DETAILED ACTION
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 Arguments
The Office has withdrawn the previous objections to the Specification and Abstract, and the previous rejection of the claims under 35 USC §112 have been fully considered but they are not persuasive. The Office has largely maintained the previous rejection of the claims under 35 USC §101 (with modifications to address the amendments), including the 35 USC §101 rejection of the media claims.
Applicant's arguments, filed 4/15/2026, concerning the previous rejection of the claims under 35 USC §101 have been fully considered but they are not persuasive.
Regarding the rejections of the claims under 35 USC §101, Applicants argue on pages 23-26 that the newly amended claims should not be rejected under 35 USC 101 because they reflect an improvement in the functioning of a computer / technology.
The Office respectfully disagrees. First, it is noted that these claims represent actions that claim the mere use of generic computer components. Additionally, it is noted that the claim language, itself, connotes the use of either mental steps (or the use of pencil and paper) or mathematics, as “calculating …”, for example, could conceivably be performed mentally or via the use of mathematics.
And, it is further noted that generic computers performing generic computer functions to apply an abstract idea do not amount to significantly more than the abstract idea of organizing information through mathematical correlations. It is noted that the Internet/computer limitations are simply a field of use that attempt to limit the abstract idea to a particular technological environment and do not add significantly more than the abstract idea itself. Viewing the limitations as a combination does not add anything further than looking at the limitations individually.
Applicants further argues on pages 26-27 that the independent claims reciting substantially similar limitations, and all dependent claims are allowable for the reasons argued above.
The Office respectfully disagrees, and counter-asserts the rationale set forth above.
Therefore, the rejections of the claims under 35 USC §101 are believed to be reasonable.
As noted in previous interviews, the best way to overcome the issue of abstractness is to claim implementation details (e.g., how a trajectory established) and integration that involves some sort of follow-on processing, not the mere storage of processed data (e.g., a unique mechanism for manipulating/archiving data to calculate the doppler effect would be abstract, but incorporating that unique mechanism and its data output into a radar tracking application would result in an “integration into a practical application”).
Allowable Subject Matter
Claims 1-7, 9 and 11-20 are allowable over the prior art. However, the claims remain rejected under 35 USC §101.
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Claim Rejections – 35 U.S.C. § 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 1-7, 9 and 11-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
These claims are rejected under 35 USC §101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites at a very level, the manipulating and subsequent viewing of that manipulated data. Thus, the claims encompass the performance of the limitations in the mind, or alternatively the solving of a math problem (i.e., a series of mathematical steps) that are not tied to a practical application.
Regarding the independent claims:
Step 1: Yes, claim 1 recites a method for a series of steps executed (therefore a process embodied in a product/machine), claim 15 is directed to a system (therefore a product/machine), and claim 18 is directed to a computer program product (therefore a product). Thus, each of these claims is directed to a statutory category.
Step 2A, Prong 1 (Judicial Exception Recited?): Yes. Claims 1, 15 and 18 recite limitations directed to an abstract idea: “calculating … thereby generating for each … a numeric density-related target value …”, “partitioning …the dataset along the numeric and/or categorical features by … a regression decision tree using the numerical density-related target values to perform successive perpendicular cuts in feature space, thereby generating a … plurality of hyper-rectangular regions, …”, “generating … a map data structure that encodes … and associates each of the plurality of hyper-rectangular regions with a corresponding density value derived from the numerical density-related target values …”. As drafted, each of these limitations recites a mentally performable process, or alternatively a mathematical process, as one can perform a calculation and divide/categorize/partition data based upon its associated value via a mental (or mathematical) process or using paper and pencil.
Step 2A, Prong 2 (Integrated into a Practical Application?): No. Claim 1 recites the following additional elements: computerized, processor, memory and a display capability. Claim 15 recites a processor, memory and a display capability. And, claim 18 recites “one or more computer storage media”, and processing, storage and display capabilities. Each of these are merely high-level recitations of generic computer components and represent mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Additionally, claims 1, 15 and 18 each recites “receiving … a dataset …”, and “displaying the map”, each of which represents insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application. Also. Note that “claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). See MPEP 2106.04(a)(2)III.A.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Viewing the additional limitations together and the claims as a whole, nothing provides integration into a practical application. Therefore, each claim is directed to an abstract idea.
Step 2B (Inventive Concept Provided?): No, the discussions for the additional elements representing mere implementation using generic computing elements are carried over and do not provide significantly more. Mere instructions to apply an exception using generic computer components (e.g., storage) cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
With respect to the receiving steps and displaying steps discussed above, and when re-evaluated these elements are well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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);" and thus remain insignificant extra-solution activity that does not provide significantly more.
Additionally, it is noted that Examples that the courts have indicated may not be sufficient to show an improvement to technology include: gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48. MPEP 2106.05(a)II.
Therefore, each of the claims, taken as a whole, does not change this conclusion and the claim is ineligible.
Claims 2-7, 9 and 11-14 depend upon claim 1, and do not correct the issues set forth above. These claims essentially add generic computing elements, modify data, perform further calculations / data processing, and searching, and thus the same rationale as above applies. Therefore, these claims are likewise rejected.
Claims 16-17 and 19-20 depend upon claims 15 and 18, respectively, and do not correct the issues set forth above. These claims essentially add generic computing elements, modify data, and perform further calculations / data processing, and thus the same rationale as above applies. Therefore, these claims are likewise rejected.
35 USC §101 – media issue
Additionally, regarding independent claim 18: The claim is directed to a “computer program product comprising one or more computer readable storage media”.
The Specification at page 7 line 25 through page 8 line 2 discusses both a “computer program product” (e.g., “may include”) and a “computer readable storage medium” in an open ended manner (e.g., ‘may be’).
Therefore, the claim has been interpreted as encompassing signal subject matter.
During examination, the PTO is obliged to give claims their broadest reasonable interpretation consistent with the specification. See In re Zletz, 893 F.2d 319 (Fed. Cir. 1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and “Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. § 101,” Aug. 24, 2009, p. 2.
The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP § 2111.01. The same is true even when the computer readable medium is limited to a “storage” medium. See Ex parte Mewherter, No. 2012-007692, p. 6-14 (PTAB May 8, 2013) (precedential) (providing a “growing body of evidence … demonstrating that the ordinary and customary meaning of ‘computer readable storage medium’ to a person of ordinary skill in the art was broad enough to encompass both non-transitory and transitory media”).
The Office suggests adding the limitation “non-transitory” to the claim. See Subject Matter Eligibility of Computer Readable Media, 1351 OG 212 (February 23, 2010).
Claims 19-20 depend upon claim 18, and do not correct the issues set forth above. Therefore, these claims are likewise rejected.
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Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Relevance is provided in at least the Abstract of each cited document.
Non-Patent Literature
Das, Subhajit, et al., “Geono-Cluster: Interactive Visual Cluster Analysis for Biologists”, IEEE Transactions on Visualizations and Computer Graphics, Vol. 27, No. 12, December 2021, pp. 4402-4413.
Biologists often perform clustering analysis to derive meaningful patterns, relationships, and structures from data instances and attributes. Though clustering plays a pivotal role in biologists’ data exploration, it takes non-trivial efforts for biologists to find the best grouping in their data using existing tools. Visual cluster analysis is currently performed either programmatically or through menus and dialogues in many tools, which require parameter adjustments over several steps of trial-and-error. In this article, we introduce Geono-Cluster, a novel visual analysis tool designed to support cluster analysis for biologists who do not have formal data science training. Geono-Cluster enables biologists to apply their domain expertise into clustering results by visually demonstrating how their expected clustering outputs should look like with a small sample of data instances. The system then predicts users’ intentions and generates potential clustering results. (page 4402, Abstract). The parameter eps is set for each quantitative attribute Q by heuristics and can be adjusted. This technique allows users to pick data instances which are similar, based on the selected quantitative attribute qj. Further, users can select another quantitative attribute cell qk. Next, from the set of selected data instances U, the system finds all instances V which fall within a thresh old range of the value selected for attribute qk. Here the size of V is less than that of U. This technique allow users to filter and select a subset of data instances V from the Table View. For categorical features X, Geono-Cluster performs exact feature value matching instead of matching data items based on a predefined range. Users can drag-drop these V data items to the Cluster View as a single cluster (C = C1). They can continue selecting another set of data items, then add them to the cluster view as a new cluster (C = C1, C2). Users complete the data exploration or they can request the system to find a model Mi iteratively (T3). (page 4410, last paragraph of section “4.4 Computational Techniques”).
May, Thorsten, et al., “Guiding Feature Subset Selection with an Interactive Visualization”, IEEE Symposium on Visual Analytics Science and Technology, Providence, RI, October 23-28, 2011, pp. 111-120.
A method for the semi-automated refinement of the results of feature subset selection algorithms. Feature subset selection is a preliminary step in data analysis which identifies the most useful subset of features (columns) in a data table. So-called filter techniques use statistical ranking measures for the correlation of features. Usually a measure is applied to all entities (rows) of a data table. However, the differing contributions of subsets of data entities are masked by statistical aggregation. Feature and entity subset selection are, thus, highly interdependent. Due to the difficulty in visualizing a high-dimensional data table, most feature subset se lection algorithms are applied as a black box at the outset of an analysis. Our visualization technique, SmartStripes, allows users to step into the feature subset selection process. It enables the investigation of dependencies and interdependencies between different feature and entity subsets. A user may even choose to control the iterations manually, taking into account the ranking measures, the contributions of different entity subsets, as well as the semantics of the features. (page 111, Abstract). In this section we will describe the components of the SmartStripes approach in detail. Due to the close coupling of data processing and visualization components, a basic understanding of the background processes is necessary for the user to interact with the visualization effectively. Thus, we will begin our presentation with a description of how we partition the features. This will be followed by a description of the feature partition view, in which the partitions of features can be manipulated by the user. In the third subsection we will describe the decomposition of measures for relevance and redundance in order to increase the granularity of the overview. This will be followed by a description of the dependency view, in which the measures can be visualized and interactively explored. Finally, we will describe how the two views are linked to empower the user in guiding the process of feature selection. (page 114, section “4 The SmartStripes Approach”).
US Patent Application Publications
Kim 2018/0067625
A non-transitory tangible computer readable medium containing instructions configured to cause one or more processors to execute a process. The process comprises analyzing a dataset to determine a number of variables and one or more types of variables associated with the dataset. One or more processors remove a number of predefined discrete grouping variables from the number of variables associated with the dataset and produces a set of remaining variables. The one or more processors select a chart type based on the number of variables and the one or more types of variables associated with the set of remaining variables and generate one or more charts depicting the dataset. The number of charts generated corresponds to the number of data points associated with the predefined discrete grouping variables. (Abstract). A graphical user interface (GUI) is commonly implemented within a software client to enable users to graphically view and manipulate data retrieved from a database system. The GUI can display a dataset, received from the database system, in the form of a trellis chart. A trellis chart, sometimes known as lattice charts or grid charts, is a series of similar graphs or charts that is concurrently presented. Each of the graphs or charts shows a different partition of the dataset. (para 0001). In an embodiment, GUI 106 generates one or more charts within a trellis chart to graphically represent query results within a display area of display device 102. The display area may be, for example, a window or container operated by client 104 and displayed on display device 102. The display area, when maximized (if allowable), can be at its largest, the size of the display screen of display device 102. In an embodiment, GUI 106 includes selectable graphical icons to enable a use to select a desired chart to graphically present query results within the display area. For example, selectable charts may include column charts, bar charts, line charts, area charts, scatter charts, among other types of charts having an x-axis and a y-axis. The chart type may be selected based on the type of data to graph. For example, line charts are commonly selected to display trends over time. (para 0021). At 1135, a pie chart, a funnel chart, or a map chart may be generated based on the remaining variables determined at 1133. One of these chart types may be selected for use in method 1100 to generate a graphical user display of a provided dataset. In an embodiment, one of these chart types may be selected when no continuous variables exist after predefined grouping variables are subtracted. For example, a dataset may store information in the form, “Twenty percent of Stage 1 sales in the United States were generated from the sale of shoes.” The variables associated with this dataset may be (1) a percentage of sales, (2) the stage of sales, (3) the region, and (4) the product. In an embodiment, the percentage of sales may be considered a discrete variable because it is categorical and represents a “part of a whole” rather than a pure numerical value. In this case, no continuous values exist leading to a determination of using a pie chart. Even after the subtraction of predefined discrete grouping variables, such as the stage, region, and/or product, no continuous values would exist. Thus, a pie chart may be selected. (para 0083).
US Patents
Atallah 12,067,358
The analytical concepts 266 include: a field 268 concept, which is a finite set of database fields. Examples of field concepts include “Sales” and “Product Category”; a value 270 concept, which is a finite set of values for a database field. Examples of value concepts include the value 10,500,000.00 for a Sales data field and the value “Chairs” for a Product Category data field; an aggregation 272 concept, which is a finite set of operators that aggregate the values of multiple rows to form a single value based on a mathematical operation. Examples of aggregation concepts include “sum,” “average,” “median,” “count,” and “distinct count”; a group 274 concept, which is a finite set of operators that partitions the data into categories. An example of a group concept includes the “by” key value; a filter 276 concept, which is a finite set of operators that return a subset of rows from the database. (col. 11 lines 13-30). FIG. 3 illustrates a user interaction with the graphical user interface 100. In this example, the user inputs (e.g., enters or types) a natural language expression (e.g., a natural language command) 304 “year over year sales” in the command box 124. The user may also input the natural language expression by speech, which is then captured using an audio input device 220 (e.g. a microphone) coupled to the computing device 200. Typically, the natural language expression includes one or more terms that identify data fields from the data source 258. A term may be a dimension (e.g., categorical data) or a measure (e.g., a numerical quantity). As illustrated by the example, the natural language input typically includes one or more terms that correspond to data fields (e.g., the term “sales” identifies a data field from the data source). (col. 12 lines 48-62). In some implementations, the count in the text table is obtained by grouping the rows of the data source according to the values of a data field. For example, in some instances, the computing device groups the data source according to values of the data fields “City” and “State.” In some implementations, the computing device deduplicates the data rows (if there are any duplicates) in the data table data prior to displaying them in the graphical user interface. In some implementations, the graphical user interface includes a de-duplication toggle button (e.g., icon, affordance, user interactive element etc.) that, when selected, toggles between a count of all data rows and a distinct count of unique data rows. (col. 21 lines 25-37).
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 extension fee 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 date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT STEVENS whose telephone number is (571)272-4102. The examiner can normally be reached Mon - Fri 6:00 - 2:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached on (571) 270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT STEVENS/Primary Examiner, Art Unit 2164
April 29, 2026
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