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 .
Response to Amendment
This Office Action has been issued in response to Applicant’s Communication of amended application S/N 18/618,986 filed on May 26, 2026. Claims 1 to 5, and 10 to 20 are currently pending with the application.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter (See 37 CFR 1.75(d)(1) and MPEP § 608.01(o)). Correction of the following is required: claims 1, 10, and 16 recite the limitation “based on a window expansion”. The specification lacks antecedent basis for the claim terminology, and more specifically, for the term “window expansion”.
Claim Objections
Claim 11 is objected to because of the following informalities:
Claim 11 recites the limitations “encompasses all of rows of the rectangular array” in line 5, which appear to contain a typographical error and that should read “encompasses all rows of the rectangular array”.
Appropriate correction is required.
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 5, and 10 to 20 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 “each cluster of at least a portion of the identified features” in line 8 at page 3. There is insufficient antecedent basis for these limitations in the claim. Same rationale applies to claims 10 and 16, since they recite similar limitations, and to claims 2 to 5, 11 to 15, and 17 to 20, since they inherit the same deficiencies by virtue of their dependency.
Claim 1 recites the limitation “based on a window expansion” in line 5 at page 3. This limitation is not clear, and more specifically, it is not clear what the intent is, or what is meant by, “based on a window expansion”. Same rationale applies to claims 10 and 16, since they recite similar limitations, and to claims 2 to 5, 11 to 15, and 17 to 20, since they inherit the same deficiencies by virtue of their dependency.
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 5, and 10 to 20 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, and identifying 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 initial data items of the healthcare system 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 selected from the group consisting of: demographic characteristics, clinical observations, test results, and procedures performed”, 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 related to healthcare information. 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 a 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 a current position in each centrality measure list, across the centrality measure lists; and for each feature that is not unique within a current window: for each cluster whose combination with the feature has an empty perish score within the current window: storing a current position number corresponding to the current position 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 the 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 a position number in the centrality measure list for the cluster at which the feature occurs based on a window expansion”, 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.
The limitation of identifying persistence scores, which specifically recites “identifying persistence scores based on the identifying features of the second 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 second data item”, 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, “identifying”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, reading persistence scores associated with a second item, and determining a cluster in which to assign the second item, using the persistence scores.
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 based on a window expansion 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 – “receiving information identifying features of a second data item”, a machine learning computing system integrated into a healthcare system, one or more memories (claims 10, 16), and a processor (claim 16). The receiving limitation amounts to data-gathering steps, and is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)). The machine learning computing system integrated into a healthcare 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.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activity identified above, which include the data gathering steps, is recognized by the courts as well-understood, routine, and conventional activity 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)). The claims are not patent eligible.
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.
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 20, since they recite similar limitations as the ones discussed above, and are therefore similarly rejected.
Claims 1 to 5 and 10 to 20 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 LERNER et al. (U.S. Publication No. 2022/0093272) hereinafter Lerner, and further in view of Morikawa et al. (U.S. Publication No. 2006/0080296) hereinafter Morikawa.
As to claim 10:
Lerner discloses:
One or more memories collectively having contents configured to cause a machine learning computing system integrated to a healthcare system to perform a method [Paragraph 0061 teaches using machine learning classifiers for prediction of patient disease state], the method comprising:
accessing a multiplicity of data items of the healthcare system 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 selected from the group consisting of: demographic characteristics, clinical observations, test results, and procedures performed [Paragraph 0003 teaches patients are clustered into distinct patient clusters, hence, each item is a member of exactly one cluster; Paragraph 0059 teaches items or features associated with physical functionalities of the patients; Paragraph 0071 teaches clinical patient data is provided and includes feature-based representations, and used to generate characteristic clusters, based on characterizing features of each respective cluster];
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 0079 teaches each cluster centroid, representing all cluster patterns, is easily interpretable as a distinct disease progression pattern; Paragraph 0090 teaches computing variable importance, and ranking for the importance of the variables by which they can be optimally selected for classification; Paragraph 0099 teaches for each cluster, feature importance was evaluated using measured of decrease in accuracy and node impurity, where average values of importance were calculated for each feature and cluster];
for each cluster: establishing a sorted list of the features in descending order of the features’ centrality measures for the cluster [Paragraph 0091 teaches normalized sensitivities of the variables are ranked, determining an order of importance for the variables by which they can be optimally selected for classification; Paragraph 0099 teaches average values of importance for each feature and cluster are used to rank the features];
arranging the established sorted lists into a rectangular array in which each cluster’s sorted feature list is a column [Paragraph 0099 teaches ranking the features based on the average values of importance for each cluster; Table 4 teaches Five most predictive features for each cluster, ranked in descending order, where each cluster’s sorted feature list is a column];
receiving information identifying features of a second data item [Paragraph 0016 teaches receiving feature-based patient representations];
identifying persistence scores based on the identifying features of the second 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 second data item [Paragraph 0016 teaches assigning a new patient to one of the patient clusters in accordance with a best match; Paragraph 0021 teaches assigning a new patient to a patient cluster in accordance with a most closely matching cluster-specific prediction, where, as indicated above, the clusters are formed based on the most representative features, therefore, based on persistence scores].
Lerner does not appear to expressly disclose arranging the established sorted lists into a rectangular array based on a window expansion.
Morikawa discloses:
arranging the established sorted lists into a rectangular array based on a window expansion [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 Lerner, by arranging the established sorted lists into a rectangular array based on a window expansion 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]).
Response to Arguments
The following is in response to arguments filed on May 26, 2026. Arguments have been carefully and respectfully considered.
Claim Rejections - 35 USC § 101
Applicant’s arguments have been carefully and respectfully considered, but are not persuasive.
In regards to claim 1, Applicant argues that “the claimed invention is directed to a technical improvement in how machine learning computer systems analyze high-dimensional, categorical cluster data, i.e., a problem that specifically arises in computing environments”, and more specifically, that “persistence measure used in the instant application addresses a known technical limitation of TF/IDF (Term Frequency / Inverse Document Frequency) in high-dimensional overlapping feature spaces, i.e., claims improving computer functionality”.
In response to the preceding argument, Examiner respectfully disagrees, and respectfully submits that it is not clear, from the Applicant’s argument, what is the specific improvement in the functioning of a computer, or the improvement to another technology or technical field, that is achieved with the claimed invention, nor how the improvement is achieved with the limitations as presently presented. That is, it is not clear how such improvements correlate with the claims, as presently presented.
In regards to claim 1, Applicant further argues that “"accessing data items, analyzing cooccurrence, establishing a list of features, determining perish/persistence scores" are claim limitations that cannot practically be performed mentally nor with the aid of pen and paper. Rather, claim limitations of claimed invention cannot be practically performed in the human mind”.
In response to the preceding argument, Examiner respectfully disagrees, and respectfully submits that the broadest reasonable interpretation of the claims, as presently presented, and as further described in the rejections above, cover performance of the limitations in the human mind, but for the recitation of generic computer components, and therefore, are directed to an abstract idea without significantly more. Rejections under 35 USC § 101 are hereby sustained.
Claim Rejections - 35 USC § 103
Applicant’s arguments have been carefully and fully considered, but are moot in view of new grounds of rejections, as necessitated by the amendments.
Further, arguments regarding claim 10 have been carefully and respectfully considered, but are not persuasive.
In regards to claim 10, Applicant argues that the references are “completely silent with respect to iterative window expansion process”, and more specifically, an iterative window expansion process where “the facility establishes a window that initially encompasses the top row of this array, but is later expanded downward by one row at a time. The facility determines a persistence measure for each combination of cluster and feature that is based on the number of these expansions for which the feature is in the window for that cluster, but not for any of the other clusters. For each of the clusters, the facility identifies the features with the highest persistence values as those that best distinguish its data items from those of the other clusters”.
In response to Applicant’s argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “iterative window expansion process”, “establishing a window that initially encompasses the top row of this array, but is later expanded downward by one row at a time. The facility determines a persistence measure for each combination of cluster and feature that is based on the number of these expansions for which the feature is in the window for that cluster, but not for any of the other clusters. For each of the clusters, the facility identifies the features with the highest persistence values as those that best distinguish its data items from those of the other clusters”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAQUEL PEREZ-ARROYO whose telephone number is (571)272-8969. The examiner can normally be reached Monday - Friday, 8:00am - 5:30pm, Alt Friday, EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAQUEL PEREZ-ARROYO/Primary Examiner, Art Unit 2169
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