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 .
Election/Restrictions
Claims 1-11 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 5/31/2026.
Drawings
The drawings were received on 9/10/2024. These drawings are accepted.
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 12-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea in the form of mathematical concept where the mathematical computation is simple enough for a human mind to perform with gathering and organizing data without significantly more. The claim(s) recite(s) obtaining keywords (gathering data), filtering keywords (organizing data), organizing the keywords in to a graph or ontology, gathering data regarding the graph with keyword, calculating average edge density and intra-cluster density (simple mathematical concepts capable of being computed by human mind) and gathering the keywords meeting criteria via storage. In addition, the claimed language recites “a processing system …”. Such is merely directed towards a generic computer performing the abstract idea. This judicial exception is not integrated into a practical application because the claimed language is merely directed towards the abstract idea without positively recited language integrating the abstract idea into practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claimed language is merely directed towards the abstract idea without positively recited language indicating significantly more than the judicial exception.
Claims 13-16 recites language directed towards an abstract idea of mathematical concept, where the concept is simple and can be performed by the human mind. Hence, such limitations merely add to the abstract idea and does not include positively recited language integrating the abstract idea into practical application and/or indicate significantly more than the judicial exception.
Claims 17,18,19 recites language directed towards gathering and organizing data and does not include positively recited language integrating the abstract idea into practical application and/or indicate significantly more than the judicial exception. Hence, such limitations merely add to the abstract idea.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea in the form of mathematical concept where the mathematical computation is simple enough for a human mind to perform with gathering and organizing data without significantly more. The claim(s) recite(s) obtaining keywords (gathering data), filtering keywords (organizing data), organizing the keywords in to a graph or ontology, gathering data regarding the graph with keyword, calculating average edge density and intra-cluster density (simple mathematical concepts capable of being computed by human mind) and gathering the keywords meeting criteria via storage. This judicial exception is not integrated into a practical application because the claimed language is merely directed towards the abstract idea without positively recited language integrating the abstract idea into practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claimed language is merely directed towards the abstract idea without positively recited language indicating significantly more than the judicial exception.
Claim 21 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea in the form of mathematical concept where the mathematical computation is simple enough for a human mind to perform with gathering and organizing data without significantly more. The claim(s) recite(s) obtaining keywords (gathering data), filtering keywords (organizing data), organizing the keywords in to a graph or ontology, gathering data regarding the graph with keyword, calculating average edge density and intra-cluster density (simple mathematical concepts capable of being computed by human mind) and gathering the keywords meeting criteria via storage. In addition, the claimed language recites “a non-transitory computer readable storage medium …”. Such is merely directed towards a generic computer performing the abstract idea. This judicial exception is not integrated into a practical application because the claimed language is merely directed towards the abstract idea without positively recited language integrating the abstract idea into practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claimed language is merely directed towards the abstract idea without positively recited language indicating significantly more than the judicial exception.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 19 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 19 recites “determining whether any of the categories obtained for a corresponding keyword include one of a plurality of predefined strings; and filtering out those keywords based on the determining that the obtained categories for the corresponding keyword do not include at least one of a plurality of predefined strings”. Such amendment is found in the preliminary amendment as opposed to the original set of claims, hence is not considered part of the original specification.
The specification discloses the following regarding the first filtration:
[0098] Once the initial dataset is populated with keyword suggestions, then, at 504, a first filtering process is performed. The first filtering process is performed for each (referred to as the selected keyword below) of the keyword suggestions contained in the initial dataset.
[0099] The first filtering process includes performing a recursive search against a knowledge base to obtain all categories that are directly or indirectly associated with the selected keyword. An example knowledge base is Wikipedia where each keyword (e.g., article) is included in at least one category.
[00100] The recursive search operates by retrieving all of the categories associated with the selected keyword, and then retrieving all of the categories associated with each of those categories and so on. In some embodiments, the depth of the recursion is at least 3 levels deep, but may be 4, 5, or more levels deep depending on application need. In some embodiments, the depth of the recursion may be controlled manually (e.g., via a variable that is specified by a user) or may be dynamically controlled based on the number of categories that have been obtained or the amount time that has been taken for the recursive search. As an example, a threshold of 100 or 1000 categories may be used as a threshold where the recursion process is performed until the number of categories that have been obtained exceeds the threshold. As other example, a timer many control how long each recursive search is allowed to be performed.
Such paragraphs do not disclose the recited limitation. Hence, the claimed language does not match the disclosure, resulting in the claim containing subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
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.
Claim(s) 12-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tacchi et al (US Publication No.: 20180032606) in view of Miasnikof et al (NPL Title: A Statistical Density-Based Analysis of Graph Clustering Algorithm Performance).
Claim 12, Tacchi et al discloses
a processing system (Fig. 3) comprising instructions that, when executed by at least one hardware processor included in the processing system, cause the at least one hardware processor to perform operations (Fig. 3, label 1020,1010) comprising:
obtaining an initial dataset of keywords based on a first keyword (Paragraph 23 discloses “each topic may be associated with a set of n-grams, such as one, two, three, four or more consecutive words appearing in natural language text.” Paragraph 22 discloses “each topic may be labeled topics in the sense that each topic has a term that refers to the concept or set of concepts …”. The term is considered the first keyword and n-grams (words) associated with each topic is considered the initial dataset of keywords.);
performing a first filtering process that filters out at least some keywords in the initial dataset of keywords to obtain an intermediate dataset of keywords (Paragraph 25 discloses “the set of n-grams associated with each topic may be pruned. For example, in some cases, n-grams having a topic-specific score that does not satisfy a threshold, for instance, is less than a threshold value (e.g., 0.7), may be omitted from the set of n-grams corresponding to the respective topic.” The pruned set or n-grams associated with each topic is considered the intermediate dataset of keywords.);
generating, based on the intermediate dataset of keywords, a network graph (Paragraph 43 discloses a relationship graph of the documents is formed using n-grams of the selected topic. The formation of the relationship graph is based on the selected topic and keywords pertaining to that topic. Paragraph 25 discloses pruning the set of n-grams associated with each topic. Fig. 1, label 18 shows the generation of relationship graph of the n-gram set per topic, wherein pruning of the n-gram set is performed prior to generation of the relationship graph.),
the network graph that includes a plurality of nodes and edges (Paragraph 94 discloses “To visualize graph relations, some embodiments of module 824 may arrange vertices (also referred to as nodes) and edges using a physics simulation that mimics the stretching of spider webs.”), with each one of the plurality of nodes corresponding to a different one of the keywords included in the intermediate dataset of keywords (Paragraph 83 discloses a graph may be constructed with terms as nodes and weighted edges as indication of relationships. Paragraphs 25,43 discloses relationship graph is generated from the set of n-grams per topic, wherein pruning of the set of n-grams per topic is performed prior to generation of the graph.);
performing a second filtering process (Fig. 1, label 22 pruning of the relationship graph.) that includes:
determining a plurality of node clusters within the network graph (paragraph 56 discloses “some embodiments may cluster the relationship graph …”. This indicates a plurality of node clusters are determined within the network graph.);
adding, to a relevant cluster dataset, each of the plurality of node clusters meeting criteria (Paragraph 56 disclosing cluster the relationship graph and then prune links between clusters based on whether aggregate measures of relationships between clusters satisfy a threshold. Such indicates adding clusters meeting criteria to a relevant cluster dataset (relationship graph after pruning and evaluation as indicated in paragraph 56).) and
storing, as suggested keywords for the first keyword, each of the keywords that are associated with nodes that are included in at least one of the node clusters that are included in the relevant cluster dataset (Paragraph 75 discloses 822 stores resulting graphs in graph repository. Fig. 1, label 24 indicates a pruned and clustered relationship graph is generated, which indicates a resulting graph with associated nodes (n-grams) and clusters of nodes or clusters of n-grams related to respective topic included in the pruned relationship graph.)
Tacchi et al discloses clustering the relationship graph (Fig. 1, label 24), where clustering can be executed via density-based clustering algorithm (paragraph 89) and meeting a criteria as disclosed in paragraph 56, but fails to disclose the criteria is determined by calculating an average edge density for the network graph; calculating, for each of the plurality of node clusters, an intra-cluster density; and adding, to a relevant cluster dataset, each of the plurality of node clusters that has an intra-cluster density that is greater than the average edge density.
Miasnikof et al discloses determining cluster quality (whether a cluster meets a criteria) comprising
calculating an average edge density for the network graph (Page 8, equation for K, where K is the global density or average edge density for the graph’s overall connectivity measure.);
calculating, for each of the plurality of node clusters, an intra-cluster density (Page 9, equation Kintra as mean intra-cluster density. The mean intra-cluster connections ratio is the mean ratio of the number of edges within each cluster over the maximum number of edges that could possibly connect the vertices within each cluster.); and
each of the plurality of node clusters that has an intra-cluster density that is greater than the average edge density (Section 3.1, page 6 discloses “an efficient clustering algorithm will label vertices such that intra-cluster connectivity is greater than global and inter-cluster connectivity.” This indicates intra-cluster density is greater than global or average edge density and indicating cluster meets a criteria.).
It would be obvious to one skilled in the art before the effective filing date of the application to modify Tacchi et al’s clustering of the relationship graph according to a criteria by incorporating a criteria such as cluster quality as disclosed by Miasnikof et al so to improve the cluster quality of clustering the relationship graph of Tacchi et al, hence improving the graphs depiction of relationships between clusters.
Claim 13, Miasnikof et al discloses the plurality of node clusters are determined by optimizing a summed difference between an intra cluster density and an inter cluster density for the plurality of node clusters (Page 9, equation Kintra as the mean intra cluster density (summed intra cluster density) and Kinter as the mean inter cluster density (summed inter cluster density). Page 16, difference between Kintra and Kinter indicates the summed difference between intra cluster density and inter cluster density for a plurality of node clusters. Page 16 discloses cluster quality assessment routine, where the difference is used to determine cluster quality or optimization of clustering.).
Claim 14, Miasnikof et al discloses the intra cluster density of a corresponding cluster of nodes is calculated based on a number of internal edges of the corresponding cluster of nodes divided by half the number of nodes within the cluster of nodes and multiplied by the number of nodes minus 1 (Page 9, equation Kintra, Eii as the mean ratio of the number of edges within each cluster over the maximum number of edges that could possibly connect the vertices within each node divided by half (.5) of the ni (number of nodes) * number of nodes -1.).
Claim 15, Miasnikof et al discloses wherein the inter cluster density of a corresponding cluster of nodes is calculated based on a number of inter cluster edges for the corresponding cluster of nodes divided by half of the number of nodes within the cluster of nodes and multiplied by the number of nodes minus 1 (Page 9, equation Kinter, Kij is Ki).
Claim 16, Miasnikof et al discloses the intra cluster density of a corresponding cluster of nodes is calculated based on a number of internal edges of the corresponding cluster of nodes divided by half the number of nodes within the cluster of nodes and multiplied by the number of nodes minus 1 (Page 9, equation Kintra, Eii as the mean ratio of the number of edges within each cluster over the maximum number of edges that could possibly connect the vertices within each node divided by half (.5) of the ni (number of nodes) * number of nodes -1.).
Claim 17, Tacchi et al discloses
Querying, based on the first keyword, a knowledge-base to obtain a plurality of additional words (Fig. 1, label 14 searches for n-grams from corpus of documents (a knowledge-base) for each topic (first keyword).);
Generating a network graph of the plurality of additional keywords and phrases with the first keyword as an origin node within the network graph (Paragraph 23 discloses n-grams includes keywords, phrases extracted from documents (Fig. 1, label 14,12) for each topic (first keyword). Fig. 1, label 18 generates relationship graph where the n-grams are nodes for each topic, indicating the topic as an origin node.);
Selecting keywords for which the corresponding node within the network graph is within a defined distance of the origin node (Paragraph 26 discloses pruning n-grams with score that does not satisfy a threshold. This indicates a defined distance (score) between a keyword or n-gram of the n-grams and topic (origin node).); and
Setting the selected keywords as the initial dataset of keywords (paragraph 26 discloses pruning keywords not meeting a criteria. This indicates keywords meeting the criteria are set in the initial dataset of keywords.).
Claim 18, Tacchi et al discloses the operations further comprises: as part of the first filtering process (Fig. 1, label 14):
Obtaining, from a knowledge-base and for each corresponding keyword in the initial dataset of keywords, at least one category that the corresponding keywords belong to (Fig. 1, label 14 associates keywords (n-grams) (initial dataset) to respective topic (category). This indicates a corresponding keyword in the initial dataset corresponds to at least one category or topic. Fig. 1, label 12 indicates the corpus of documents where the keywords of the initial dataset is retrieved.); and
Recursively, for at least two additional iterations, obtaining categories to which the obtained categories belong to (Paragraph 40 discloses obtaining categories (topics) which belong to other categories (topics). For example, a topic “healthcare” may be merged with topic “food”. Paragraph 42 discloses “some embodiments may infer topics contributing to the cluster”. This indicates more than 1 iteration or search for categories belonging to other categories.),
Wherein the at least some keywords are filtered out based on the categories that have been obtained in connection with each corresponding category (Paragraph 26 discloses pruning of the n-grams (keywords) per a topic, wherein topics or categories may be found for a keyword. Pruning keywords (n-grams) per topic indicates keywords for a topic associated with the topics would occur.).
Claim 19, Tacchi et al discloses determining whether any of the categories obtained for a corresponding keyword include one of a plurality of predefined strings (Paragraph 24 discloses the n-gram (keyword) “court” is associated with topic “basketball” and “law”, wherein the score for association between n-gram “court” and respective topic differs. The determination of the categories indicates the topic “basketball” and “law” are not duplicates.); and
Filtering out those keywords based on determining that the obtained categories for the corresponding keyword do not include at least one of a plurality of predefined strings (Paragraph 24 discloses the topics or categories “basketball” and “law” with keyword “court” are determined. Paragraph 26 discloses pruning the n=grams associated with each topic. Pruning is performed when n-grams having a topic-specific score that does not satisfy a threshold. As long as “court” meets the criteria for keeping as opposed to pruning, the keyword is not filtered or pruned. This also indicates since the topics are not duplicates, the keyword “court” is not filtered.).
Claim 20 recites similar limitation as claim 12 and is rejected on the same grounds as claim 12.
Claim 21 recites similar limitations as claim 12 and is rejected on the same grounds as claim 12.
Conclusion
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/LINDA WONG/Primary Examiner, Art Unit 2655
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