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 responsive to the Amendment filed 09/26/2024 and Applicant's Remarks filed in reply to the Office Action of January 16, 2026. Claims 1-18 are pending in the application. Claims 1-8 were previously presented; Claims 9-18 are new.
This action is a NON-FINAL action. Prosecution is hereby reopened to apply newly cited prior art, as set forth in detail below.
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d). The certified copy has been received.
Response to Arguments
Applicant's arguments filed in reply to the Office Action of January 16, 2026 have been fully considered. Applicant argues that Sun does not describe or reasonably suggest “identify a type of a distribution of original data,” as recited in claim 1, because Sun at ¶86 and ¶7 discloses modeling user profile data using an assumed or prescribed probability distribution rather than identifying the type of distribution that the original data actually follows. Applicant further argues that Sun's disclosure at ¶¶98-99 of generating replacement centroid feature vectors by sampling from a standard normal distribution is directed to a differential-privacy technique that is purpose distinct from the claimed “pseudo data.”
Applicant's arguments have been found persuasive with respect to the specific combination previously applied. Upon further consideration, and in view of newly discovered prior art, the rejections of record are withdrawn and new grounds of rejection are set forth below.
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 of this title, 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 1-2, 7-8, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US 2023/0078704 A1) in view of Fujimoto (US 2005/0283505 A1).
Regarding Claim 1
Sun discloses:
An information providing apparatus comprising:
processing circuitry configured to:
identify a type of a distribution of original data that have been stored in plural servers in a state of being divided and shared by secret sharing (Sun ¶¶62-67: teaches that original data, e.g., user profiles, are divided into secret shares and stored across plural computing systems, e.g., MPC1 and MPC2, of an MPC system; Sun ¶¶86-91: further teaches modeling the secret shared user profile data using an n-dimensional normal distribution parameterized as x~Nn(μ,Σ) and determining the characteristic parameters, i.e., a mean value μ and a covariance matrix Σ, of that distribution from the secret shares. Sun, however, does not explicitly disclose identifying, i.e., testing or determining, the type of the distribution, but rather models the data using an assumed normal distribution.);
generate pseudo data according to the type of the distribution identified (Sun ¶0088-0089: after modeling the users of the first cluster using the n-dimensional normal distribution x~Nn(μ,Σ), each of MPC1 and MPC2 generates a random vector z drawn from a standard normal distribution using a Box-Muller transform, and computes a label result1 = [μ2] + (1/2^(1/2))·k·A·([z1]+[z1′]), where A is a Cholesky factor of Σ such that A·AT=Σ. This transforms the standard normal distributed random vector z by the covariance matrix A and shifts it by the mean μ, thereby producing pseudo data.); and
provide the pseudo data generated (Sun ¶0090: the computing system MPC1 provides, to the application, the first label of the resultant cluster, i.e., the generated pseudo-data vector result1, and an encrypted version of the second label result2 determined by MPC2, thereby making the generated pseudo data available to the requesting application instead of the original data.).
Sun does not explicitly disclose identifying a type of a distribution by performing a test on the original data. Fujimoto, however, teaches a distribution test device for testing whether measured data matches an estimated probability distribution (Fujimoto Abstract; ¶0063: a distribution estimation part reads observed data and estimates a distribution parameter), comprising a counting section determination unit, a counting unit, and a goodness-of-fit test unit that performs a goodness-of-fit test, e.g., a chi-square goodness-of-fit test, based on the counted numbers of data in each of a plurality of counting sections, and judges whether the measured data does or does not follow the estimated distribution based on a comparison of a test statistic to a chi-square critical value (Fujimoto ¶0064-0069; 0161-0169); and further teaches that it is assumed that the measured data follows a plurality of probability distributions, and that the widths of the counting sections, and thus the goodness-of-fit test, are determined and performed for each of the assumed probability distributions, thereby identifying which of the plurality of candidate distribution types the data follows (Fujimoto ¶0163-0164).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Sun's secret sharing and distribution modeling framework with Fujimoto's goodness-of-fit testing technique, such that the type of distribution used by Sun to model the secret shared original data is identified and determined using Fujimoto's goodness-of-fit test before generating pseudo data according to the identified distribution. One of ordinary skill in the art would have been motivated to make this combination because a system such as Sun’s would benefit from confirming that the assumed distribution accurately reflects the statistical properties of the secret shared data and applying Fujimoto's statistical testing technique to a secret shared data would have yielded the predictable result of accurately identifying the distribution type before generating pseudo data, thereby improving the fidelity of the generated pseudo data to the original data.
Regarding Claim 2
Sun discloses:
The information providing apparatus according to claim 1, wherein in a case where the type of the distribution has been identified as a normal distribution, the processing circuitry is further configured to generate, as the pseudo data, random data conforming to a normal distribution having a mean value and a variance value that have been specified (Sun ¶86 and 97: teaches identifying the type of distribution as a normal distribution by modeling secret-shared user data as an n-dimensional normal distribution. Sun ¶86–¶88: further teaches determining the parameters of the normal distribution, including a mean value (centroid μ) and a variance value (covariance matrix Σ) for the modeled data. Sun ¶89: further teaches generating random data conforming to the identified normal distribution by generating random vectors drawn from a standard normal distribution using a Box-Muller transform and transforming the random vectors using the determined mean and covariance parameters. The resulting data conforms to a normal distribution defined by the specified mean and variance and is used in place of the original data, thereby constituting pseudo data. Fujimoto ¶0163: teaches assuming and testing a plurality of candidate distributions, including a normal distribution, to identify which distribution type the measured data follows).
Regarding Claim 7
Claim 7 is directed to a method corresponding to the apparatus in claim 1. Claim 7 is similar in scope to claim 1 and is therefore rejected under similar rationale.
Regarding Claim 8
Claim 8 is directed to a computer-executable instruction corresponding to the method in claim 1. Claim 8 is similar in scope to claim 1 and is therefore rejected under similar rationale.
Regarding Claim 12
Sun in view of Fujimoto teaches identifying a type of a distribution of the original data, as set forth above with respect to claim 1. Fujimoto further teaches that the goodness-of-fit test unit automatically judges, based on a comparison of a computed test statistic to a chi-square critical value, whether the measured data does or does not follow the estimated distribution (Fujimoto ¶0069: “When the test statistic is larger than the chi-square value, it is judged that the data does not follow the estimated discrete distribution”), and further teaches that it is assumed that the measured data follows a plurality of probability distributions, and that this test-based determination is performed for each of the plurality of assumed distributions, thereby automatically determining, based on the result of the test, which of the candidate distribution types the data follows (Fujimoto ¶0163: “it is assumed that the measured data follows a plurality of probability distributions, and the counting section determination unit changes the widths of the counting sections for each of the assumed probability distributions”). This directly teaches automatically determining the type of the distribution on a basis of a result of a test performed on the original data.
It would have been obvious to one a POSITA before the effective filing date of the claimed invention to combine Sun's secret sharing and distribution modeling framework with Fujimoto's goodness-of-fit testing technique, such that the type of distribution used by Sun to model the secret shared original data is identified and determined using Fujimoto's goodness-of-fit test before generating pseudo data according to the identified distribution. One of ordinary skill in the art would have been motivated to make this combination because a system such as Sun’s would benefit from confirming that the assumed distribution accurately reflects the statistical properties of the secret shared data and applying Fujimoto's statistical testing technique to a secret shared data would have yielded the predictable result of accurately identifying the distribution type before generating pseudo data, thereby improving the fidelity of the generated pseudo data to the original data.
Regarding Claim 13
Sun discloses:
The information providing apparatus according to claim 1, wherein the processing circuitry is further configured to receive specification of characteristic parameters corresponding to the type of the distribution identified, and to generate the pseudo data on a basis of the characteristic parameters (Sun ¶¶0086-0090: MPC1 and MPC2 determine the characteristic parameters of the identified normal distribution, namely the mean/centroid μ (calculated as μ1=Σi∈ID[Pi,1]) and the covariance matrix Σ (calculated via [Σ1]=Σi∈ID(k*[Pi,1]−μ1)T*(k*[Pi,1]−μ1)); Sun further teaches generating the pseudo data, i.e., the random vector result1, on the basis of these characteristic parameters, specifically by transforming a standard normal random vector z using the Cholesky factor A of the covariance matrix Σ and the mean μ.).
Claims 3, 4 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US 2023/0078704 A1), in view of Fujimoto (US 2005/0283505 A1) as applied to claim 1 above, and further in view of Rane (US 2011/0040820 A1).
Regarding Claim 3
Sun in view of Fujimoto teaches identifying a type of a distribution of the original data by performing a goodness-of-fit test on the secret shared data. However, Sun and Fujimoto do not disclose that this statistical test is performed through secure computation. On the other hand, Rane teaches performing statistical determinations of a data distribution through secure multi-party computation (SMPC), specifically by determining a joint empirical probability distribution (JEPD) of vectors of data using SMPC such that a first processor does not discover a second vector and a second processor does not discover a first vector during the computation (Rane ¶0008, ¶¶0031-0040); and further teaches that statistical functions can be expressed and evaluated directly in terms of the securely-computed distribution, e.g., as a normalized summation of products of values of the JEPD with corresponding results of the function (Rane ¶¶0031-0039), thereby enabling statistical testing and distribution level analysis to be performed under secure computation without revealing the underlying data.
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 teachings of Sun-Fujimoto with Rane's secure multi-party computation techniques for statistical analysis of a distribution, such that the counting, tallying, and comparison operations of Fujimoto's goodness-of-fit test are performed on secret shares of the original data using a known secure computation method. One of ordinary skill in the art would have been motivated to make this combination because Sun's system is directed to preserving the privacy of the secret-shared original data, and a POSITA seeking to identify the distribution type of that data without compromising its privacy would have looked to Rane's known technique for performing statistical distribution analysis under secure computation, yielding the predictable result of a privacy preserving statistical test for identifying the distribution type.
Regarding Claim 4
Claim 4 is directed to an apparatus corresponding to the apparatus in claim 3. Claim 4 is similar in scope to claim 3 and is therefore rejected under similar rationale.
Regarding Claim 9
Sun in view of Fujimoto and Rane teaches performing a statistical test through secure computation to identify the type of the distribution, as set forth above with respect to claim 3. Fujimoto explicitly teaches that the goodness-of-fit test unit tests the counted data by a chi-square goodness-of-fit test (Fujimoto ¶0065, 0069, 0169; claim 10: “the goodness-of-fit test unit tests the counted data by a chi-square goodness-of-fit test”), i.e., a chi-square test as recited. Rane further teaches performing the counting, tallying, and statistical evaluation operations underlying such a test using secret shares of the original data, rather than the data in plaintext, via its secure multi-party computation techniques (Rane ¶¶0049-0052, ¶¶0057-0066), thereby teaching using shares of the original data to perform the test.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fujimoto's specific chi-square goodness-of-fit test with Rane's technique of performing statistical computations using secret shares of the data, for the same reasons and with the same predictable result as set forth above with respect to claim 3. A POSITA would have recognized the chi-square test and its combination with Rane's secure computation on shares technique amounts to no more than the predictable application of a known statistical test in a known secure computation environment.
Claims 5-6, 10-11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US 2023/0078704 A1), in view of Fujimoto (US 2005/0283505 A1), in view of Rane (US 2011/0040820 A1) as applied to claims 3-4 above, and in further view of Lindell (US 2018/0357427 A1).
Regarding Claim 5
Sun in view of Fujimoto and Rane teaches performing the statistical test to identify the type of the distribution through secure computation, as set forth above with respect to claim 3. Sun in view of Fujimoto and Rane does not explicitly disclose performing the statistical test by using a measure of central tendency that is a predetermined quantile resulting from sorting of records of the original data. Lindell however teaches performing secure computation on encrypted or secret-shared datasets including computing rank functions as part of an SQL query flow by having the servers holding the shares, e.g., S1 and S2, perform a secure sort in which the LESS-THAN comparison operator is replaced with a secure computation. Using garbled circuits or another known secure MPC protocol, such that the plaintext ordering of the records is not revealed, and once given the securely sorted values each server locally computes the rank/percentile function (Lindell ¶0074). Lindell further teaches that these percentile values, once computed after the secure sort, constitute quantile statistical measures computed while preserving data privacy.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Lindell's known secure sort quantile/percentile computation technique into the statistical teachings of Sun-Fujimoto-Rane such that the statistical test uses a predetermined quantile resulting from securely sorting records of the secret shared original data. A POSITA would have been motivated to make this combination because Lindell's secure sort and rank computation is a known technique for computing quantile statistics on shared or encrypted data without reconstructing the data in plaintext, directly compatible with the secure computation statistical testing framework of Sun-Fujimoto-Rane, yielding the predictable result of a quantile measure of central tendency computed securely.
Regarding Claim 6
Claim 6 is directed to an apparatus corresponding to the apparatus in claim 5. Claim 6 is similar in scope to claim 5 and is therefore rejected under similar rationale.
Regarding Claim 10
Sun in view of Fujimoto, Rane, and Lindell teaches performing the statistical test using a predetermined quantile resulting from secure sorting of records of the original data, as set forth above with respect to claim 5. Fujimoto further teaches determining, according to the estimated probability distribution, widths of a plurality of counting sections such that the respective counting sections have equal probabilities on the estimated distribution, i.e., a plurality of quantile-based sections (Fujimoto ¶0063-0068, ¶0163: counting sections are determined for each of a plurality of assumed probability distributions), classifying the measured data into the plurality of counting sections (Fujimoto ¶0065), and comparing the counted, i.e., observed, numbers of data in each of the plurality of counting sections against the expected numbers of data for that section under the candidate distribution, via a chi-square test statistic, to check whether the measured data conforms to the estimated distribution (Fujimoto ¶0065-0069, Tables 2-4). This use of a plurality of equal probability, i.e., quantile based, counting sections to compare observed against expected data constitutes checking conformance of the original data to the distribution by using a plurality of quantiles.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Fujimoto's own multi-section quantile conformance checking technique into the combination of Sun, Fujimoto, Rane, and Lindell of claim 5, such that the conformance of the secret shared original data to the identified distribution is checked using a plurality of quantiles computed via Lindell's secure sort. A POSITA would have been motivated to make this combination because Fujimoto already teaches that its own goodness-of-fit test relies on a plurality of equal probability sections for improved test accuracy and applying this known technique using Lindell's secure quantile computation would have yielded the predictable result of a more granular conformance check performed under secure computation.
Regarding Claim 11
Sun in view of Fujimoto, Rane, and Lindell teaches performing the statistical test using a predetermined quantile resulting from sorting of records of the original data, as set forth above with respect to claim 5. Rane's secure multi-party computation architecture is specifically designed such that neither the first processor nor the second processor ever reconstructs or discovers the plaintext of the other party's data during the computation of the joint distribution or downstream statistics (Rane ¶0039: “the first processor does not discover the second vector Y and the second processor does not discover the first vector X”), and Lindell's secure sort and rank/percentile computation is similarly designed such that the servers holding the shares never reconstruct the plaintext ordering of the records during the sort or percentile computation, using garbled circuits or another secure MPC protocol in place of plaintext comparisons (Lindell ¶58: “Third type: Other Values... garbled circuits secure computation or any other MPC can be used to compute the result of the condition with S1, S2”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to recognize that the statistical test of claim 5, performed using Rane's and Lindell's no reconstruction secure computation architectures is performed without reconstructing records of the original data and to configure the combined system to perform this test at high speed by using these known efficient secure computation protocols. One of ordinary skill in the art would have been motivated to do so because avoiding reconstruction of the original data records is a core objective of Rane's and Lindell's secure computation techniques, and performing the statistical test using these known techniques yields the predictable result of a faster more secure statistical test that does not reconstruct records of the original data.
Regarding Claim 18
Sun in view of Fujimoto, Rane, and Lindell teaches performing the statistical test using a predetermined quantile resulting from secure sorting of records of the original data, as set forth above with respect to claim 5. Fujimoto further teaches converting the estimated, i.e., candidate, probability distribution into a normalized, equal-probability representation by determining the widths of the counting sections such that the respective counting sections have equal probabilities on the estimated probability distribution, thereby converting the estimated distribution, e.g., an inhomogeneous Poisson distribution that varies temporally, into a uniform distribution (Fujimoto ¶0063-0064, ¶0155-0158:“the counting sections are determined so that the widths of the respective counting sections have equal probabilities on the estimated probability distribution... the distribution is converted into a uniform distribution”); classifying, i.e., normalizing, the measured data into these equal probability, i.e., quantile, sections (Fujimoto ¶0065); and comparing the counted, i.e., quantile, values of the normalized data against the expected quantile values under the candidate probability distribution via a chi-square test statistic to check conformance of the measured data to the probability distribution (Fujimoto ¶0065, ¶0069, Tables 2-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fujimoto's normalization quantile comparison conformance checking technique with the combination of Sun, Rane, and Lindell of claim 5, such that the original data is normalized and a predetermined quantile of the normalized data is compared to a corresponding quantile of the candidate probability distribution, using Lindell's secure sort and quantile computation, to check conformance of the original data to the probability distribution. A POSITA would have been motivated to make this combination for the same reasons set forth above with respect to claims 5 and 10, namely that Fujimoto's own technique for converting an estimated distribution into a normalized equal probability representation and comparing observed to expected quantile values is a known predictable technique for checking distributional conformance, and its combination with Lindell's known secure quantile computation would have yielded the predictable result of a normalized quantile conformance check performed under secure computation.
Claims 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US 2023/0078704 A1) in view of Fujimoto (US 2005/0283505 A1) as applied to claims 1 above, and in further view of Ferdowsi (US 2012/0331108 A1).
Regarding Claim 14
Sun in view of Fujimoto teaches providing the pseudo data generated, as set forth above with respect to claim 1. Sun in view of Fujimoto does not explicitly disclose providing the pseudo data by storing the pseudo data in a server and distributing a URL that enables the pseudo data to be downloaded from the server. Ferdowsi (¶0006-0011; 0033) however teaches a file access server that enables a user to obtain a URL or other resource identifier, i.e., a link, that can be shared with others and which provides access to one or more files or file folders managed by the user and stored on a network storage device operated by the file access service, wherein the file access server generates the link and associates it with the file set specified by the user such that the link once distributed enables the referenced file to be accessed (downloaded) from the server.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Ferdowsi's known URL file storage and access technique with Sun's step of providing the generated pseudo data, such that the pseudo data is stored in a server and a URL enabling the pseudo data to be downloaded from the server is distributed. One of ordinary skill in the art would have been motivated to make this combination because Ferdowsi's technique for making server stored content available for download via a distributed URL is a well-known technique for providing generated content to a requesting party, and applying it to Sun's pseudo data would have yielded the predictable result of enabling the pseudo data to be downloaded from a server via a distributed URL.
Claims 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US 2023/0078704 A1) in view of Fujimoto (US 2005/0283505 A1) as applied to claims 1 above, and in further view of Harvey (US 2014/0115007 A1).
Regarding Claim 15
Sun in view of Fujimoto teaches providing the pseudo data generated, as set forth above with respect to claim 1. Sun in view of Fujimoto does not explicitly disclose providing an analysis environment where the pseudo data are able to be used. Harvey however teaches a data processing system that receives a model of an original, confidential dataset, wherein the model does not contain any of the actual data but supports the creation of synthetic data conforming to the rules, patterns, and/or conditions of the model (Harvey ¶0026), and wherein the data processing system provides the functionality to perform analysis, e.g., via database queries, on the synthetic data as a substitute for directly analyzing the original confidential dataset (Harvey ¶0025, ¶0027: “The data processing system provides the functionality to perform analysis on the model 12 as a substitute for directly analysing the dataset stored by the array 10”), i.e., an analysis environment in which the synthetic, i.e., pseudo, data are used.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harvey's known technique of providing an analysis environment for synthetic data, as a substitute for the confidential original data, with Sun's system for generating pseudo data from secret-shared original data. One of ordinary skill in the art would have been motivated to make this combination because Sun's pseudo data, like Harvey's synthetic data, is generated so that it can be used by an analyst in place of the confidential original data, and providing a corresponding analysis environment in which the generated pseudo data can be used, as taught by Harvey, would have yielded the predictable result of enabling analysis of Sun's pseudo data without exposing the underlying secret shared original data.
Regarding Claim 17
Sun in view of Fujimoto teaches generating and providing pseudo data, as set forth above with respect to claim 1. Sun in view of Fujimoto does not expressly disclose that the processing circuitry is further configured to generate a number of sets of pseudo data, wherein the number is specified by a user. Harvey teaches generating synthetic data and controlling the quantity of synthetic data to be generated. In particular, Harvey teaches determining how many synthetic records are to be generated, including an example in which 1,000 synthetic records are generated (Harvey ¶0044), and further teaches that the amount of synthetic data created is controlled by the query applied to the database view (Harvey ¶0070).
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 pseudo data generation system of Sun in view of Fujimoto to permit a user to specify the number of sets of pseudo data to be generated, as suggested by Harvey. Such a modification would have allowed the user to obtain a quantity of pseudo data suited to the intended analysis or downstream use and would have yielded the predictable result of generating the user-specified number of sets of pseudo data.
Claims 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US 2023/0078704 A1) in view of Fujimoto (US 2005/0283505 A1) as applied to claims 1 above, and in further view of Wang (US 2024/0163341 A1).
Regarding Claim 16
Sun in view of Fujimoto teaches that the original data are stored in the plural servers by secret sharing, as set forth above with respect to claim 1. Sun and Fujimoto do not disclose that the secret sharing is performed using Shamir's Threshold Secret Sharing. Wang however is directed to a privacy preserving centroid models generated using a secure multi-party computation cluster comprising two computing systems, e.g., MPC1 and MPC2, to which client devices upload secret shares of user profile data and an application running on a client device divides the user profile into secret shares for provision to the respective computing systems of the MPC cluster, the application uses Shamir's secret sharing scheme to perform this splitting (Wang ¶0087:“The exact splitting can depend on the secret sharing algorithm and crypto library used by the application 112. In some implementations, the application uses Shamir's secret sharing scheme.”; Wang ¶0137: same, in the context of an alternative, stronger privacy implementation).
It would have been obvious to one a POSTIA before the effective filing date of the claimed invention to implement Sun's secret sharing of the original user profile data using Wang's Shamir's Threshold Secret Sharing scheme. One of ordinary skill in the art would have been motivated to make this combination because Wang expressly identifies Shamir's Threshold Secret Sharing secret sharing algorithm for dividing user profile data among the computing systems of an MPC cluster, yielding the predictable result of enabling threshold reconstruction of the secret shared original data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAAD ABDULLAH whose telephone number is (571) 272-1531. The examiner can normally be reached on Monday - Friday, 9:30am - 5:30pm, EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynn Feild can be reached on (571) 272-2092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SAAD AHMAD ABDULLAH/Examiner, Art Unit 2431
/SHIN-HON (ERIC) CHEN/Primary Examiner, Art Unit 2431
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