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 .
Claims 1 – 18 are pending.
Response to Arguments
Applicant presents the following arguments in the 28 July amendment:
Specifically, the Examiner points to Howard paragraphs [0080-0082] for teaching "searching the index structure for nearest neighbors of database object" but this passage is not searching an index structure. Instead nearest neighbor searching such as by locality-sensitive hashing (LSH) is used to classify the binaries. Howard paragraph [0083] explains that "features of the binaries are extracted and encoded using deep learning autoencoder" which are then "represented by a set of floating-point vectors [].""These vectors are what need to be hashed by the LSH compiler []." The vectors are not an index, much less are they the claimed index structure which contains a plurality of index trees. The LSH searching described in paragraphs [0080-0083] is on the vectors which does not correspond to the claimed "searching the index structure." A search of an index is described in Howard paragraph [0085], but it fails to teach searching an "index structure" and fails to utilize the nearest neighbors found to generate a minimum spanning tree as per instant claim 1.
Howard teaches "[t]he hashes for each band for each binary are stored in the database, which is indexed on the band values. Therefore, matches for any binary can be located extremely quickly, simply by query on each band and returning the results." The band values are indexed in Howard and not a plurality of index trees by performing LSH. And the results of the search in Howard are not used to generate a minimum spanning tree. Thus, the disclosure in Howard paragraphs [0076-0086] does not teach the searching of an "index structure" or generating a minimum spanning tree as per present claim 1.
The Examiner also refers to Howard paragraph [0176-182] as showing these claimed steps. These passages relate to determining the lineage and evolutionary analysis of the malware. Paragraph [0176] begins with using a probabilistic graphical model "containing variables representing the true and observed timestamp of each sample, as well as a variable indicating whether the timestamp was obfuscated." The timing component is very important to Howard because "[w]ithout this information, it would be hard to determine the direction of parent-child relationships." Algorithms are employed that "identifies the best parent of each sample," "creates strong straight line sublineages using the best parents," and finally "merges sublineages using minimum spanning tree."' While minimum spanning tree is mentioned, it is not generated from nearest neighbors found from searching an "index structure" which contains a plurality of index trees, as per claim 1. The claimed index structure is established by performing LSH on the descriptor based on a plurality of hash functions. The algorithm-produced lineage in Howard is not generating an "index structure" as per claim 1. Thus, Howard paragraphs [0176-0182] do not teach or suggest searching an index structure for nearest neighbors and generating a minimum spanning tree from the nearest neighbors found, as recited in claim 1.
The Examiner acknowledges that Howard fails to teach the claimed step of using probabilistic layout algorithm to generate visualization data from the minimum spanning tree for visualization of the data objects in the database. To remedy this deficiency the 1 Howard at [0179]. Examiner turns to Hu. Per the Examiner, Hu teaches generating visualization of graph data. Missing is the use of probabilistic layout algorithm to generate visualization data.
Further missing is the requirement that the visualization data is generated from the minimum spanning tree. The Examiner does not allege these elements are taught in Hu. And, indeed, they are not. The probabilistic nature of graphing is not at all discussed in Hu. The embodiment described in paragraph [0030] of Hu, cited by the Examiner, describes the use of specialized hardware, namely "application specific integrated circuit (ASIC)", that executes a post-processing graphing application. No mention of probabilistic layout algorithm. And certainly, no mention of using minimum spanning tree in a probabilistic layout algorithm to generate visualization data. Thus, Hu does not teach the admittedly missing step in Howard.
But the rejection is further in error. Claim 1 requires the database objects to be molecules and the descriptor a molecular fingerprint. The Examiner turns to Deng as allegedly teaching database objects as molecules and an index comprising chemical fingerprints for the molecules. But Deng relates to a method for representing and analyzing 3D target molecule-ligand intermolecular interactions. The method generates structural interaction fingerprints ("SIFts") for a target molecule and a plurality of ligands. The SIFts convert three-dimensional structural interaction information into linear information strings that contain a plurality of information blocks. As per Deng paragraph [0037], the "database includes a plurality of SIFts generated from a target molecule and a plurality of ligands. Each SIFt is in the form of an information string that includes a plurality of information blocks, and each information block includes a plurality of information units." Contrary to the Examiner's assertion, the database objects are not molecules but are the fingerprint data known as SIFts and no index is described.
Nonetheless, the rejection lacks a suggestion or motivation for combining the structural interaction fingerprint data of Deng with the predictive malware techniques of Howard. While Howard mentions hashing and LSH techniques, no such searching is described in Deng. As Deng has no hashing or need for nearest neighbor searching, the Examiner's proposal to use the nearest neighbor searches of Howard are inapplicable. Indeed, it is not obvious that the teachings of Howard and Deng are combinable, much less that an average artisan would find a suggestion to apply the predictive malware method of Howard to chemical interaction modeling as disclosed in Deng.
Furthermore, the combined teachings of Howard, Hu, and Deng fail to suggest the claimed visualization. Howard provides no incentive to use a (i) probabilistic layout algorithm to generate visualization data from (ii) a minimum spanning tree for (iii) visualization of data objects in a database. Hu deals with methods for graphing data, i.e., visually depicting relationships between data, and describes an algorithm that overcomes the warping effects of the spring-electrical model, without destroying the global structure of the graph. Like Howard, Hu provides no suggestion of the claimed visualization using a probabilistic layout algorithm from minimum spanning tree. Deng does not teach visualizing molecules using a probabilistic layout algorithm from minimum spanning tree. Not only are the teachings not properly combined, but the cited teachings do not suggest the claimed visualization method.
Examiner presents the following responses to Applicant’s arguments:
With respect to applicant’s argument A, Applicant's arguments have been fully considered but they are not persuasive. Applicant appears to argue that Howard does not search an index structure, but later confirms that Howard discloses searching an index. Further Applicant cites Howard of Para. 0085, which is directed to the database scheme, using an index, to optimally enable rapid nearest neighbor search, which is used for family clustering of the hierarchical lineage. The hierarchical lineage including leaves, i.e. a tree structure. Therefore, Applicant’s admission for Para. 0085’s disclosure of index search is for a nearest neighbor search. Applicant appears to be declaring than an index structure is not an index; however, the claims and Applicant’s specification are silent to the differentiation even should, for argument’s sake, the argument be considered that the index of Howard is not an index structure as alleged. However, the argument is not persuasive given the lack of differentiation and the disclosure of Howard. Howard further discloses the scheme of family clusters comprising a lineage of parent-child relationships (i.e. based on the family clusters of the nearest neighbor search and hierarchical algorithms for family clustering within the lineage) which is further later optimized using a spanning tree algorithm. This lineage of parent-child relationships are a tree structure within the index of the database, clustered from the nearest neighbor search, and further used in the generation of the minimum spanning tree using the minimum spanning tree algorithm. Therefore, the claims are sufficiently disclose by Howard as currently claimed. See Howard: Para. 0080 – 0082, 0085 – 0086, 0119, 0172, 0176 – 0182, etc. as previously cited, see also 0083 – 0084, 0088, 0173 – 0175.
Further clarification within the claim language of the index structure, the spanning tree generated from the nearest neighbors found, the index trees, etc. may be sufficient in overcome the current rejection in view of Howard. However, under broadest reasonable interpretation (BRI) of the current claim language, as supported by the specification and Applicant’s above arguments, Howard discloses the current claim language.
With respect to applicant’s argument B, Applicant's arguments have been fully considered but they are not persuasive. See response to argument A above. Howards hierarchical lineage of the index within the database, comprising parent-child relationships that can be expanded by the addition of leaf(s), is an index structure comprising trees within. Applicant’s specification does not provide a limiting disclosure of the index structure, index trees within, etc. Further, the current language of the claims do not provide the differentiation between the index structure and the index of Howard as claimed. Nor between the index trees of the claims and the hierarchical lineage comprising leaves within the indexed database of Howard. Such clarifications within the language of the claims may be sufficient in overcoming the disclosure of Howard, as supported by Applicant’s specification.
With respect to applicant’s argument C, Applicant's arguments have been fully considered but they are not persuasive. See response to Applicant’s arguments A and B above.
With respect to applicant’s argument D, Applicant's arguments have been fully considered but they are not persuasive. Howard discloses the use of the probabilistic layout algorithm via the probabilistic graphical model for construction of the lineage graph. However, Howard not explicitly disclose the graph being visualization data, Hu was cited for its disclosure of visualization of generated graph data. Applicant appears to be arguing the references individually using piecemeal analysis for each not showing the whole. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
With respect to applicant’s argument E, Applicant's arguments have been fully considered but they are not persuasive. See response to Applicant’s argument D above. Piecemeal analysis that Hu does not disclose the features that were cited by Howard is not persuasive as one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Howard discloses the use of the probabilistic layout algorithm in the creation of the lineage graphs from the hierarchical lineage structures of the index, but does not explicitly disclose the generated graphs, particular by the use of the probabilistic graphical model of Howard generates visualization data for/within the graph data. Hu’s use of visualizing graph data on the device is cited for the generation of visualization data for graph data. Such graph data as produced by the probabilistic layout model of Howard. Howard and Hu are both disclose the generation of graph data from tree structures, including the use of spanning trees.
With respect to applicant’s argument F, Applicant's arguments have been fully considered but they are not persuasive. Deng was cited for the use of an index within a database comprising molecules and molecular data, including fingerprints of the molecules. Applicant appears to argue that Deng does not comprising molecules but merely fingerprints of molecules. However, Applicant’s own specification discloses the use of fingerprints as descriptors indicative of properties of a molecule and is the molecular structure of a molecule, see pages 16 – 17 and further 22 – 23 for examples stating the fingerprints are the representations of the molecules within the database. Therefore, it appears Applicant arguments against Deng not disclosing molecules as objects within the database apply to Applicant’s own disclosure. Deng discloses the use of SIFt data structures to allow for search of molecules, when modifying Howard in view of Hu sufficiently disclose the claim language in the current form. Further clarification within the claim language is necessary to narrow the interpretation as supposed by BRI and Applicant’s own specification at large, particularly to elements beyond mere intended use of the data within the index structures being molecules and fingerprints thereof.
With respect to applicant’s argument G, Applicant's arguments have been fully considered but they are not persuasive. See response to Applicant’s argument F above. Further, Applicant again appears to make piecemeal analysis arguments and one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
With respect to applicant’s argument H, Applicant's arguments have been fully considered but they are not persuasive. Applicant appears to be utilizing piecemeal arguments as reasons for combination of references; however, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1 – 9, 11 – 14 and 16 – 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2019/0199736 issued to Howard et al (hereinafter referred to as Howard) in view of U.S. Patent Application Publication No 2010/0134497 issued to Hu et al (hereinafter referred to as Hu) and in further view of U.S. Patent Application Publication No. 2007/0020642 issued to Deng et al (hereinafter referred to as Deng).
As to claim 1, Howard discloses establishing an index structure for a plurality of database objects by providing a descriptor for each of the plurality of database objects and a plurality of hashing functions and specifying a plurality of index trees by performing a locality sensitive hashing of the descriptor based on the plurality of hash functions (indexed bands of hashes stored in the database to be searched, hierarchical clustering algorithms for processing the binaries for storage using the signatures of each binary, and locality-sensitive hashing in the nearest neighbor search, see Howard: Para. 0010, 0072, 0076 – 0079, 0085 - 0086, 0119, 0126, 0139 – 0142, 0172, 0176 – 0182),
searching the index structure for nearest neighbors of database objects (nearest neighbor searches (NNS) for the binaries, see Howard: Para. 0080 – 0082, 0176 – 0182),
generating a minimum spanning tree from nearest neighbors found (using minimum spanning tree for optimizing parent-child relationships between the binaries, see Howard: Para. 0080 – 0082, 0176 – 0182), and
using a probabilistic layout algorithm to generate graph data from the minimum spanning tree for graphing of data objects in a database (using a probabilistic graphical model to express the structure to the lineage graph constructor, see Howard: 0089 and 0176 – 0182).
However, Howard does not explicitly disclose using a probabilistic layout algorithm to generate visualization data; wherein the database objects are molecules, and wherein establishing an index structure for a plurality of database objects comprises providing as a descriptor a molecular fingerprint..
Hu teaches generating visualization of graph data (visually producing the graph on a display device, see Hu: Para. 0030).
Hu and Howard are analogous for their disclosure of nearest neighbor searches.
Therefore, it would have been obvious to one of ordinary skill in the art to modify Howard’s use of distance measures in nearest neighbor searches with Hu’s use of generating a visualization of graph data in order to provide a system and process for graphing data.
However, Howard modified by Hu does not explicitly disclose wherein the database objects are molecules, and wherein establishing an index structure for a plurality of database objects comprises providing as a descriptor a molecular fingerprint.
Deng teaches wherein the database objects are molecules (molecules added to the database, see Deng: Para. 0024, 0037, 0150, 0172), and
wherein establishing an index structure for a plurality of database objects comprises providing as a descriptor a molecular fingerprint (index comprising chemical fingerprints for the molecules, see Deng: Para. 0036 – 0037, 0093, 0127, 0191, 0252, 0268).
Deng, Hu and Howard are analogous due to their disclosure of searching database objects.
Therefore, it would have been obvious to one of ordinary skill in the art to modify Howard and Hu’s use of distance measures in nearest neighbor searches with Deng’s use of databases and indices comprising molecules and fingerprints as unique descriptors for molecules in order to provide a database includes a plurality of SIFts generated from a target molecule and a plurality of ligands, each SIFt in the form of an information string that includes a plurality of information blocks and units (Deng: Para. 0037).
As to claim 2, Howard modified by Hu and Deng discloses wherein establishing an index structure, the database or parts thereof are retrieved from non-volatile computer-readable memory (can be stored in a centralized cloud system, locally on end-user client machines, etc. in memory, see Howard: 0112 – 0117, 0125 – 0126, 0144 – 0145 and 0244 – 0245).
As to claim 3, Howard modified by Hu and Deng discloses wherein the database comprises more than 100,000 objects (more than 100,000 binaries, see Howard: Para. 0093, 0094, 0152).
As to claim 4, Howard modified by Hu and Deng discloses wherein the database comprises objects having more than 20 dimensions (500 dimensions, more than 20, see Howard: Para. 0099).
As to claim 5, Howard modified by Hu and Deng discloses wherein at least one index tree specified has at least one sequence of linear nodes, and the step of establishing the index structure comprises collapsing the linear nodes (lineages are constructed for the indexed tree and ordered using a linear order, creates strong straight line sublineages using the best parents, see Howard: Para. 0172 – 0173, 0179, 0186, 0209 – 0210, 0215 – 0217, 0232).
As to claim 6, Howard modified by Hu and Deng discloses wherein an LSH forest is specified, comprising a plurality of different index trees (hash binaries into a high-dimensional space using LSH with hash families, including lineages and sub-lineages thereof, see Howard: Para. 0139 – 0142 and 0179 – 0182).
As to claim 7, Howard modified by Hu and Deng discloses wherein the LSH forest comprises many trees that are smaller than the number of different hashing functions, in particular, smaller than half of the number of different hashing functions (using buckets for condensing hash functions, in a scheme of n hash functions and b bands comprising a set of n/b hashes and must hash into identical sets of buckets for an entire band (hashing into two identical bucks is halving the hashes), see Howard: Para. 0139 – 0142).
As to claim 8, Howard modified by Hu and Deng discloses wherein the LSH tree or LSH forest is stored for the next neighbor search while searching for next neighbors (LSH compiling into hashes that will allow nearest neighbor searches and stored in databases in the computer systems for future searches, computer systems comprising RAM and storage media, see Howard: Para. 0119, and 0242 – 0244, and reducing client endpoint memory usage for LSH produced database, wherein memory is RAM, see Howard: Para. 0124, 0146 and 0245, see also Para. 0172, 0189, 0205, and 0230, the endpoint clients’ memory (RAM) is utilized for storing the LSH produce database entries).
As to claim 9, Howard modified by Hu and Deng discloses wherein the molecular fingerprint is a MinHash Fingerprint (MHFP) or Extended-Connectivity Fingerprint (ECFP) (ECFP, see Deng: Para. 0191).
As to claim 11, Howard modified by Hu and Deng discloses wherein the step of searching the index structure for nearest neighbors of database object comprises selecting a number of k approximate next neighbor objects, in particular selecting k next neighbors from kc*k neighbors identified with a kc>1 (selecting and classifying binaries that are nearest neighbors, including nearest neighbors with high confidence values, more than 1 nearest neighbors for determining the nearest neighbor is kc*k wherein kc>1, see Howard: Para. 0092, 0119, 0123 – 0126, 0138 – 0142).
As to claim 12, Howard modified by Hu and Deng discloses wherein the probabilistic layout algorithm comprises the use of a graph drawing technique (proximity graph methods for high probability hashing functions, see Howard: Para. 0139 – 0142, 0162, 0164, 0176 – 0182).
However, Howard does not explicitly disclose wherein the probabilistic layout algorithm comprises the use of a force-directed graph drawing technique.
Hu teaches wherein the probabilistic layout algorithm comprises the use of a force-directed graph drawing technique (post-processing transformation of graph using scalable force directed placement, see Hu: Para. 0042 and 0054 – 0056).
Hu and Howard are analogous due to their disclosure of nearest neighbor searches in determination of graph tree layouts.
Therefore, it would have been obvious to one of ordinary skill in the art to modify Howard’s use of probabilistic graph algorithms utilizing nearest neighbor searches with Hu’s use of creating force directed graphs in order to provide a computationally efficient algorithm that overcomes the warping effects of the spring-electrical model without destroying the efficiency and good global structure that may be achieved with the spring-electrical model.
As to claim 13, Howard modified by Hu and Deng discloses wherein the probabilistic layout algorithm is an efficient probabilistic layout algorithm (MinHash is an efficient locality-sensitive hashing scheme, see Howard: Para. 0078).
As to claim 14, Howard modified by Hu and Deng discloses the limitations of claim 13 substantially as claimed; however, Howard does not explicitly disclose wherein the efficient probabilistic layout algorithm comprises the use of a spring- electrical model layout method with a multilevel multipole-based force approximation.
Hu teaches wherein the efficient probabilistic layout algorithm comprises the use of a spring- electrical model layout method with a multilevel multipole-based force approximation (implementing spring-electrical model to approximate forces for multi-level data structures to efficiently and effectively draw large graphs, see Hu: Para. 0004, 0054 – 0059).
Hu and Howard are analogous due to their disclosure of nearest neighbor searches in determination of graph tree layouts.
Therefore, it would have been obvious to one of ordinary skill in the art to modify Howard’s use of probabilistic graph algorithms utilizing nearest neighbor searches with Hu’s use of spring-electric models in order to provide a computationally efficient algorithm that overcomes the warping effects of the spring-electrical model without destroying the efficiency and good global structure that may be achieved with the spring-electrical model.
As to claim 16, Howard modified by Hu and Deng discloses the computer-implemented method of claim 1, wherein establishing an index structure, the database or parts thereof are retrieved from non-volatile computer-readable memory, wherein the non-volatile computer-readable memory is a local disk, a web-server or a cloud (To ensure that the system scales or is scalable to the needs of modern malware defense, it was developed it to be a scalable, distributed enterprise-based system. An endpoint application provides rapid classification locally on end-user client machines, while a centralized cloud system stores the complete database of samples, handles the learning of the autoencoder model, and performs full-featured on-demand classification of edge cases that cannot be classified on the endpoint above a specified confidence threshold. To ensure speed and scalability, the classification method is based on an efficient locality-sensitive hashing (LSH) technique built on top of a distributed NoSQL database. It was also designed to operate in an enterprise environment, where client machines are exposed to binaries that need to be classified, and a central server, or servers, provides support for classification, see Howard: Para. 0112 – 0114, and can be stored in a centralized cloud system, locally on end-user client machines, etc. in memory, see Howard: 0112 – 0117, 0125 – 0126, 0144 – 0145 and 0244 – 0245).
As to claim 17, Howard modified by Hu and Deng discloses the computer-implemented method of claim 1, wherein the LSH tree or LSH forest is stored for the next neighbor search in a RAM while searching for next neighbors (reducing client endpoint memory usage for LSH produced database, wherein memory is RAM, see Howard: Para. 0124, 0146 and 0245, see also Para. 0172, 0189, 0205, and 0230, the endpoint clients’ memory (RAM) is utilized for storing the LSH produce database entries, and LSH compiling into hashes that will allow nearest neighbor searches and stored in databases in the computer systems for future searches, computer systems comprising RAM and storage media, see Howard: Para. 0119, and 0242 – 0244).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Howard in view of Hu and Deng, and in further view of U.S. Patent Application Publication No. 2015/0242393 issued to Zaragoza et al (hereinafter referred to as Zaragoza).
As to claim 10, Howard modified by Hu and Deng discloses wherein the step of searching the index structure for nearest neighbors of database object comprises identifying neighbor objects that are approximately next neighbors in view of a Hamming distance measure, a Levenshtein distance measure, a Cosine similarity measure, and a Jaccard similarity measure (cosine similarity and Jaccard similarity measures, see Howard: Para. 0078, 0092, 0126).
However, Howard modified by Hu and Deng does not explicitly disclose the use of a Hamming distance measure or a Levenshtein distance measure.
Zaragoza discloses the use of a Hamming distance measure and a Levenshtein distance measure in nearest neighbor searches (see Para. 0010).
Zaragoza, Deng, Hu and Howard are analogous for their disclosure of database object searches.
Therefore, it would have been obvious to one of ordinary skill in the art to modify Howard, Hu and Deng’s use of distance measures in nearest neighbor searches with Zaragoza’s use of Hamming and Levenshtein distance measures in order to perform NNS on a given collection of points and a given metric, such as in automatically classifying passages by first looking up the most similarly classified passage in a storage system.
Claim(s) 15 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Howard in view of Hu and Deng, and in further view of U.S. Patent No. 11,132,721 issued to Uthaman et al (hereinafter referred to as Uthaman).
As to claim 15, Howard modified by Hu and Deng discloses visual output of graph data; however, Howard modified by Hu and Deng does not explicitly disclose wherein the visualization data is output in a portable data format.
Uthaman teaches wherein the visualization data is output in a portable data format, in particular as portable HTML data (generating graph using probabilistic approaches including nearest neighbor search, see Uthaman: Col. 13 lines 22 - 56, and outputting using HTML to the client device see Uthaman: Col. 18 lines 5 – 28, and the client device including portable/remote device, see Uthaman: Col. 19 line 31 – Col. 21 line 31).
Uthaman, Deng, Howard and Hu are analogous for their disclosure of database object searches.
Therefore, it would have been obvious to one of ordinary skill in the art to modify Howard, Hu and Deng’s use of distance measures in nearest neighbor searches with Uthaman’s use of HTML to display the graph data in order to provide dynamic content to users while maintaining optimal network response rates.
As to claim 18, Howard modified by Hu and Deng discloses visual output of graph data; however, Howard modified by Hu and Deng does not explicitly disclose the computer-implemented method of claim 1, wherein the visualization data is output in a portable data format, wherein the portable data format is portable HTML.
Uthaman teaches the computer-implemented method of claim 1, wherein the visualization data is output in a portable data format, wherein the portable data format is portable HTML (generating graph using probabilistic approaches including nearest neighbor search, see Uthaman: Col. 13 lines 22 - 56, and outputting using HTML to the client device see Uthaman: Col. 18 lines 5 – 28, and the client device including portable/remote device, see Uthaman: Col. 19 line 31 – Col. 21 line 31).
Uthaman, Deng, Howard and Hu are analogous for their disclosure of database object searches.
Therefore, it would have been obvious to one of ordinary skill in the art to modify Howard, Hu and Deng’s use of distance measures in nearest neighbor searches with Uthaman’s use of HTML to display the graph data in order to provide dynamic content to users while maintaining optimal network response rates.
Additional References
U.S. Patent Application Publication No. 2010/0312537 issued to Rayan et al discloses the use of indices of molecules and molecular data, including the use of ECFP’s as molecular descriptors within the indices, see Para. 0020 and 0059, and would be an alternative references for Deng above. Rayan further discloses the use of K-nearest neighbor searches and would be analogous with Howard and Hu for a 35 USC 103 rejection.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MARK E HERSHLEY/Primary Examiner, Art Unit 2164