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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 2/13/2024, and 1/13/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Status
Claims 10-17 are pending.
Claims 1-9 are cancelled.
Claims 16-17 are new.
Claims 10-17 are rejected.
Claim Objections
Claim 13 is objected to because of the following informalities: Line 10 of the claim should contain a ‘wherein the data repository’ prior to describing what the matching comprises. Appropriate correction is required.
Applicant is advised that should claim 16 be found allowable, claim 17 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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 10-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a server arrangement and method for processing molecular information and facilitating inter-party communication relating to molecular fingerprints. This judicial exception is not integrated into a practical application because while claims 10-17 attempt to integrate the exception into a practical application, said practical application is a generically recited computer element that does not add meaningful limitations to the abstract idea as it is simply implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of statutory subject matter (a process, machine manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically a system (10-12), methods (13-17), and a CRM (16-17).
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [see MPEP § 2106.04(a)]
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
The following claims recite abstract ideas (mental processes and mathematical concepts):
Claims 10 and 15: Processing molecular information, and encrypting the molecular fingerprints are processes of comparing/contrasting and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes. The molecular fingerprint being a representation of molecule in a multi-dimensional space enabling comparison with other molecules, the network comprising a variational autoencoder, and the network comprising an adversarial encoder are merely further limiting the data itself which are abstract ideas, specifically mental processes.
Claim 12: The input being a biopolymer sequence such as DNA, RNA, or protein sequence is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 13: Processing molecular information, matching the molecular fingerprint, and performing operations for generating a ciphertext are processes of comparing/contrasting and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes. The data repository comprising SMILE representations of molecules is merely further limiting the data itself which is an abstract idea, specifically a mental process. The network comprising a variational autoencoder, and the network comprising an adversarial encoder are merely further limiting the data itself which are abstract ideas, specifically mental processes.
Claim 14: The encrypted molecular fingerprint being trainable using the specified method is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 16 and 17: Processing molecular information, matching the molecular fingerprint, and performing operations for generating a ciphertext are processes of comparing/contrasting and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes. The data repository comprising SMILE representations of molecules is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [see MPEP § 2106.04(d)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of non-abstract elements:
Claims 1 and 15: Receiving an input is an insignificant extra solution activity, specifically mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839- 40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)]. A server is a generic and nonspecific element of a computer that does not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)]. Storing the encrypted fingerprints in a data repository is an insignificant extra solution activity, specifically mere data gathering and an insignificant application (See Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93) [See MPEP § 2106.05(d) & MPEP § 2106.05(g)].
Claim 11: Receiving an input is an insignificant extra solution activity, specifically mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839- 40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Claim 12: Receiving an input is an insignificant extra solution activity, specifically mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839- 40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Claims 13, and 16-17: Receiving an input is an insignificant extra solution activity, specifically mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839- 40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)]. A computer readable medium, and computing hardware are generic and nonspecific elements of a computer that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract ideas into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exceptions because the claims recite additional elements that are generic, conventional, nonspecific, or insignificant extra solution activity. These additional elements include:
The additional elements of receiving an input, and storing the encrypted fingerprints in a data repository are insignificant extra solution activities, specifically mere data gathering and insignificant applications, that are recognized as well understood, routine and conventional by the courts (See Analyzing DNA to provide sequence information or detect allelic variants, Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546, Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log), and Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of a computer readable medium, servers, and computing hardware are generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See § MPEP 2106.05(d)(II)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 10-17, when the limitations are considered individually and as a whole, are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 10-13, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Iwamura et al. (US 20140355756 A1) and Samanta et al. (Molecules (2020) 1-16).
Claim 10 is directed to a server arrangement for processing molecular information, encrypting said information and storing the information.
Claim 13 is directed to a method arrangement for processing molecular information, encrypting said information and storing the information.
Claim 15 is directed to a method arrangement for processing molecular information, encrypting said information and storing the information.
Claim 16 is directed to a CRM that performs the method of claim 13.
Claim 17 is directed to a CRM that performs the method of claim 13 (Duplicate Claim).
Iwamura et al. teaches in paragraph [0001] “The present invention relates to a search system and a search method for examining whether similar data is present within a database while maintaining the confidentiality of a search condition and the database”, in paragraph [0058] “The search system has a management device 10 that has a database 12 that stores fingerprints of compounds, and a searcher device 30 that transmits a query to the management device 10 to request a search of the database 12. The management device 10 and the searcher device 30 are connected by a network 50 such as the Internet”, reading on a server arrangement arranged to process molecular information, the server arrangement configured to. Iwamura et al. teaches in paragraph [0041] “FIG. 1 is a view illustrating an example of a fingerprint. A fingerprint is a way of representing a compound. A fingerprint represents whether or not a compound has a predetermined substructure with a sequence of numbers that takes "1" and "0" as elements”, and paragraph [0001] “The present invention relates to a search system and a search method for examining whether similar data is present within a database while maintaining the confidentiality of a search condition and the database”, reading on receive an input of the molecular information, wherein the molecular information comprises information pertaining to molecular structure of at least one molecule.
Iwamura et al. teaches in paragraph [0041] “FIG. 1 is a view illustrating an example of a fingerprint. A fingerprint is a way of representing a compound. A fingerprint represents whether or not a compound has a predetermined substructure with a sequence of numbers that takes "1" and "0" as elements. In the example shown in FIG. 1, the fingerprint represents the presence or absence of a "pyrrole ring", a "benzene ring", "O", "N", and a "double bond of N" in order from the first bit. A "0" value for an element indicates that the relevant structure is not present, and a "1" value indicates that the relevant structure is present. With respect to compound 1, since "1" is set in the second to fourth bits, the compound 1 includes a "benzene ring", "O", and "N", and since the values of the first and fifth bits are "0", the compound 1 does not include a "pyrrole ring" or a "double bond of N"… Methods of representing a compound using a fingerprint in this manner include an MDL MACCS key, a Daylight fingerprint, an ECFP (extended connectivity fingerprint), a FCFP (functional class fingerprint), and a pharmacophore key. An example of an MDL MACCS key is shown in FIG. 1… Although data for only five bits is shown as one example in FIG. 1, the length of an actual fingerprint is longer, and for example is 960 bits in the case of an MDL MACCS key. The present invention can be applied to whatever fingerprints of any kind are used”, reading on process the molecular information to map the molecular structure of each of the at least one molecule in the input to a low dimensional molecular fingerprint representative of the structural distance between molecules corresponding thereto using neural networks wherein the molecular fingerprint is a representation of the at least one molecule in a multi-dimensional space that enables comparison of the at least one molecule with other molecules using a randomized simplified molecular-input line-entry system (SMILES) representation of molecules by either convolutional or RNN layers, with Iwamura et al. specifically not teaching the use of a neural network.
Iwamura et al. teaches in paragraph [0134] “Although examples that use the Paillier cryptosystem as an encryption scheme that has an additive homomorphic property and a property as a probabilistic cryptosystem are described in the foregoing embodiments, any encryption scheme may be used in the present invention as long as the encryption scheme satisfies the condition of having an additive homomorphic property and a property as a probabilistic cryptosystem”, reading on encrypt the molecular fingerprints using a symmetric encryption algorithm with homomorphic properties. Iwamura et al. teaches in paragraph [0011] “A search system of the present invention comprises a management device having a database that stores data representing a substructure or a property of a substance”, reading on store the encrypted molecular fingerprints in a data repository. Iwamura et al. teaches in paragraph [0013] “In addition, apart from utilizing a cryptosystem having an additive homomorphic property that is mentioned here, it is also possible in the present invention to utilize a cryptosystem having a multiplicative homomorphic property and an additive homomorphic property”, and in paragraph [0050] “An additive homomorphic cryptosystem is a cryptosystem that can perform a calculation corresponding to addition in plaintext using encrypted values. Specifically, as shown in FIG. 3A, an additive homomorphic cryptosystem has a property such that an encrypted value Enc(A) of a value A and an encrypted value Enc(B) of a value B can be computed to calculate an encrypted value Enc(A+B) of a value A+B while in the encrypted state. An operation performed on the encrypted value Enc(A) and the encrypted value Enc(B) differs depending on the kind of cryptosystem”, reading on performing operations on the existing encrypted molecular fingerprint for generating a ciphertext using cryptographic algorithm, wherein the ciphertext enables multi-party computation between the first party and the second party by computing a private function on the matched molecular fingerprints.
Iwamura et al. does not teach the use of neural networks nor the specific use of variational auto encoders and adversarial autoencoders, or that the data comprise SMILES representations.
Samanta et al. teaches in the abstract “Molecular similarity is an elusive but core “unsupervised” cheminformatics concept, yet different “fingerprint” encodings of molecular structures return very different similarity values, even when using the same similarity metric. Each encoding may be of value when applied to other problems with objective or target functions, implying that a priori none are “better” than the others, nor than encoding-free metrics such as maximum common substructure (MCSS).We here introduce a novel approach to molecular similarity, in the form of a variational autoencoder (VAE). This learns the joint distribution p(z|x) where z is a latent vector and x are the (same) input/output data. It takes the form of a “bowtie”-shaped artificial neural network. In the middle is a “bottleneck layer” or latent vector in which inputs are transformed into, and represented as, a vector of numbers (encoding), with a reverse process (decoding) seeking to return the SMILES string that was the input”, and on page 10, paragraph 1 “The final architecture used here (shown in Figure 2C) required 6 days’ training on a 1-GPU machine. It involved a convolutional neural network (CNN) encoder with the following layers (Figure 2C): convolution (1D): size (in-248 = SMILES string length, 40 possible unique SMILES characters, out-9, kernel_size = 9), ReLU, convolution (1D): size (in-9, out-9, kernel_size = 9) ReLU, convolution (1D): size (in-9, out-10, kernel_size = 11) ReLU, Linear (fully connected): size(140, latent_dims = 100) SeLU, with VAE mean—Linear (fully connected): size(140, latent_dims = 100) and variance—Linear (fully connected): size(140, latent_dims = 100). For the decoder we used a Reparameterization (combined mean and sigma together) such that the output will be the same as the latent dimension (100 in our case), Linear (fully connected): size(latent_dims = 100, latent_dims = 100) SeLU, RNN-GRU (gated neural unit): size (hidden size = 488, num_layers = 3), Linear (fully connected): size(in-488 = hidden_gru_size, out-248 = SMILES length) Softmax”, reading on enables comparison of the at least one molecule with other molecules using a randomized simplified molecular-input line-entry system (SMILES) representation of molecules by either convolutional or RNN layers, and variational autoencoders wherein the molecular information is represented as distributions over a latent space instead of single points.
It would have been obvious at the time of first filing to have modified the teachings of Iwamura et al. for the majority of the independent claims with the teachings of Samatra et al. for the use of variational auto encoder networks comprising CNN and RNN layers that process and return SMILES data as the latter teaches in the abstract “The VAE vector distances provide a rapid and novel metric for molecular similarity that is both easily and rapidly calculated”, and on page 11, paragraph 1 “On this basis, an effect of training set size on the improvement of generalization (here defined simply as being able to return an accurate answer from a molecule not in the training set) is to be expected, and our ability to generalize (as judged by test set error) improved as the number of molecules increased up to a few million”. One would have had a reasonable expectation of success given that this is merely a substitution of one known method with expected results for another known method. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 11 is directed to the system of claim 10 but further specifies that the system be configured to receive an input from a user or database storing the information.
Iwamura et al. teaches in the abstract “A search system has a management device that stores compound data and a searcher device. The management device defines a similarity between query sequence data q representing a substructure or property of a substance that is input…”, reading on wherein the server arrangement is configured to receive the input from a user or a database storing the molecular information.
Claim 12 is directed to the system of claim 10 but further specifies the receiving of biopolymer sequences.
Samantra et al. teaches on page 6, paragraph 2 “Autoencoders that use SMILES as inputs can return three kinds of outputs: (i) the correct SMILES output mirroring the input and/or translating into the input molecular structure (referred to as “perfect”), (ii) an incorrect output of a molecule different from the input but that is still legal SMILES (hence will return a valid molecule), referred to as “good”, and (iii) a molecule that is simply not legal SMILES”, reading on wherein the server arrangement is configured to receive input of biopolymer sequences such as deoxyribonucleic acid sequence, ribonucleic acid sequence and/or protein sequence.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Iwamura et al. (US 20140355756 A1) and Samanta et al. (Molecules (2020) 1-16) as applied to claims 10-13 and 15-17 above, and further in view of Shokri et al. (Proceedings of the 22nd ACM SIGSAC conference on computer and communications security (2015) 1310-1321).
Claim 14 is directed to the method of claim 13 but further specifies training the networks using tanh activation in a privacy preserving manner by a collection of nodes each holding their own dataset.
Iwamura et al. and Samanta et al. teach the methods and system of claims 10-13 and 15-17 as previously described.
Iwamura et al. and Samanta et al. do not teach training the networks using tanh activation in a privacy preserving manner by a collection of nodes each holding their own dataset.
Shokri et al. teaches in the abstract “Massive data collection required for deep learning presents obvious privacy issues… Furthermore, centrally kept data is subject to legal subpoenas and extra-judicial surveillance. Many data owners—for example, medical institutions that may want to apply deep learning methods to clinical records—are prevented by privacy and confidentiality concerns from sharing the data and thus benefitting from large-scale deep learning. In this paper, we design, implement, and evaluate a practical system that enables multiple parties to jointly learn an accurate neural network model for a given objective without sharing their input datasets. We exploit the fact that the optimization algorithms used in modern deep learning, namely, those based on stochastic gradient descent, can be parallelized and executed asynchronously. Our system lets participants train independently on their own datasets and selectively share small subsets of their models’ key parameters during training”, and on page 1311, column 2, paragraph 9 “Multi-layer neural networks are the most common form of deep learning architectures. Figure 1 shows a typical neural network with two hidden layers. Each node in the network models a neuron. In a typical multi-layer network, each neuron receives the output of the neurons in the previous layer plus a bias signal from a special neuron that emits 1. It then computes a weighted average of its inputs, referred to as the total input. The output of the neuron is computed by applying a nonlinear activation function to the total input value. The output vector of neurons in layer k is ak = f(Wk ak1); where f is an activation function and Wk is the weight matrix that determines the contribution of each input signal. Examples of activation functions are hyperbolic tangent f(z) = (e2z - 1)(e2z + 1)-1, sigmoid f(z) = (1 + e-z)-1, rectifier f(z) = max(0; z), and softplus f(z) = log(1 + ez)”, reading on wherein the encrypted molecular fingerprint in the data repository can be trained by the neural networks using tanh activation function in a privacy preserving manner by a collection of nodes each holding their own dataset.
It would have been obvious at the time of first filing to have modified the teachings of Iwamura et al. and Samantra et al. for the method and system of claims 10-13 and 15-17, with the teachings of Shokri et al. for the use of tanh activation functions for privacy preserving as the latter teaches in the abstract “Our system lets participants train independently on their own datasets and selectively share small subsets of their models’ key parameters during training. This offers an attractive point in the utility/privacy tradeoff space: participants preserve the privacy of their respective data while still benefitting from other participants’ models and thus boosting their learning accuracy beyond what is achievable solely on their own inputs. We demonstrate the accuracy of our privacy preserving deep learning on benchmark datasets”. One would have had a reasonable expectation of success given that the use of an activation function is necessary within network architectures and this is merely one of many types of activation functions, meaning that this is merely a substitution of one known method for another. Therefore, it would have been obvious at the time of first filing for a person skilled in the art to have modified the teachings of each and to be successful.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEENAN NEIL ANDERSON-FEARS whose telephone number is (571)272-0108. The examiner can normally be reached M-Th, alternate F, 8-5.
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/K.N.A./Examiner, Art Unit 1687
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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