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 .
Priority
Acknowledgement is made of applicant’s claim for priority based on provisional Application No. 63/569,844, filed on 26 March 2024.
Information Disclosure Statement
Applicant is reminded of the continuing obligation under 37 CFR 1.56 to timely apprise the Office of any information which is material to patentability of the claims under consideration in this application.
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 1-19 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception (i.e., an abstract idea) without significantly more.
Independent claims 1 and 10 recite transforming the at least one dataset into an enriched dataset, and generating a visual representation of the enriched dataset containing a plurality of widgets that are configured to be explorable and navigable by a user to determine insights of the at least one dataset. These encompass managing personal behavior or relationships or interactions between people, which falls under the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, as well as an observation, evaluation, and/or judgment with respect to the transformation of data into an enriched dataset, and determining of insights, both of which fall under the “Mental Processes” grouping of abstract ideas.
Dependent claims 2 and 12 recite that a selection by the user of values displayed within the widgets which creates a variety of subsets of the enriched dataset represented by secondary widgets. Similarly, dependent claims 3 and 13 recite that a selection by the user of secondary values within the secondary widgets create further subsets which are selectable for the user to visualize specific information about the at least one dataset. These represent the abstract idea of “filtering data”, which falls under the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.1
Dependent claims 5 and 15 recite automatically generating the type of the plurality of widgets based on data values of the enriched dataset. This encompasses an evaluation, observation, and/or judgment (which falls under the “Mental Processes” grouping of abstract ideas), as well as “Certain Methods of Organizing Human Activity”.
With the exception of limitations reciting the use of a computing system and various hardware components, nothing in the claims preclude the claimed steps from being practically performed in the mind. If a claim limitation covers performance of the limitation in the mind but for the recitation of generic computer components, then such claims still fall within the “mental processes” grouping of abstract ideas. Additionally, other limitations recite “certain methods of organizing human activity” but for the recitation of such computing elements as described. Accordingly, the claims recite an abstract idea.
The claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed computing components, e.g., enricher, platform, memory, processor, machine learning, algorithms, and artificial intelligence models, etc., are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
More particularly, the claims attempt to narrow the computing elements to, e.g., using an enricher to transform the at least one dataset into an enriched dataset, the enricher utilizing at least one of machine learning, algorithms and artificial intelligence models (independent claim 1 and dependent claim 11), that the enricher is one of a variable buildable enricher or a built-in enricher (dependent claims 6 and 16), and that the variable buildable enricher is built on the platform using models trained by the user with a specific uploaded dataset (dependent claims 7 and 17). However, these nothing more than recite the abstract idea while adding “apply it” with a computer—here, with an enricher. Essentially, the claims only utilize an “enricher” with more descriptive characteristics, to describe a context rather than a particular manner of achieving the result, as they are not integrated into the abstract steps, i.e., represent a concrete embodiment as to how the claimed abstract steps are accomplished using such an enricher. As a result, such limitations are nothing more than insignificant field-of-use limitations.
Similarly, dependent claims 4 and 14 recite that the widgets visually represent at least one of: the enriched dataset, unstructured data from the data, and structured data from the data; and dependent claims 8 and 18 recite that the at least one dataset is one of: uploaded by the user and fetched automatically based on a query by the user. Similar to above, these are not integrated into the abstract steps, i.e., do not further indicate how, by what particular technical process, the claimed abstract steps are accomplished. Rather, such limitations only provide context, rather than a particular manner, of achieving the claimed results.
Independent Claims 1 and 10 further recite receiving a dataset. This is an insignificant pre-solution activity. The independent claims and dependent claims 2-3 and 12-13 further recite displaying the plurality of widgets on a platform. This is an insignificant extra-solution activity.
Dependent claims 9 and 19 further recite wherein upon enriching the data(set), a clone is generated of the data and the enriched dataset is added to the clone to preserve an integrity of the data(set). This is an insignificant extra-solution activity, which is a tangential or nominal addition to the claim, as it does not further explain how, by what particular process or structure, the claimed abstract steps are accomplished. Furthermore, it is an insignificant field-of-use limitation in that the enriched dataset is added to a clone (rather than the original data itself being modified), i.e., describing a context rather than a particular manner of achieving the result.
Accordingly, the claims are not integrated into a practical application of the idea.
The claims do not recite additional elements that amount to significantly more than the judicial exception.
As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of various computing hardware components, which amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Independent claims 1 and 10 recite receiving the dataset. This is nothing more than the well-understood, routine, and conventional activity of receiving/transmitting data. The independent claims and dependent claims 2-3 and 12-13 further recite displaying the widgets (on a platform). This is also nothing more than a well-understood, routine, and conventional activity in computers. See MPEP § 2106.05(d)(II) (“Receiving or transmitting data over a network, e.g., using the Internet to gather data” with regards to the receiving and displaying steps; and “Presenting offers and gathering statistics” with regards to the displaying step).
Dependent claims 9 and 19 recite adding the enriched dataset to a clone of data. This is the well-understood, routine, and conventional activity within computers. See MPEP § 2106.05(d)(II) (“Electronic recordkeeping” and “Electronically scanning or extracting data from a physical document”, as the claims are similar to making copies of some sort of original data and transforming it into a different from, albeit with enriched data which is an insignificant field-of-use limitation).
Even when considered as an ordered combination, the claimed elements do not add anything that is not already present when the steps are considered separately. In other words, even when considering the claimed invention as a whole, i.e., as an ordered combination, the additional elements add nothing that would move the claims outside the realm of abstract ideas. The claims, as a whole, do not state how—by what particular process or structure—the claimed steps are performed, including how the enrichment of data is performed (other than contextualizing it to computers performing the claimed steps, which does not add significantly more, as it states that abstract idea while adding the words “apply it” with a computer, with the same deficient result), or how the widgets are specifically generated, selected for display, or even displayed on the platform. Rather, the claimed steps are recited in a purely functional manner, rather than representing a concrete embodiment of the claimed steps.
See, e.g., Affinity Labs of Texas LLC v. DirecTV., 838 F.3d 1266 (Fed. Cir. 2016) at p. 7-8 (“At that level of generality, the claims do no more than describe a desired function or outcome, without providing any limiting detail that confines the claim to a particular solution to an identified problem. The purely functional nature of the claim confirms that it is directed to an abstract idea, not to a concrete embodiment of that idea”); and Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016), slip op. 12 (“[The] essentially result-focused, functional character of claim language has been a frequent feature of claims held ineligible under § 101”).
Thus, despite the claims’ attempt to narrow the claims to particular types of information (an enriched dataset), and particular types of interactive visual components (widgets), such limitations do not move the claims outside the realm of abstract ideas, as the claims are recited at such a high level of generality that they fall within certain methods of organizing human activity (e.g., allowing users to view, interact with/manipulate displayed data, etc.) and mental processes. Various court cases have previously found such ideas to be abstract.
See, e.g., Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016) (“…[the claims] do not require an arguably inventive set of components or methods, such as measurement devices or techniques, that would generate new data. Merely requiring the selection and manipulation of information—to provide a ‘humanly comprehensible’ amount of information useful for others…by itself does not transform the otherwise-abstract process of information collection and analysis”); and
Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 121 USPQ2d 1928 (Fed. Cir. 2017) (“Remotely accessing and retrieving user-specified information is an age-old practice that existed well before the advent of computers and the Internet… the claimed invention does not recite any particular unique delivery of information through this mobile interface. Rather, it merely recites retrieving the information through the mobile interface. Nor do the claims describe how the mobile interface communicates with other devices or any attributes of the mobile interface, aside from its broadly recited function. Thus, the mobile interface here does little more than provide a generic technological environment to allow users to access information…”).
At this level of generality, the claims do no more than describe a desired function or outcome, and without providing any limiting detail that confines the claims to a particular solution to an identified problem. The purely functional nature of the claims confirm that they are directed to an abstract idea, not to a concrete embodiment of the idea. A desired goal (i.e., result or effect), absent of structural or procedural means for achieving that goal, is an abstract idea. In this case, the claims are directed to an abstract idea for failing to describe how—by what particular process or structure—the goal is accomplished. Even with the additional elements, the claimed limitations fail to restrict how the goal is accomplished.
Thus, for at least the aforementioned reasons, the claims are rejected under 35 U.S.C. 101 for being directed to a judicial exception (i.e., an abstract idea) without significantly more.
Claim Rejections - 35 USC § 103
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.
Claims 1-6, 8-16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Dhir et al. (“Dhir”) (US 2022/0342912 A1), in view of Bahatyrevich et al. (“Bahatyrevich”) (US 11,755,344 B1).
Regarding claim 1: Dhir teaches A method of exploring data, the steps comprising:
receiving at least one dataset (Dhir, [0087-0088], where the data set enrichment and display tool receives the input (e.g., the selected and/or uploaded first and second data sets), and the preprocessing module receives the first and second data sets);
running an enricher on the at least one dataset, the enricher utilizing at least one of: machine learning, algorithms and artificial intelligence models, to transform the at least one dataset into an enriched dataset (Dhir, [0075] and [0093-0094], where the system receives preprocessed data sets and generates, based on the preprocessed data sets, an enriched data set by applying at least one entity matching technique and/or statistical matching technique, where the similarity between entities may be determined by inputting corresponding features of the first entity and first corresponding entity into one or more artificial intelligence or machine learning models);
generating a visual representation of the enriched dataset on a platform, the visual representation containing a [visualization], the [visualization] displayed on the platform and configured to be explorable and navigable by a user to determine insights of the at least one dataset (Dhir, [FIGs. 3A-3B] and [0144-0156], where the system visualizes data insights and/or recommendations, including an outcome of the data set enrichment and analysis (Dhir, [FIG. 3A]), and a region 326 in [FIG. 3B], which shows a visualization of one or more data analyses, the region including a graphical visualization of the sensitivity analysis and optimization analysis with each data point (e.g., circle 328) included in the graph representing an entity of the enriched data set).
Dhir does not appear to explicitly state that the visual representation contains a plurality of widgets.
Bahatyrevich teaches the visual representation contains a plurality of widgets (Bahatyrevich, [FIGs. 5-6] and [37:21-43], where five high level charts 502, 504, 506, 508, and 510 are shown, which are displayed in response to a user search query, where all five charts can be in response to the same query, showing different aspects of the results).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Dhir and Bahatyrevich (hereinafter “Dhir as modified”) with the motivation of providing greater intuitive interactions with the data, e.g., by enabling users to drill down and learn more with respect to certain data, as opposed to have to either write out a query for specific data, or utilize separate navigational controls.
Regarding claim 2: Dhir as modified teaches The method of Claim 1, wherein a selection by the user of values displayed within the plurality of widgets creates a variety of subsets of the enriched dataset, the subsets of the enriched dataset further represented by secondary widgets (Bahatyrevich, [FIGs. 5-6] and [37:21-67]-[39:1-14], where five high level charts 502, 504, 506, 508, and 510 are shown, which are displayed in response to a user search query. A user may have interest in more details regarding graph 608 on display 606, in which case the user clicks on chart 608 (or an associated icon) to bring up more detail in the form of a display, resulting in three different levels of detail being provided, with different types of charts at each level. See Bahatyrevich, [21:13-28], where the intake system 110 can enrich the received data, from which the visualizations are generated (i.e., “enriched dataset”)).
Regarding claim 3: Dhir as modified teaches The method of Claim 2 wherein the selection by the user of secondary values within the secondary widgets creates further subsets of the enriched dataset, wherein each of the further subsets is selectable for the user to visualize specific information about the at least one dataset (Bahatyrevich, [FIGs. 5-6], [FIG. 9], [FIG. 13], and [37:21-67]-[39:1-14], where five high level charts 502, 504, 506, 508, and 510 are shown, which are displayed in response to a user search query. A user may have interest in more details regarding graph 608 on display 606, in which case the user clicks on chart 608 (or an associated icon) to bring up more detail in the form of a display, resulting in three different levels of detail being provided, with different types of charts at each level. A third level of detail, e.g., chart 610, may be provided by clicking on, or otherwise activating, one of charts 608, 910, and 912, thereby displaying further details on those statistics, e.g., in a chart form. See Bahatyrevich, [21:13-28], where the intake system 110 can enrich the received data, from which the visualizations are generated (i.e., “enriched dataset”)).
Regarding claim 4: Dhir as modified teaches The method of Claim 1 wherein the widgets visually represent at least one of: the enriched dataset, unstructured data from the at least one dataset, and structured data from the at least one dataset (Dhir, [0154] and [FIG. 3B], where region 326 shows a visualization of one or more data analyses, the region including a graphical visualization of the sensitivity analysis and optimization analysis with each data point (e.g., circle 328) included in the graph representing an entity of the enriched data set).
Regarding claim 5: Dhir as modified teaches The method of Claim 1, wherein each one of the plurality of widgets has a type, and the type of the plurality of widgets is automatically generated based on data values of the enriched dataset (Bahatyrevich, [FIGs. 5-6], [FIG. 9], [FIG. 13], and [37:21-67]-[39:1-14], where five high level charts 502, 504, 506, 508, and 510 are shown, which are displayed in response to a user search query (i.e., indicating that the widgets are “automatically generated based on data values of the … dataset”, as queries are for retrieving data from datasets). A user may have interest in more details regarding graph 608 on display 606, in which case the user clicks on chart 608 (or an associated icon) to bring up more detail in the form of a display, resulting in three different levels of detail being provided, with different types of charts at each level, e.g., one or more of charts 608, 910, and 912 could be a different type of chart, such as a bubble chart, bar chart, etc. See Bahatyrevich, [21:13-28], where the intake system 110 can enrich the received data, from which the visualizations are generated (i.e., “enriched dataset”)).
Regarding claim 6: Dhir as modified teaches The method of Claim 1 wherein the enricher is one of: a variable buildable enricher or a built-in enricher (Dhir, [0065-0066], where the automated data set enrichment and analysis system 100 comprises a data set enrichment and analysis tool 101 that enriches ingested data).
Regarding claim 8: Dhir as modified teaches The method of Claim 1 wherein the at least one dataset is one of: uploaded by the user and fetched automatically based on a query by the user (Dhir, [0069], where data set enrichment and analysis may be initiated by a requestor 102 that indicates instructions to upload one or more data sets).
Regarding claim 9: Dhir as modified teaches The method of Claim 1 wherein upon applying the enricher, a clone is generated of the at least one dataset, and the enriched dataset is added to the clone to preserve an integrity of the at least one dataset (Dhir, [0077], where the enriched data set is stored, and the enriched data set may be compared to original data sets in the first data source 104 and/or second data source 106; see also, e.g., Dhir, [0090], where the enriched data set may be modified dynamically as the first data set and/or the second data set are modified, further indicating that the enriched data set is a “clone of the at least one dataset” with another (second) dataset. See Dhir, [0090], where the processing module may generate an enriched data set based on the preprocessed first data set and second data set).
Regarding claim 10: Claim 10 recites substantially the same claim limitations as claim 1, and is rejected for the same reasons.
Note that Dhir teaches A system for exploring data using a platform, the system comprising: a processor; a memory in communication with the processor, the memory comprised of computer executable instructions that, when executed by the processor, cause the processor to [implement the claimed steps] (Dhir, [0041], where the system may be embodied as a non-transitory computer-readable storage medium storing instructions configured to be executed by one or more processors of a system, wherein execution of the instructions by the one or more processors causes the system to implement the disclosed steps).
Regarding claim 11: Dhir as modified teaches The system of Claim 10 wherein the enricher utilizes at least one of: machine learning, algorithms and artificial intelligence (AI) models, to transform the at least one dataset into the enriched dataset (Dhir, [0075] and [0093-0094], where the system receives preprocessed data sets and generates, based on the preprocessed data sets, an enriched data set by applying at least one entity matching technique and/or statistical matching technique, where the similarity between entities may be determined by inputting corresponding features of the first entity and first corresponding entity into one or more artificial intelligence or machine learning models).
Regarding claim 12: Claim 12 recites substantially the same claim limitations as claim 2, and is rejected for the same reasons.
Regarding claim 13: Claim 13 recites substantially the same claim limitations as claim 3, and is rejected for the same reasons.
Regarding claim 14: Claim 14 recites substantially the same claim limitations as claim 4, and is rejected for the same reasons.
Regarding claim 15: Claim 15 recites substantially the same claim limitations as claim 5, and is rejected for the same reasons.
Regarding claim 16: Claim 16 recites substantially the same claim limitations as claim 6, and is rejected for the same reasons.
Regarding claim 18: Claim 18 recites substantially the same claim limitations as claim 8, and is rejected for the same reasons.
Regarding claim 19: Claim 19 recites substantially the same claim limitations as claim 9, and is rejected for the same reasons.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dhir et al. (“Dhir”) (US 2022/0342912 A1), in view of Bahatyrevich et al. (“Bahatyrevich”) (US 11,755,344 B1), in further view of Liu et al. (“Liu”) (US 2020/0183035 A1).
Regarding claim 7: Dhir as modified teaches The method of Claim 6, but does not appear to explicitly teach wherein the variable buildable enricher is built on the platform using models trained by the user with a specific uploaded dataset.
Liu teaches wherein the variable buildable enricher is built on the platform using models trained by the user with a specific uploaded dataset (Liu, [0069] and [0075], where the augmentation methods and/or various transformations are selected to be utilized using training and/or test datasets, e.g., the ML system utilizing a reinforcement learning approach to learn augmentation methods and/or transformations that are plausible, and train an ML system to learn augmentation methods and transformations. See Dhir, [0087-0088], where the data sets were uploaded).
Although Liu does not appear to explicitly state that the training is performed “by the user” as claimed, one of ordinary skill in the art would have found it obvious to have modified Liu to be utilized with user feedback with the motivation of potentially improving data augmentation accuracy (e.g., as users may provide better training feedback/inputs).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Dhir as modified and Liu with the motivation of ensuring the relevancy of the data enrichment/augmentation (see, e.g., Liu, [0010], where some of the data used in data augmentation may not be plausible), and thus producing enriched data that is highly relevant and therefore useful.
Regarding claim 17: Claim 17 recites substantially the same claim limitations as claim 7, and is rejected for the same reasons.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IRENE BAKER whose telephone number is (408)918-7601. The examiner can normally be reached M-F 8-5PM PT.
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/IRENE BAKER/Primary Examiner, Art Unit 2152
6 January 2026
1 See, e.g., Bascom Global Internet Services, Inc. v. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016) at p. 12 (“…filtering content is an abstract idea because it is a long-standing, well-known method of organizing human behavior…”).