DETAILED ACTION
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 .
This Office Action has been issued in response to Applicant’s Communication of application S/N 18/618,986 filed on March 27, 2024. Claims 1 to 6, and 10 to 21 are currently pending with the application.
Election/Restrictions
Applicant’s election without traverse of Group I, claims 1 to 5, and 11 in the reply filed on September 2, 2025 is acknowledged. Claims 7 to 9 are cancelled. Election was made without traverse in reply filed on September 2, 2025.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 to 6, and 11 to 21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitations “the highest centrality measure” in line 13, “the current position” in lines 18 and 22, “the current window” in lines 19 and 21, and “the position number in the centrality measure list for the cluster at which the feature occurs” in line 2 at page 3. There is insufficient antecedent basis for these limitations in the claim. Same rationale applies to claim 16 since it recites similar limitations, and to claims 2 to 6, and 17 to 21, since they inherit the same deficiencies, by virtue of their dependency.
Claim 4 recites the limitation “the highest persistence scores for the cluster” in line 3. There is insufficient antecedent basis for this limitation in the claim. Same rationale applies to claim 15 since it recites similar limitations.
Claim 11 recites the limitation “the top row of the rectangular array” in line 3, “the rows of the rectangular array” in line 6, and “the other clusters” in line 9. There is insufficient antecedent basis for these limitations in the claim. Same rationale applies to claims 12 to 15, since they inherit the same deficiencies by virtue of their dependency on claim 11.
Claim 11 further recites the limitation “determining a persistence measure for the combination is based on the number of expansions for which the feature is in the window for the cluster, but not for any of the other clusters”, in line 8. This limitation is not clear, therefore rendering the claim indefinite. Upon further review of the specification, the limitation is still not clear. Same rationale applies to claims 12 to 15, since they inherit the same deficiencies by virtue of their dependency on claim 11.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 to 6, and 10 to 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1 and 16 recite accessing data items, analyzing cooccurrence, establishing a list of features, determining centrality, perish, and persistence scores. Claim 10 additionally recites arranging the sorted lists in an array.
The limitation of accessing data items, which specifically recites “accessing a multiplicity of data items organized into a plurality of clusters, such that each data item of the multiplicity is a member of exactly one cluster; each data item having one or more of a plurality of features”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by a processor” (claim 16), nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by a processor” language, “accessing”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, reading information of data items clustered into separate clusters, where the data items have associated features. The limitation of analyzing cooccurrence, which specifically recites “for each of the clusters: analyzing cooccurrence of features in individual data items of the cluster to obtain, for each feature, a measure of the feature’s centrality among data items of the cluster”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by the processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by the processor” language, “analyzing”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, studying the features in each of the clusters and determine cooccurrence, to further obtain a measure of each feature’s importance or centrality.
The limitation of establishing a list of features, which specifically recites “establishing for the cluster a sorted list of the features in descending order of their obtained centrality measures”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by the processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by a processor” language, “establishing”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, writing down a list of the features for each cluster, in descending order based on the previously obtained centrality measure.
Continuing with the analysis, the limitation of determining perish, which specifically recites “for each combination of one of the features with the cluster, initializing to empty a perish score and a persistence score; for each of a plurality of positions across the centrality measure lists of the clusters, beginning at a top of the lists containing the highest centrality measure of each centrality measure list and having an initial position number, and progressing to positions having increasingly lower centrality measures in the centrality measure lists and progressively higher position numbers: establishing a window encompassing from the top of each centrality measure list to the current position in each centrality measure list, across the centrality measure lists; and for each feature that is not unique within the current window: for each cluster whose combination with the feature has an empty perish score within the current window: storing the current position number as the perish score of the combination of the cluster and the feature”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by the processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by the processor” language, “storing”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, analyzing the sorted lists of features for each cluster in order from top to bottom, row by row, determining the features that are not unique, and for each cluster with an empty perish score in a current row, writing down the position number as a perish score for the combination of cluster and feature.
The limitation of determining persistence scores, which specifically recites “for each combination of cluster and feature: determining a persistence score for the combination of cluster and feature reflecting a degree to which the feature distinguishes items of the cluster from items of other clusters of the plurality of clusters, by determining a difference between the perish score determined for the combination of cluster and feature and the position number in the centrality measure list for the cluster at which the feature occurs”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by the processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by a processor” language, “determining”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, calculating a persistence score for the combination of cluster and feature, by determining a difference between the previously determined perish score, and the position number at which the feature occurs in the list.
Continuing with the analysis, in claim 10, the limitation arranging the sorted lists in an array, which specifically recites “arranging the established sorted lists into a rectangular array in which each cluster’s sorted feature list is a column”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by the processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by a processor” language, “arranging”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, writing down the sorted lists as a table where each of the list of each cluster is a column of the table. If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements – a computing system, one or more memories (claims 10, 16), and a processor (claim 16). The computing system, one or more memories, and processor in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim recites the additional limitations of “constructing a graph reflecting patterns of feature cooccurrence among the data items of the cluster; and performing a process against the graph to produce centrality measures for each of the features among data items of the cluster”, where the broadest reasonable interpretation covers performance of the limitations in the human mind; that is, the constructing the graph could be performed in the human mind with the aid of pen and paper, by drawing a diagram, and where the performing a process, which is not comprehensively described in the claim, could also be performed in the human mind, by determining centrality measures of the features depicted in the graph. Therefore, the claim is further elaborating on the abstract idea, and does not amount to significantly more. Same rationale applies to claim 4, since it recites limitations that are further elaborating on the abstract idea.
Claim 3 is dependent on claim 2 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim recites the additional limitations of “the performed process produces PageRank centrality measures; and wherein the constructed graph is an undirected graph”, where the performing a process producing PageRank centrality measures is directed to mathematical calculations, and where the construction of an undirected graph can be performed in the human mind with the aid of pen and paper. The claim does not amount to significantly more.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of claim 1. The claim recites the additional limitations of “for each of one or more of the plurality of clusters: displaying visual indications of one or more of the features based on their persistence scores for the cluster”, which amounts to data presentation steps, considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)), and recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d) (II)(v) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93)). Therefore, the limitations do not amount to significantly more than the abstract idea.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites the additional limitation of “receiving information identifying features of a distinguished data item; and using the persistence scores for each cluster of at least a portion of the identified features to predict a proper cluster for the distinguished data item”, where the predicting limitation further elaborates on the abstract idea since it can be performed in the human mind with the aid of pen and paper, and where the receiving element amounts to data-gathering steps, which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)), and recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d) (II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Therefore, the claim does not amount to significantly more than the abstract idea.
Additionally, the claims do not include a requirement of anything other than conventional, generic computer technology for executing the abstract idea, and therefore, do not amount to significantly more than the abstract idea.
Same rationale applies to claims 11 to 15, and 17 to 21, since they recite similar limitations as the ones discussed above, and are therefore similarly rejected.
Claims 1 to 6 and 10 to 21 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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 10 is rejected under 35 U.S.C. 103 as being unpatentable over Moerchen et al. (U.S. Publication No. 2008/0208847) hereinafter Moerchen, and further in view of Morikawa et al. (U.S. Publication No. 2006/0080296) hereinafter Morikawa.
As to claim 10:
Moerchen discloses:
One or more memories collectively having contents configured to cause a computing system to perform a method, the method comprising:
accessing a multiplicity of data items organized into a plurality of clusters, such that each data item of the multiplicity is a member of exactly one cluster; each data item having one or more of a plurality of features [Paragraph 0006 teaches ranking document clusters based on features, where each document or data item may include multiple features; Paragraph 0017 teaches clusters are a group, association, grouping, or agglomeration of documents, or document information associated with the documents assigned to a cluster; Paragraph 0031 teaches retrieving documents, clusters, document information, and feature vectors of documents];
for each combination of one of the clusters with one of the features: determining a measure of the feature’s centrality among data items of the cluster [Paragraph 0023 teaches storing cluster information such as a cluster centroid, e.g., a feature vector representative of the cluster; Paragraph 0033 teaches information includes a numerical feature vector of the documents, a numerical distance between the feature vector of the documents and the cluster centroid of their associated cluster, etc.; Paragraph 0037 teaches cluster information includes a conciseness measure of the cluster determined as the mean value plus one standard deviation of the distances between the feature vectors of the documents of the cluster and the centroid of the cluster, therefore, determining a measure of the feature’s centrality among the data items of the cluster];
for each cluster: establishing a sorted list of the features in descending order of the features’ centrality measures for the cluster [Paragraph 0041 teaches determining relevance factors based on document information; Paragraph 0047 teaches ranking objects based on relevance factor, by comparing their relevance factors to the relevance factor of other objects in the cluster, and sorting the objects into a hierarchical list].
Moerchen does not appear to expressly disclose arranging the established sorted lists into a rectangular array in which each cluster's sorted feature list is a column.
Morikawa discloses:
arranging the established sorted lists into a rectangular array in which each cluster’s sorted feature list is a column [Paragraph 0011 teaches obtaining characteristic words from the document groups and calculating the level of relative importance, to prepare a characteristic word list for each cluster; Paragraph 0036 teaches a characteristic table sorted based on levels of relative importance, where the objects of the sorting are the columns of the characteristic table, and where the sum of the levels of relative importance is calculated in each column and the columns are arranged from the left of the table in descending order of summed values].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Moerchen, by arranging the established sorted lists into a rectangular array in which each cluster's sorted feature list is a column, as taught by Morikawa [Paragraph 0011, 0036], because both applications are directed to document and feature cluster analysis; ordering the sorted features in a table enable the characteristics of the objects to be visually captured and easily grasped (See Morikawa Para [0013]).
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure.
GU et al., (U.S. Publication No. 2022/0091818) is directed to data feature processing, including obtaining groups of business data, where each group includes data features, calculating feature importance values of the data features, and further sorting the features in descending order of feature importance values. However, GU does not disclose arranging the sorted groups into a table or rectangular array in which each cluster’s sorted feature group or list is a column.
Shivamoggi et al., (U.S. Publication No. 2020/0184367) is directed to cluster interpretation, including determination of relative feature importances of features that drive the formation of clusters of data. However, Shivamoggi does not disclose the sorting of the features for further arranging the sorted groups into a table or rectangular array in which each cluster’s sorted feature group or list is a column.
Conclusion
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/RAQUEL PEREZ-ARROYO/Primary Examiner, Art Unit 2169