threshold is defined at least as large as “the dependent thresholds” determining if the Hamming distance between the received PUF string and an enrollment PUF string retrieved from the database is below the upper threshold, (b) if so, determining the specific threshold depending on the specific retrieved enrollment PUF string, the enrollment PUF string matching if the Hamming distance between the received PUF string and retrieved enrollment PUF string is below the specific threshold. One would have been motivated to do so because Zhdanov teaches that large biometric databases "can hold up to hundreds of millions of records" and that searching such databases "can often result in the process of determining a match requiring a lengthy period of time to complete" (Zhdanov: Col. 1, lines 43-50). Zhdanov 's two-stage approach addresses this problem by using "a staged scoring process... [which] increases the efficiency of the comparison module as increased analysis time is only required on smaller and smaller sets of biometric records" (Zhdanov: Col. 5, limes 53-56). One would have been motivated to seek to optimize PUF authentication in large databases (as taught by Paral) would look to Zhdanov's efficient two-stage filtering approach to reduce computational burden while maintaining accuracy. This represents applying a known technique (two-stage threshold filtering) to a known system (PUF authentication with enrollment-specific thresholds) to yield predictable results (efficient database searching with maintained accuracy).
Regarding claim 20, claim 20 is similar in scope to claim 4, and is therefore rejected under similar rationale.
Claims 6-8 and 10 is rejected under 35 U.S.C. 103 as being unpatentable over Paral et al. (“Paral,” US 2012/0183135) in view of Braithwaite et al. (“Braithwaite,” US 2004/0193893), and Peirce et al. (“Peirce,” US 7,545,962), and Van Der Leest et al. (“Van,” US 10,554,398), and further in view of A. Sadr et al. (“Sadr,” Weighted Hamming distance for PUF performance evaluation, Electronics Letters, 24th October 2013, pages 1-2).
Regarding claim 6, the combination of Paral, Braithwaite, and Peirce teaches the method of claim 1. Paral, Braithwaite, and Peirce do not explicitly disclose wherein a PUF string is a PUF bit string, a particular PUF device being associated with a deviance, being the average absolute difference between the 1-probability of bit and ½, the dependent threshold determined for a PUF string of the PUF device depending on a measure for the average deviance determined from the PUF string.
However, in an analogous art, Van discloses wherein a PUF string is a PUF bit string (Van: abstract, "The PUF is configured to produce a first noisy bit string during an enrollment phase and a second noisy bit string during a reconstruction phase."; Col. 3, lines 54-55, A PUF which has a low bias will, at least on average produce a bit string .. ), a particular PUF device being associated with a deviance (Van: Col. 3, lines 54-55, A PUF which has a low bias will, at least on average produce a bit string …), being the average absolute difference between the 1-probability of bit and ½ (Van: Col. 3, lines 54-55, "A PUF which has a low bias will, at least on average produce a bit string, in which the absolute difference between the fraction of 1 bits and the fraction of 0 bits is small. The fraction may be taken as the number of 1 or 0 bits respectively, divided by the length of the string.")
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Van with the method and system of Paral, Braithwaite, and Peirce to include wherein a PUF string is a PUF bit string, a particular PUF device being associated with a deviance, being the average absolute difference between the 1-probability of bit and ½. One would have been motivated to make this combination because Van teaches that bias in PUF responses causes security vulnerabilities—specifically, that high bias can cause "key leakage" where "the noise-reduction data may even reveal sufficient information about the key to allow complete reconstruction of the key by an attacker" (Van, Col. 2:54-57). Therefore, measuring and accounting for bias/deviance in a PUF device improves the security and reliability of PUF-based authentication systems. Furthermore, the combination would have had a reasonable expectation of success because both Paral and Van are directed to PUF-based cryptographic systems that analyze PUF bit strings for authentication and key generation purposes.
Peirce teaches the dependent threshold determined for a PUF string of the PUF device (Peirce: Co.8, lines 9-15) but does not explicitly disclose “the dependent threshold determined for a PUF string of the PUF device depending on a measure for the average deviance determined from the PUF string.”
However, in an analogous art, Sadr discloses that proper threshold selection depends on bit stability characteristics (Sadr, page 2: "if we choose a proper threshold, the identification problem will have less FAR and FRR errors" "the value of each bit in the Hamming distance is related to the stability of that bit against noise"; Peirce: Col. 8, lines 9-15; Van: Col. 3, lines 54-55).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sard with the method and system Paral, Braithwaite, Peirce, and Van to include the dependent threshold determined for a PUF string of the PUF device depending on a measure for the average deviance determined from the PUF string. One would have been motivated to improve PUF authentication security (by accounting for bias/deviance as taught by Van) and to optimize authentication performance by reducing FAR and FRR errors (by adapting threshold based on deviance-related stability as taught by Sadr).
Regarding claim 7, the combination of Paral, Braithwaite, Peirce, Van, and Sadr teaches the method of claim 6. The combination of Paral, Braithwaite, Peirce, Van, and Sad, further teaches comprising
computing the measure for the deviance from the enrollment PUF string and/or the received PUF string (Van: abstract, Col. 3, lines 54-58), and
computing the threshold from the measure (Peirce: Col. 8, lines 9-15; Sadr, page 2: "if we choose a proper threshold, the identification problem will have less FAR and FRR errors" "the value of each bit in the Hamming distance is related to the stability of that bit against noise").
Regarding claim 8, the combination of Paral, Braithwaite, Peirce, Van, and Sadr teaches the method of claim 6. While none of the references explicitly state "threshold is non-increasing for increasing deviance," this relationship is inherent in the combined teachings of Van and Sadr, and a person of ordinary skill in the art would recognize this relationship as the only logical engineering choice.
Van teaches that deviance determines stability (Van: Col. 3, lines 54-55, "A PUF which has a low bias will, at least on average produce a bit string, in which the absolute difference between the fraction of 1 bits and the fraction of 0 bits is small. The fraction may be taken as the number of 1 or 0 bits respectively, divided by the length of the string."). Van teaches measuring deviance/bias from the PUF string. The relationship between deviance and stability is inherent in the definition of deviance: a bit with high deviance has a strong preference for one output value and will therefore consistently produce that value (stable) (i.e. few errors between enrollment and authentication), while a bit with low deviance has no strong preference and will vary between outputs (unstable) (i.e. many errors between enrollment and authentication).
Sadr explicitly teaches that bit stability affects the Hamming distance errors observed during authentication (Sadr, page 2: "if we choose a proper threshold, the identification problem will have less FAR and FRR errors" "the value of each bit in the Hamming distance is related to the stability of that bit against noise"). Sadr teaches that stability affects error rate and proper threshold depends on expected errors. In other word," if we choose a proper threshold, the identification problem will have less FAR and FRR errors", "the value of each bit in the Hamming distance is related to the stability of that bit against noise"
As deviance increases, stability increases, expected errors decrease, and therefore threshold decreases (or stays the same). The threshold never increases when deviance increases.
Therefore, a person of ordinary skill in the art, applying the teachings of Van (deviance determines stability) and Sadr (stability affects errors; proper threshold minimizes FAR/FRR), would necessarily arrive at a non-increasing threshold for increasing deviance as the only logical implementation.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Paral et al. (“Paral,” US 2012/0183135) in view of