DETAILED ACTION
This communication is in response to the Amendments and Arguments filed on 4/21/2026. Claims 1, 4-8, 11-15, and 18-20 are pending and have been examined. Hence, this Action has been made FINAL.
Any previous objection/rejection not mentioned in this Office Action has been withdrawn by the examiner.
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
With respect to claim objections, Claims 2, 9, and 16 have been cancelled thus the objection is considered moot and has been withdrawn.
With respect to the 35 U.S.C. 101 rejections for claims 1, 4-8, 11-15, and 18-20, the applicant asserts that the amended claims now explicitly require that the NLP algorithms "automatically extract structured contextual artifacts," which represents a specific technical operation performed by the processing device, not a mental process that a human could perform with pen and paper.
Examiner respectfully disagrees, there no support for “structured contextual artifacts” in the specification and thus under broadest reasonable interpretation this could represent any contextual information taken from natural language. The human mind is capable of identifying such context and structuring it in a variety of different ways.
The applicant further asserts that claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRI Int'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019). The amended claims establish technical interdependence between NLP processing and UI generation. The amended claims now recite "programmatically determine one or more sources of network data based on at least the structured contextual artifacts" as recited by claims 1, 8, and 15. This establishes a direct technical link between the computational NLP output and the subsequent data source determination. The amended claims further recite "programmatically generating one or more designated data slots, wherein each designated data slot of the one or more designated data slots corresponds to a respective source of network data identified based on the structured contextual artifacts" as recited by claims 1, 8, and 15. This creates a technical interdependence where the computational output of the NLP algorithms directly and automatically determines the configuration of the user interface. As described in the specification, "[b]y identifying specific sources of network data based on information associated with the first user interface, the system may focus on analyzing the most accurate information for presentment." As-Filed Specification, paragraph [0056]. This demonstrates that the system performs a technical function of automatically selecting relevant data sources based on computational analysis. The claims further recite "dynamically map the information summaries for the information associated with the one or more sources of network data with the one or more designated data slots based on matching a source of network data from which each information summary was generated to the respective source of network data corresponding to each designated data slot" as recited by claims 1, 8, and 15. This dynamic mapping based on computationally-derived artifacts represents a technical process that cannot be performed mentally.
Examiner respectfully disagrees, the word “programmatically” is merely describing that the mental process is being applied via a computing device, not separating the process from a mental process. The human mind is capable of determining a source of a piece of data using context found within data such as an author, title, or publication. The human mind is capable assigning data slots based on a source, for example, creating a document that summarizes today’s news and has specific areas of the document for each news source. The human mind can analyze an interface (any visual presentation) and analyze information found in it based on source, for example, reading articles from specific authors in a newspaper. Finally, the human mind is capable of mapping summaries to the source of the information. None of these aspects are inherently technical and are merely being applied via generic computing components.
The applicant further asserts that the amended claims are integrated into a practical application. The amended claims recite a specific technical implementation where NLP algorithms executing on a processing device automatically extract structured contextual artifacts that programmatically determine both: (1) which network data sources to query, and (2) the slot configuration of the generated dashboard. As described in the specification, "[i]n displaying the information, the system may generate a personalized dashboard for the user based on at least the one or more sources of network data. In one aspect, the dashboard may include data slots for the identified sources of network data." As- Filed Specification, paragraph [0058]. The claims integrate the abstract idea into a practical application by using the computational NLP output to automatically configure a technical user interface structure.
Examiner respectfully disagrees, the dashboard is merely a post-solution step of presenting the result of the method to the user. The process of determining a data source and assigning data slots based on the source is a mental process where the NLP algorithms are performing the task the human mind would perform. In order for the device to integrate the claims there would need to be steps the human mind cannot reasonably perform or a specific device design that is not considered generic or general purpose.
The applicant further asserts that the specification further describes that "[b]y selectively choosing sources of network data, the present disclosure captures and extracts data from the most applicable sources of network data and provides accurate insights and intelligence using efficient visualization techniques. In doing so, the present disclosure reduces the amount of computing resources, such as processing resources, storage resources, network resources, and/or the like, that are being used." As-Filed Specification, paragraph [0030]. The claimed technical solution provides a concrete improvement to computer functionality by reducing computing resource usage through selective data source identification. The Federal Circuit has distinguished between claims that are "directed to" a judicial exception and those that are not, e.g., claims that improve the functioning of a computer or other technology or technological field. See MPEP § 2106.06(b). The Examiner's analogy to a human creating a poster board fails because the amended claims require that each designated data slot "corresponds to a respective source of network data identified based on the structured contextual artifacts" that are "automatically extract[ed]" by NLP algorithms executing on a processing device. A human cannot mentally execute NLP algorithms to automatically extract structured contextual artifacts that then programmatically determine a slot configuration.
Examiner respectfully disagrees, an improvement in efficiency and reduction in computing resources is not explicit within the claim language as extracting context and gathering/organizing information based on the context does not state or imply that the data gathering would be faster. Furthermore, the claims ultimately reflect an improvement to a mental process of data gathering and presentment rather a technical one. The term “network data” does not inherently mean a human mind couldn’t perform it as information from a network can contain a physical copy which can be analyzed without a computer. The NLP algorithms are not something a human mind cannot inherently perform as language processing is a broad concept with many meaning and humans are capable of performing the language processing tasks presented in the claims.
Therefore, the applicant’s arguments are not persuasive.
With respect to the 35 U.S.C. 103 rejections for claims 1, 4-6, 8, 11-13, 15, and 18-20, the applicant asserts that, regarding Ferucci, this cluster-based organization with links to evidence passages does not teach programmatically generating designated data slots that each correspond to a respective source of network data, nor does it teach dynamically mapping summaries to slots based on matching the source from which each summary was generated to the source corresponding to each designated data slot. Regarding Gross, this keyword extraction for search query suggestion does not teach the claimed generation of designated data slots corresponding to respective sources of network data, nor the dynamic mapping of summaries to slots based on source matching. Regarding Neervannan, this side-by-side comparison of company documents is fundamentally different from programmatically generating designated data slots based on structured contextual artifacts and dynamically mapping summaries to those slots based on matching the source from which each summary was generated to the source corresponding to each designated data slot. Regarding Soubbotin, this is a unified text summary with a separate source list, not designated data slots where each slot corresponds to a respective source of network data and where summaries are dynamically mapped to slots based on source- to-source matching.
Examiner respectfully disagrees, Ferucci is meant to teach the overall process of receiving input, extracting context from the input, determining data sources from the context, retrieving the data, summarizing it, and displaying it on a dashboard to a user. As currently claimed, this represents most major operations performed in the method and further references are merely meant to show that the minor differences exist in different, yet closely related inventions. Soubbotin is meant to teach the difference in how data is presented including the language from the amended claims. Soubboutin teaches a system which summarizes references from a search (Col. 22, Lines 1-26), organizes them based on relevance which is considered dynamically based on context information (Col. 21, Lines 29-32), and presents them in designated slots that correspond to the source of the network data (Fig. 2). These references present closely related technological improvements that would be obvious to combine. The current claim language is taught in entirety by the references and would require further amendment to distinguish itself from the references.
The applicant further asserts that the amended claims require a specific technical integration where: (1) NLP algorithm automatically extract structured contextual artifacts comprising n-grams, noun phrases, themes, or facets; (2) sources of network data are programmatically determined based on these artifacts; (3) designated data slots are programmatically generated where each slot corresponds to a respective source identified based on the artifacts; and (4) information summaries are dynamically mapped to slots based on matching the source from which each summary was generated to the source corresponding to each designated data slot. None of the cited references, alone or in combination, teaches this integrated approach where the NLP-extracted structured contextual artifacts drive both the identification of network data sources and the generation of corresponding data slots, with summaries then being mapped to their respective slots based on source-to-source matching."
Examiner respectfully disagrees, there is no support from the specification to suggest that the “structured contextual artifacts” are anything other than general context information which is already taught by Ferucci (Col. 4, Lines 36-63) and Neervannan (Col. 10, Lines 32-36). Ferucci already teaches finding sources of network data from the context (Col. 9, Lines 44-67) and terms such as “programmatically” and “artifacts” do not change the meaning of the previously presented claim. Generating data slots corresponding to respective sources with associated summaries is taught by Soubboutin (Fig. 2) and the changes to the claim language did not alter the claim scope enough to separate the teachings.
Therefore, the applicant’s arguments are not persuasive.
Claim Rejections – 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4-8, 11-15, and 18-20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The amended claim language for claims 1, 8, and 15 states “structured contextual artifacts” which has no support within the specification. The amendment replaced the broad terminology “contextual information” which is the definition that will still be used for claim interpretation reasons below. Paragraphs 53-56 of the specification describe this aspect more broadly as “to determine contextual information associated with independent terms from the information associated with the first user interface in a specific context.” With no mention of a specific structure or the term “artifact”.
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 1-2, 4-9, 11-16, and 18-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8, and 15 recite A system for context-based data aggregation and presentment, the system comprising: a processing device; a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to: receive, from [a user input device], a user input triggering data aggregation and presentment from a first user interface, wherein the user input comprises selection of an interaction feature presented on the first user interface, wherein the interaction feature comprises a swipe-up gesture; initiate semantic parsing of information associated with the first user interface using natural language processing algorithms, wherein the natural language processing algorithms execute on the processing device to automatically extract structured contextual artifacts from the information associated with the first user interface; capture contextual information associated with the first user interface based on at least the semantic parsing, wherein structured contextual artifacts comprise at least one of n-grams, noun phrases, themes, or facets; programmatically determine one or more sources of network data based on at least the structured contextual artifacts; retrieve information from the one or more sources of network data; initiate extractive summarization of information associated with the one or more sources of network data using the natural language processing algorithms; generate information summaries based on at least the extractive summarization; and display, via [a second user interface], the information summaries on the user input device, wherein displaying further comprises: generate a personalized dashboard for the user based on at least the one or more sources of network data, wherein generating the personalized dashboard comprises programmatically generating one or more designated data slots wherein each designated data slot of the one or more designated data slots corresponds to a respective source of network data identified based on the structured contextual artifacts; and dynamically map the information summaries for the information associated with the one or more sources of network data with the one or more designated data slots based on matching a source of network data from which each information summary was generated to the respective source of network data corresponding to each designated data slot.
Each step in these claims, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. A human is capable of receiving a gesture as confirmation to start a task, for example, giving a thumbs up when you’re ready. A human can semantically parse information and capture contextual meaning from it, this is reading comprehension and is done whenever a human reads a new piece of information. Reading comprehension can include things such as identifying n-grams (specific words), noun phrases, themes and facets. Natural language processing algorithms are mathematical equations which a human could perform. Determining sources of network data could be, for example, a human looking for a relevant book in a library or even looking through printed out website pages to find relevant information. Retrieving information from these sources could be done by writing/drawing a copy a of the information you want. The human mind is capable of comprehending text they have read and summarizing it. The displaying of information can also be done by a human using a writing utensil and paper. A human can create a personalized dashboard for presenting sources of network data. For example, someone doing a research presentation may create a poster board to present their ideas. The poster board would contain various sources of research they found and summaries for them. The poster board would have designated areas for each source of data that correspond to where the research came from and pieces from the initial question that correspond to that information. The poster could also have fold-out components that dynamically reveal summaries for the information. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claims 1, 8, and 15 recite a user input device with a user interface and a display device with a second user interface. The input device containing a user interface is necessary to gather data that the system will act upon, thus it is extra pre-solution activity and does not integrate the judicial exception. To this same extent the swipe-up gesture would be considered part of the pre-solution activity done on this user interface. Furthermore, there are generic examples of the types of equipment used provided in the specification in paragraph 22. The display device that could contain the second user interface is necessary to present the data the system produces; thus, it is extra post-solution activity and does not integrate the judicial exception. Furthermore, there are generic examples of the types of equipment used provided in the specification in paragraph 34. Claim 1 specifically lists additional components of a processing device and a non-transitory storage device. Generic examples of the processor and storage device are listed in the specification in paragraphs 38 and 40 respectively. Each of these additional components amount to general purpose computing devices. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because it does not impose any meaningful limits on practicing the abstract idea. The claims are not patent eligible.
Claims 4, 11, and 18 recite receiving from the user input device, a user interaction input associated with a first information summary, wherein the first information summary is associated with a first data source; and generate a third user interface to be overlaid on the second user interface to display a first information associated with the first data source, wherein the first information is an unabridged version of the first information summary.
The limitations in these claims, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. An “unabridged version of the first information summary” is being interpreted as providing the original source material without the summarization. A human could include both a summary they created and a copy of the original material on a note sheet for example. Furthermore, the generation of a third user interface could be the human equivalent of having another page of notes or even unfolding a piece of paper to reveal more information. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. As mentioned previously the recitation of “user input device” and “user interface” amount to general-purpose computing devices. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 5, 12 and 19 recite the one or more sources of network data are associated with the user.
The limitation in these claims, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. A human can select sources of information based on their own preferences/history. For example, choosing to use an article from a writer they enjoy/trust. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not include additional elements that weren’t in the independent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 6, 13, and 20 recite receiving from the user input device, a user customization input selecting a first subset of the one or more sources of network data; and update the second user interface to display the information from the first subset of the one or more sources of network data based on at least the user customization input.
The limitation in these claims, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. A human can customize how they want a collection of information gathered to look when being displayed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. As mentioned previously the recitation of “a user interface” amount to general purpose computing devices. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 7 and 14 recite deploying using a machine learning (ML) subsystem, a trained ML model on the information from the one or more sources of network data; generate predictive analytics for the user based on at least the information from the one or more sources of network data using the trained ML model; and display the predictive analytics for the user on the second user interface.
Each step in these claims, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. A human can perform analysis and predictive analytics on a piece of information. For example, if looking at a line graph a human could see the current upward/downward trend and deduce that it will continue in that direction. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claims 7 and 14 recite a “machine learning subsystem” and a “machine learning model” which as described in paragraphs 61-64 of the specification are generic implementations of these models with no specific construction. As mentioned previously the recitation of “a user interface” amounts to a general-purpose computing device. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4-6, 8-9, 11-13, 15-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication 11803401 B1 (Ferrucci et al.) in view of US Patent Application Publication 20160360382 A1 (Gross et al.), US Patent Publication 11740770 B1 (Neervannan et al.), and US Patent Publication 10783192 B1 (Soubbotin).
Regarding Claims 1, 8, and 15, Ferrucci et al. teaches a system for context-based data aggregation and presentment, the system comprising: a processing device (Col. 17, lines 20-26); a non-transitory storage device containing instructions (Col. 17, lines 27-47).
Claim 8 states A computer program product for context-based data aggregation and presentment, the computer program product comprising a non-transitory computer-readable medium comprising code (Col. 17, lines 27-47).
Claim 15 states A method for context-based data aggregation and presentment (Col. 53, lines 32-48).
Receive, from a user input device, a user input triggering data aggregation and presentment from a first user interface.
Ferrucci et al. includes various (UI Components) to receive inputs from a user through their device. (In some examples, the research assistant UI component 112 may receive user input for specifying an input query and call the query component 114 to process the input query.) (Col. 10, lines 29-45). “Triggering data aggregation and presentment” is being interpreted as beginning to process the input which is equivalent to (process the input query) in the quote from above. Furthermore, (Fig. 10, also referred to as Example User Interface 124 in Fig. 1) Can be mapped to the “first user interface” with element 1006 (As depicted, the example user interface 1002 is has been researching concepts related to “Syndrome A.” The example user interface element 1006 is highlighting one of the proposition nodes.) (Col. 36, lines 46-55) where (1006) contains information from the user input.
Initiate semantic parsing of information associated with the first user interface using natural language processing algorithms,
Ferrucci et al. teaches information associated with the first user interface (input query) undergoing semantic parsing (In some examples, the query component may receive an input query that includes a natural language question and use a semantic parser to convert the natural language question to a structured question. The semantic parser may parse the text of the natural language question and convert the text into machine language (e.g., structured representation), which is a machine-understandable representation of the meaning of the text.) (Col. 4, lines 36-63). It can also be seen in (Fig. 1) that the (Query Component) and (Natural Language Understanding Engine) are in the system together. In regards to the claim mentioning a plurality of algorithms Ferrucci et al. also utilize various (engines) that are detailed further at (Col. 9, lines 3-19).
wherein the natural language processing algorithms execute on the processing device to automatically extract structured contextual artifacts from the information associated with the first user interface;
(In some examples, the query component may receive an input query that includes a natural language question and use a semantic parser to convert the natural language question to a structured question. The semantic parser may parse the text of the natural language question and convert the text into machine language (e.g., structured representation), which is a machine-understandable representation of the meaning of the text.) (Col. 4, lines 36-63).
Fig. 2, shows that the semantic parser and NLU engine are part of the processing device. The structured contextual artifacts are represented by the extracted meaning represented in a machine readable format.
Capture contextual information associated with the first user interface based on at least the semantic parsing.
As mentioned previously the semantic parsing is interpreting meaning which can be considered contextual information. (The semantic parser may parse the text of the natural language question and convert the text into machine language (e.g., structured representation), which is a machine-understandable representation of the meaning of the text) (Col. 4, lines 36-63).
Programmatically determine one or more sources of network data based on at least the structured contextual artifacts;
Network data is being interpreted as any source of data the system is pulling from. The (query component) mentioned previously contains contextual information. (The query component 210 may receive an input query and perform a search based on the input query.) (Col. 19, lines 35-46). The search is the equivalent to the determination of network data as can be further explained (As described herein, the research process may include a series of research steps, including, but not limited to: receiving a research topic as an input query, searching for documents/text related to the input query (i.e., “information”), parsing the evidence documents/text to understand the information...) (Col. 9, lines 44-67). “One or more sources of network data” can be further exhibited by (non-limiting examples of the knowledge sources may include unstructured, semi-structured, and structured knowledge (e.g., medical ontologies, knowledge graphs, research papers, clinical studies, etc.).) (Col. 3, lines 42-51).
Retrieve information from the one or more sources of network data.
The section referenced previously (Col. 9, lines 44-67) showing the overall process of Ferrucci et al. shows steps such as (parsing the evidence documents/text to understand the information) and (linking the evidence together to find logical reasoning to support research results) that could not be done without first retrieving said information.
initiate extractive summarization of information associated with the one or more sources of network data using the natural language processing algorithms.
Ferrucci et al. uses natural language understanding algorithms on the “network data” or query result (The NLU engine 116 may apply a multi-dimensional interpretation process with a domain-independent interpretation schema to analyze the query results.) (Col. 12, lines 19-28). Then uses a knowledge aggregation and synthesis engine to summarize data (The knowledge aggregation and synthesis engine 118 may apply clustering and similarity algorithms to aggregate information in the interpreted query results.) (Col. 12 line 55 to Col. 13 line 12).
Generate information summaries for the information associated with the one or more sources of network data based on at least the extractive summarization.
Ferrucci et al. teaches an evidence summary component for generating information summaries (The evidence summary component 122 may process the ranked aggregate results with the evidence texts. The evidence summary component 122 may process the ranked aggregate results with the evidence texts to generate results data, including one or more result clusters annotated with the related portion of evidence texts. The one or more result clusters include at least one concept cluster, a relation cluster, and a propositional cluster. Each cluster of the one or more result clusters annotated with the related portion of evidence texts includes a link to a summarized evidence passage.) (Col. 13, lines 42-58).
Display, via a second user interface, the information from the one or more sources of network data on the user input device.
Ferrucci et al. states (The research assistant UI component 112 may generate a user interface to guide user input to enter the query and explore the evidence chains. In some examples, the research assistant UI component 112 may configure the user interface to guide the user input and repeat the research process by iteratively exploring evidentiary chains to connect the dots through a large body of knowledge (“data sources”), including natural language text (e.g., journals, literature, documents, knowledge base, market research documents, and/or structured databases).) (Col. 10, lines 1-28). This shows “network data” being displayed on a user interface. While this system uses multiple user interfaces and transitions between them (Figs. 6-20), Fig. 10 specifically shows (The example user interface 1002 may include a visual presentation of query graph that is generated in response to a research, and the nodes of the graph include propositions constructed from combined evidence links from previous research (e.g., from research process illustrated in FIG. 9) with selectable nodes to explore the supporting evidence associated with the node) (Col. 36, lines 36-42). Where the second user interface (1002) contains information from sources of network data (query graph).
wherein displaying further comprises: generate a personalized dashboard for the user based on at least the one or more sources of network data,
Ferrucci et al. teaches building a query graph with the search results (The query component 114 may generate a query graph (“research results graph”) to store search results (“findings”) for an iterative exploration of the input query.) (Col. 10, lines 46-64). The query graph is then visually represented to the user based on the inputs they initially provided making it personalized (The example user interface 1002 may include a visual presentation of query graph that is generated in response to a research, and the nodes of the graph include propositions constructed from combined evidence links from previous research (e.g., from research process illustrated in FIG. 9) with selectable nodes to explore the supporting evidence associated with the node.) (Col. 36, lines 24-55).
Ferrucci et al. does not explicitly teach: wherein the user input comprises selection of an interaction feature presented on the first user interface, wherein the interaction feature comprises a swipe-up gesture; wherein the structured contextual artifacts comprise at least one of n-grams, noun phrases, themes, or facets; wherein generating the personalized dashboard comprises programmatically generating one or more designated data slots, wherein each designated data slot of the one or more designated data slots corresponds to a respective source of network data identified based on the structured contextual artifacts; and dynamically map the information summaries for the information associated with the one or more sources of network data with the one or more designated data slots based on matching a source of network data from which each information summary was generated to the respective source of network data corresponding to each designated data slot.
However, Gross et al. teaches a wherein the user input comprises selection of an interaction feature presented on the first user interface,
(The method further includes: detecting, via the touch-sensitive surface, a swipe gesture that, when detected, causes the electronic device to enter a search mode that is distinct from the application. The method also includes: in response to detecting the swipe gesture, entering the search mode, the search mode including a search interface that is displayed on the display. In conjunction with entering the search mode, the method includes: determining at least one suggested search query based at least in part on information associated with the content.) (Paragraph 126)
Gross et al. teaches a display that when interacted with by the user, causes search, summarization, and display methods to occur.
wherein the interaction feature comprises a swipe-up gesture;
(detecting, via the touch-sensitive surface, a swipe gesture that, when detected, causes the electronic device to enter a search mode that is distinct from the application.) (Paragraph 126).
The user interaction is a swipe gesture.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the search/summarization system as taught by Ferrucci et al. to include an interaction feature that initiates the method as taught by Gross et al. This would have been an obvious improvement as it is an intuitive way to interact with a device that is effective and efficient for the user’s satisfaction. (Gross et al. Paragraph 125).
Ferrucci et al. in view of Gross et al. does not explicitly teach: wherein the structured contextual artifacts comprise at least one of n-grams, noun phrases, themes, or facets; wherein generating the personalized dashboard comprises programmatically generating one or more designated data slots, wherein each designated data slot of the one or more designated data slots corresponds to a respective source of network data identified based on the structured contextual artifacts; and dynamically map the information summaries for the information associated with the one or more sources of network data with the one or more designated data slots based on matching a source of network data from which each information summary was generated to the respective source of network data corresponding to each designated data slot.
However, Neervannan et al. teaches a wherein the structured contextual artifacts comprise at least one of n-grams, noun phrases, themes, or facets;
(FIG. 2 illustrates an overview of the deep search process 50. In the process, the search system receives feeds, that may be real-time, of pieces of content (52) … The linguistic unit also discerns the topic of the content using special linguistic rules which is different from traditional search engines where a search is performed using word and phrases without contextual understanding of the text. For example, the linguistic analysis unit tags sentences based on their tense, to determine whether they talk about something that happened in the past, is continuing, or is expected to happen in the future. This is accomplished through a combination of linguistic analysis and domain-based language models that understand, for example, that a noun phrase like “deferred expenses” implies something about the future.) (Col. 4, Line 53 to Col. 5, Line 19).
(Feature extraction: Terms, phrases, or co-occurring words that are judged to be relevant from the point of view of sentiment classification are selected by a domain expert according to the approaches described in above. Another alternative is using n-grams or a combination of features.) (Col. 10, Lines 32-36)
Neervannan et al. teaches a method of rendering context-based information that can learn context from text based on things like noun-phrases and n-grams.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the search/summarization system as taught by Ferrucci et al. to include using noun phrases and n-grams to identify context as taught by Neervannan et al. This would have been an obvious improvement adding context analysis to a search system can help retrieve more results that were not specifically stated in a query (Neervannan et al. Col. 1, Lines 50-67).
Ferrucci et al. in view of Gross et al. and Neervannan et al. does not explicitly teach: wherein generating the personalized dashboard comprises programmatically generating one or more designated data slots, wherein each designated data slot of the one or more designated data slots corresponds to a respective source of network data identified based on the structured contextual artifacts; and dynamically map the information summaries for the information associated with the one or more sources of network data with the one or more designated data slots based on matching a source of network data from which each information summary was generated to the respective source of network data corresponding to each designated data slot.
However, Soubbotin teaches a wherein generating the personalized dashboard comprises programmatically generating one or more designated data slots, wherein each designated data slot of the one or more designated data slots corresponds to a respective source of network data identified based on the structured contextual artifacts;
(In step 430 the multi-document summarization engine receives the query and the links to the documents. Then, the multi-document summarization engine parses each individual document to extract key semantic concepts and corresponding text fragments or sentences and performs multi-document summarization of the documents based on the extracted concepts, producing a text summary representing a digest of the search results. The summary may comprise key semantic concepts, text fragments or sentences matching these concepts, or a combination of the two. The user query may be taken into account, to focus the summary on a particular topic using means such as, but not limited to, NLP or metadata to help determine relevance of the results. … A relevance score may be calculated for each concept or text fragment, and this relevance score may or may not be displayed to the user in the summary and may be used by the system for learning.) (Col. 21, Line 65 to Col. 22, Line 26).
(Furthermore, some embodiments of the present invention may specify how to order the text fragments, for example, without limitation, alphabetically by source, by relevance, etc.) (Col. 21, Lines 29-32).
Soubbotin summarizes multiple sources of network data in response to a user query and displays them on a second user interface. Fig. 1 shows a first user interface (a typical search engine interface) with a button to “Get text summary of results” which transitions it to the second user interface seen in Fig. 2. This is a personalized dashboard in the sense that it is generated based on the context of the query and each results detected relevance to it. There are designated slots for each summarized source of network data as well as links to the websites.
and dynamically map the information summaries for the information associated with the one or more sources of network data with the one or more designated data slots based on matching a source of network data from which each information summary was generated to the respective source of network data corresponding to each designated data slot.
(The sentences or text fragments within summary 200 originated from various sources, which are listed below the summary in a source list 205. The sources in source list 205 refer to the documents or Web pages found by the search engine as displayed in a conventional search result, shown by way of example in FIG. 1.) (Col. 22, Lines 14-19).
(The summary may comprise key semantic concepts, text fragments or sentences matching these concepts, or a combination of the two. The user query may be taken into account, to focus the summary on a particular topic using means such as, but not limited to, NLP or metadata to help determine relevance of the results. … A relevance score may be calculated for each concept or text fragment, and this relevance score may or may not be displayed to the user in the summary and may be used by the system for learning.) (Col. 21, Line 65 to Col. 22, Line 26).
(Furthermore, some embodiments of the present invention may specify how to order the text fragments, for example, without limitation, alphabetically by source, by relevance, etc.) (Col. 21, Lines 29-32).
The order that the text fragments are arranged in the text summary can be based on a detected relevance to the initial query. Thus, the summary displayed in Fig. 2 shows dynamically mapped data slots that are associated with information summaries and network data determined from contextual information (as can be seen in the associated links in Fig. 2). Each sentence and corresponding link within the summary correspond to a different piece of network information.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the search/summarization system as taught by Ferrucci et al. to include a dynamically mapped user interface with data slots for each summary as taught by Soubbotin This would have been an obvious improvement as search summaries and/or results that consider context of the user/query may provide more accurate search summaries and/or results for the user. (Soubbotin Col. 1, Lines 58-64).
Regarding Claims 4, 11, and 18, Ferrucci et al. in view of Gross et al., Neervannan et al., and Soubbotin teaches the system of claims 1, 8, and 15,
Furthermore, Ferrucci et al. teaches wherein the system is further configured to: receive, from the user input device, a user interaction input associated with a first information summary, wherein the first information summary is associated with a first data source.
A user selecting a summary on the user interface can be seen in Ferrucci et al. in (Fig. 10) and further described as (In response to selecting the example user interface element 1006, the research assistant UI component 208 may generate the example user interface element 1004.) (Col. 36, lines 43-45).
and generate a third user interface to be overlaid on the second user interface to display a first information associated with the first data source, wherein the first information is an unabridged version of the first information summary.
The generating of the third user interface can be seen in the previously mentioned quote and Fig. 10 where (1004) represents the third user interface and (1002) represents the second user interface in which the third user interface is overlaid on. The “unabridged version” is represented in 1004 with evidence links (The example user interface element 1006 is highlighting one of the proposition nodes. The example user interface element 1004 allows user input to explore support evidence for the evidence links used to generate the proposition “Syndrome A has symptom dry eyes caused by lacrimal gland inflammation.”) (Col. 36, lines 46-55). Furthermore, Ferrucci et al. teaches providing “unabridged” material in the evidence summary component (The evidence summary component 234 may provide citations and links to the evidence texts.) (Col. 25 line 63 to Col. 26 line 21).
Regarding Claims 5, 12 and 19, Ferrucci et al. in view of Gross et al., Neervannan et al., and Soubbotin teaches the system of claims 1, 8, and 15,
Furthermore, Ferrucci et al. teaches wherein the one or more sources of network data are associated with the user.
Ferrucci et al. teaches multiple ways that the user can affect the data provided by the system, one being (Additionally, as described herein, the present system may request user feedback (e.g., thumbs up or thumbs down) for supporting/refuting evidence for a proposition. The system can use this feedback to (1) dynamically re-rank the list of evidence passages and provide immediate visual feedback by removing the evidence passage with negative feedback and up-ranking the evidence passage with positive feedback;) (Col. 8 line 38 to Col. 9 line 2).
Regarding Claims 6, 13, and 20, Ferrucci et al. in view of Gross et al., Neervannan et al., and Soubbotin teaches the system of claims 1, 8, and 15,
Furthermore, Ferrucci et al. teaches wherein the system is further configured to: receive, from the user input device, a user customization input selecting a first subset of the one or more sources of network data.
Fig. 7 shows an example user interface receiving input from the user on multiple sources of data. (The example user interface 702 presents the example user interface element 704, which includes an exploration window to allow user input to explore relations or concepts relative to the specific concept “Syndrome A.”) and (As depicted, based on user input, “Syndrome A” has the relation link “has symptoms” relative to the concepts: “Dry eyes,” “Nocturnal cough,” and “Dry mouth.” The user has selected those three concepts for further exploration.) (Col. 34 line 54 to Col. 35 line 8).
And update the second user interface to display the information from the first subset of the one or more sources of network data based on at least the user customization input.
Fig. 7 shows a user interface being updated based on user input (The research assistant UI component 208 may generate the example user interface 802 to continue guiding user input to enter the query following the examples illustrated in FIG. 7. As depicted, following the example in FIG. 7, the user has added an additional relation “manifest as” and an additional concept “parotid gland enlargement.”) (Col. 34 line 54 to Col. 35 line 8). Then Fig. 8 shows an example of how the user interface of Fig. 7 can generate new interface options based on the input received (As described herein, an input query may include a search schema that specifies a causal schema. The causal schema may trigger automatic repeat searches for a causal pathway from a starting point (“source concept”) and connected to an ending point (“target concept”).) (Col. 35, lines 9-58).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication 11803401 B1 (Ferrucci et al.) in view of in view of US Patent Application Publication 20160360382 A1 (Gross et al.), US Patent Publication 11740770 B1 (Neervannan et al.), US Patent Publication 10783192 B1 (Soubbotin). and further in view of US Patent Publication 10963518 B2 (Aggour et al.).
Regarding Claims 7 and 14 Ferrucci et al. in view of Gross et al., Neervannan et al., and Soubbotin teaches the system of claims 1 and 8.
Gross et al., Neervannan et al., and Soubbotin does not explicitly teach: wherein the system is further configured to: deploy, using a machine learning (ML) subsystem, a trained ML model on the information from the one or more sources of network data; generate predictive analytics for the user based on at least the information from the one or more sources of network data using the trained ML model; and display the predictive analytics for the user on the second user interface.
However, Aggour et al. teaches a query system is further configured to: deploy, using a machine learning (ML) subsystem, a trained ML model on the information from the one or more sources of network data;
(In accordance with implementations, scalable analytic execution layer 116 can optionally apply machine learning and artificial intelligence techniques to the query results, step 325. These techniques identify data correlations responsive to the consumer's query details.) (Col. 8, lines 18-30). In this reference the (query results) are being mapped to “network data.”
generate predictive analytics for the user based on at least the information from the one or more sources of network data using the trained ML model;
“Predictive analytics” are being mapped to (identifying data correlations) and (visualizations of the raw data or analytic results can be generated) as any task being performed by a machine learning algorithm could be considered “predictive”.
and display the predictive analytics for the user on the second user interface.
(Visualizations of the raw data or analytic results can be generated, step 330. The visualizations of raw data and/or analytic results, or the raw data and/or analytic results in native format (e.g., relational data, time series data, images, document, etc.) can be presented to the data consumer, step 335.) (Col. 8, lines 18-30).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the search/summarization system as taught by Gross et al., Neervannan et al., and Soubbotin to extract information from query results using machine learning as taught by Aggour et al. This would have been an obvious improvement to efficiently and scalably provide data relationships and rapid analytics to the user as described by Aggour et al. (Col. 6 line 65 to Col. 7 line 10).
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/NICHOLAS D LOWEN/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653 07/10/2026
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