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 .
Claims 4 and 20 have been canceled by the applicant on 03/09/2026.
Response to Arguments
101 Rejections:
With respect to Applicant’s argument that “Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are ostensibly directed to an abstract idea without significantly more. Applicant submits the claims are not directed to an abstract idea and, further, are directed to a technological solution to a technological problem. As described in the Application "the technology described herein enables usage of a reduced amount of computing resources as it more specifically analyzes aspects of interest to a user (e.g., via elicited user feedback). I…….. Further, to propose inquiries to elicit the user's analysis intention both informatively and efficiently, inquiry optimization (e.g., Pareto Frontier Optimization) can be used to ensure necessary information is gathered with a minimum number of inquiries proposed to the user. In this way, inquiry optimization facilitates a more efficient reduction of the number of candidate data stories, thereby reducing computer resource utilization needed to analyze candidate data stories for providing as a data story recommendation. Enabling elicitation of a minimum number of inquiries further decreases computing resource utilization required to present inquiries and process user feedback." Application, Para. [0028]. Accordingly, Applicant requests withdrawal of the 35 U.S.C. 101 rejection”, Examiner respectfully disagrees.
Examiner cites that the claims remain directed to the abstract idea of analyzing information, eliciting user feedback, calculating expected values, selecting a recommendation, and displaying the recommendation. In particular, claim 1 recites generating candidate data stories, determining a data story recommendation based on user feedback, selecting inquiries based on potential reductions of candidate data stories, generating expected values for candidate inquiries, determining an expected value based on an average of data story number sizes, and providing the recommendation for display.
These limitations “generating a set of data stories”, “determining,…..potential responses” recite mental processes because they involve evaluation, judgment, selection, and recommendation of information. The limitation “providing the recommendation for display” is an additional element.
Applicant’s alleged improvement is not an improvement to computer technology itself. Rather, the alleged improvement is to the abstract process of selecting which inquiries to present and narrowing candidate data stories. Reducing the number of options considered or reducing the number of questions asked may make the recommendation process more efficient, but this is an improvement to the abstract decision-making process itself, not a technological improvement to the operation of a computer, database, display, or network.
The claims do not recite a specific technological mechanism that improves computer functionality. Instead, the claims use generic computer components, such as computer storage media, processors, and display functionality, merely as tools to perform the abstract idea.
Applicant’s reliance on reduced computing resources is also not persuasive because the reduction allegedly results from performing fewer abstract evaluations or presenting fewer inquiries. The claims do not show that the computer itself operates differently or more efficiently in a technical sense. Rather, the claims reduce the amount of information to be analyzed by applying the claimed expected-value and averaging logic. Such optimization of an abstract analysis does not integrate the abstract idea into a practical application.
Accordingly, the claims do not integrate the abstract idea into a practical application under Step 2A, Prong Two. The additional elements, individually and in combination, amount to no more than applying the abstract idea using generic computer components and displaying the result.
The claims also do not include significantly more under Step 2B. The computer storage media, processors, and display are recited at a high level of generality and perform their ordinary functions. The ordered combination of the claim elements merely implements the abstract recommendation process on a generic computer system.
Therefore, the rejection under 35 U.S.C. § 101 is maintained.
103 Rejections:
With respect to claim 1, new prior arts Deleris and Erath are cited for new amended limitation “determining a data story recommendation, from the set of candidate data stories, based on an adaptive elicitation of user feedback via a set of inquiries selected in accordance with at least one potential reduction of the set of candidate data stories determined by generating expected values, for candidate inquiries, based on potential reductions of the set of candidate data stories in accordance with potential responses associated with the candidate inquiries, wherein an expected value for a particular candidate inquiry is determined based on an average of data story number sizes for corresponding potential responses”.
With respect to claim 10, for new amended limitation “san expected value determined based on a first potential reduction of a set of candidate data stories, the first potential reduction comprising an average of data story number sizes in accordance with a first set of potential responses associated with the first inquiry” new prior art Erath is cited.
With respect to claim 16, amended limitation “wherein the expected value to reduce the candidate set of data stories is based on an average of expected reductions for the candidate set of data stories in accordance with potential response options associated with the inquiry” is incorporated from the dependent claim 20. Therefore, previously cited reference for claim 20 Stolze is cited.
Stolze teaches in para. [0020, calculate question scores for respective said questions such that the question score for each question is dependent on one of (a) the product scores of any products excluded from said set if a said rule relating to an answer associated with that question is effective], [0042, Next, in step 30 the QA planner initializes an answer score variable A.sub.S to zero for each answer appearing in the rule condition], [0053, The rule weight is then distributed, via the answer scores, to the answers necessary for the rule to fire, and then to the question scores in accordance with the answers associated with each question], [0104, n step 61 the QA planner calculates the current focus set value by averaging the product utility values over the focus set as described above. In step 62, the QA planner determines, for the first rule, which (if any) products will be rejected if that rule fires. Then, in step 63, the focus set value for the resultant product set, i.e. excluding any rejected products, is calculated], which describes the average of the scores (average expected value) to reduce the set of data based on answer.
Therefore, Stolze teaches the above cited limitation in claim 16.
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 1-3, 5-15 are rejected under 35 U.S.C. 101 because of the following reasons:
Claim 1:
At Step 1:
The claim is directed to a "method" and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“generating a set of candidate data stories” recites a mental process because human mind can generate a set of candidate data stories by evaluation and judgment of data. For example, human mind can generate causes/explanations etc (data stories) for patterns i.e. increase/decrease in sales data by evaluation and judgement of the sales data.
-“determining a data story recommendation, from the set of candidate data stories, based on an adaptive elicitation of user feedback via a set of inquiries selected in accordance with at least one potential reduction of the set of candidate data stories determined by generating expected values, for candidate inquiries, based on potential reductions of the set of candidate data stories in accordance with potential responses associated with the candidate inquiries, wherein an expected value for a particular candidate inquiry is determined based on an average of data story number sizes for corresponding potential responses” recites a mental process because the determining limitation recites evaluating candidate data stories and candidate inquires considering potential user responses, calculating expected values based on averages of data story number sizes, and selecting a recommended data story. This is an abstract idea because it is directed to mental processes by evaluation, judgement and selection of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-"One or more computer storage media having computer-executable instructions embodied thereon that, when executed by one or more processors" which are all a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“wherein each candidate data story comprises one or more data visualizations” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“providing, for display, the data story recommendation including a set of data visualizations” is insignificant extra-solution activity as mere data gathering such as 'data outputting'. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein each candidate data story comprises one or more data visualizations” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-"providing, for display, the data story recommendation including a set of data visualizations " is well-understood, routine and conventional activities (WURC) see, MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 2:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the set of candidate data stories are generated based on a user-selected dataset” recites a mental process because human mind can generate set of data stories based on users selection by evaluation and judgement of data.
Claim 3:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the adaptive elicitation of user feedback via a set of inquiries comprises a sequence of system-selected inquiries presented to obtain user feedback selecting at least one presented response option, wherein each inquiry of the sequence of system-selected inquiries is subsequently selected based on prior user feedback provided in response to prior presented inquiries” is insignificant extra-solution activity as mere data gathering such as 'data outputting'. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the adaptive elicitation of user feedback via a set of inquiries comprises a sequence of system-selected inquiries presented to obtain user feedback selecting at least one presented response option, wherein each inquiry of the sequence of system-selected inquiries is subsequently selected based on prior user feedback provided in response to prior presented inquiries” is well-understood, routine and conventional activities (WURC) see, MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … 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/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 5:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the data story recommendation is provided concurrently with a plurality of candidate data visualizations for use in modifying the data story recommendation” is insignificant extra-solution activity as mere data gathering such as 'data outputting'. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the data story recommendation is provided concurrently with a plurality of candidate data visualizations for use in modifying the data story recommendation” is well-understood, routine and conventional activities (WURC) see, MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 6:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein determining the data story recommendation based on the adaptive elicitation of user feedback via the set of inquiries selected in accordance with the at least one potential reduction of the set of candidate data stories comprises: selecting a first inquiry, from a set of candidate inquiries, based on a first potential reduction of the set of candidate data stories in accordance with a first set of potential responses associated with the first inquiry” recites a mental process because human mind can select an inquiry from the set of inquires to reduce the candidate data stories based on the responses of the inquires by evaluation and judgement of data.
-“reducing the set of candidate data stories in accordance with the selection of the at least one response option associated with the first inquiry” recites a mental process because human mind can filter (reduce) the set of candidate date stories based on a response to the inquiry by evaluation and judgement of data.
-“selecting a second inquiry, from the set of candidate inquiries, based on a second potential reduction of the reduced set of candidate data stories in accordance with a second set of potential responses associated with the second inquiry” recites a mental process because human mind can select an inquiry from the set of inquires to reduce the candidate data stories based on the responses of the inquires by evaluation and judgement of data.
-“and reducing the reduced set of candidate data stories in accordance with the selection of the at least one response option associated with the second inquiry” recites a mental process because human mind can filter (reduce) the set of candidate date stories based on a response to the inquiry by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“obtaining a first user feedback associated with the first inquiry, wherein the first user feedback includes a selection of at least one response option associated with the first inquiry” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“obtaining a second user feedback associated with the second inquiry, wherein the second user feedback includes a selection of at least one response option associated with the second inquiry” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“obtaining a first user feedback associated with the first inquiry, wherein the first user feedback includes a selection of at least one response option associated with the first inquiry” is well-understood, routine and conventional activities (WURC) see, MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … 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/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“obtaining a second user feedback associated with the second inquiry, wherein the second user feedback includes a selection of at least one response option associated with the second inquiry” is well-understood, routine and conventional activities (WURC) see, MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … 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/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 7:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein reducing the set of candidate data stories in accordance with the selection of the at least one response option associated with the first inquiry comprises aligning the first user feedback with the reduced set of candidate data stories” recites a mental process because human mind can reduce set of candidate data stories based on the selection of a response by aligning the users feedback with the reduced set of candidate data stories by evaluation and judgement of data.
Claim 8:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the set of inquiries include response options related to content of a desired data story and structure of the desired data story” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the set of inquiries include response options related to content of a desired data story and structure of the desired data story” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 9:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the set of inquiries comprise content inquiries requesting interest in content of the data story recommendation and structure inquiries requesting interest in structure of the data story recommendation” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the set of inquiries comprise content inquiries requesting interest in content of the data story recommendation and structure inquiries requesting interest in structure of the data story recommendation” is well-understood, routine and conventional activities (WURC) see, MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … 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/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 10:
At Step 1:
The claim is directed to a "method" and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“selecting,, a first inquiry, from a set of candidate inquiries, for eliciting a first user feedback, the first inquiry being selected based on an expected value determined based on a first potential reduction of a set of candidate data stories, the first potential reduction comprising an average of data story number sizes in accordance with a first set of potential responses associated with the first inquiry” recites a mental process because human mind can select an inquiry from the set of inquires to reduce the candidate data stories to average data stories numbers sizes based on the responses of the inquires by evaluation and judgement of data.
-“reducing, , the set of candidate data stories in accordance with the selection of the at least one response option associated with the first inquiry” recites a mental process because human mind can filter (reduce) the set of candidate date stories based on a response to the inquiry by evaluation and judgement of data.
-“selecting a second inquiry, from the set of candidate inquiries, for eliciting a second user feedback, the second inquiry selected based on a second potential reduction of the reduced set of candidate data stories in accordance with a second set of potential responses associated with the second inquiry” recites a mental process because human mind can select an inquiry from the set of inquires to reduce the candidate data stories based on the responses of the inquires by evaluation and judgement of data.
-“reducing, , the reduced set of candidate data stories in accordance with the selection of the at least one response option associated with the second inquiry” recites a mental process because human mind can filter (reduce) the set of candidate date stories based on a response to the inquiry by evaluation and judgement of data.
-“generating a data story recommendation based on a candidate data story from the reduced set of candidate data stories” recites a mental process because human mind can generate a set of candidate data stories by evaluation and judgment of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“via the data story engine” which is high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application and/or is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“obtaining, via the data story engine, the first user feedback associated with the first inquiry, wherein the first user feedback includes a selection of at least one response option associated with the first inquiry” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“obtaining, via the data story engine, the second user feedback associated with the second inquiry, wherein the second user feedback includes a selection of at least one response option associated with the second inquiry” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“obtaining, via the data story engine, the first user feedback associated with the first inquiry, wherein the first user feedback includes a selection of at least one response option associated with the first inquiry” is well-understood, routine and conventional activities (WURC) see, MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … 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/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“obtaining, via the data story engine, the second user feedback associated with the second inquiry, wherein the second user feedback includes a selection of at least one response option associated with the second inquiry” is well-understood, routine and conventional activities (WURC) see, MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … 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/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 11:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“generating, the set of candidate inquiries based on a dataset for which a data story recommendation is to be generated” recites a mental process because human mind can generate set of inquires based on a dataset for which a data story recommendation is to be generated by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“via the data story engine” which is high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application and/or is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 12:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“providing, via the data story engine, the data story recommendation for display to a user providing the first user feedback and the second user feedback” is insignificant extra-solution activity as mere data gathering such as 'data outputting'. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“providing, via the data story engine, the data story recommendation for display to a user providing the first user feedback and the second user feedback” is well-understood, routine and conventional activities (WURC) see, MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 13:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the reducing the set of candidate data stories in accordance with the selection of the at least one response option associated with the first inquiry comprises generating alignment rewards to evaluate alignment of candidate data stories, in the set of candidate data stories, using user feedback for a set of attributes” recites a mental process because human mind can reduce set of candidate data stories by generating alignment rewards using user feedback for a set of attributes by evaluation and judgement of data.
Claim 14:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the set of attributes comprise content attributes and structure attributes” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 15:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“generating, the set of candidate data stories” recites a mental process because human mind can generate a set of candidate data stories by evaluation and judgment of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“via the data story engine”, “wherein each candidate data story comprises one or more data visualizations” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“via the data story engine”, “wherein each candidate data story comprises one or more data visualizations” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
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.
Claim(s) 1, 2, 3, 6, 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. (US 2022/0237228) and in view of Deleris et.al. (US 2016/0155053) and in view of Erath et al. (US 2014/0188433).
With respect to claim 1, Xu teaches one or more computer storage media having computer-executable instructions embodied thereon that, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising ([0160, a processor (e.g., a microprocessor) receives instructions, from a non-transitory computer-readable medium, (e.g., memory), and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein], memory and processor):
generating a set of candidate data stories, wherein each candidate data story comprises one or more data visualizations ([0039, Then, as illustrated in FIG. 2, the visual-data-story system 106 determines data insights in an act 204 from the data-attribute values of the input dataset 202 by detecting attributes and dataset groups and extracting data insights for the attributes and dataset groups…. FIG. 2, the visual-data-story system 106 generates a visual data story 210 in various formats within a graphical user interface from one or more visual data stories.], the stories are generated to select from and data stories comprises data visualizations);
determining a data story recommendation, from the set of candidate data stories ([0045, visual data stories to select visual data stories to surface within a graphical user interface (e.g., as recommended visual data stories). The visual-data-story system 106 generating and utilizing a visual-data-story graph is described in greater detail below (e.g., in relation to FIG. 5).], determining data story recommendations),
providing, for display, the data story recommendation including a set of data visualizations ([0049, visual data stories to select visual data stories to surface within a graphical user interface (e.g., as recommended visual data stories). The visual-data-story system 106 generating and utilizing a visual-data-story graph is described in greater detail below (e.g., in relation to FIG. 5).], displaying recommended data stories via data visualizations).
Xu does not explicitly teach based on an adaptive elicitation of user feedback via a set of inquiries selected in accordance with at least one potential reduction of the set of candidate data stories determined by generating expected values, for candidate inquiries, based on potential reductions of the set of candidate data stories in accordance with potential responses associated with the candidate inquiries, wherein an expected value for a particular candidate inquiry is determined based on an average of data story number sizes for corresponding potential responses.
However, Deleris teaches based on an adaptive elicitation of user feedback via a set of inquiries selected in accordance with at least one potential reduction of the set of candidate data stories ([0007, An elicitation question asks the decision maker which outcome vector the decision maker prefers between two outcome vectors. The decision maker provides answers to the N elicitation questions. The decision maker's answers to the N elicitation questions represent the partially specified preference information.], [0024, The decision maker preferences are elicited in a form of comparison questions, i.e., questions for asking a user of which outcome vectors (s)he prefers between two outcome vectors.], [0020, In order to identify N pairs of outcome vectors, the computing system selects the N pair of outcome vectors that maximizes an expected information gain, e.g., by using programmed method steps of an algorithm “Algorithm 3” described in greater detail herein below. The expected information gain can be measured, for example, as a reduction in an expected number of undominated strategies. The computing system selects the N pair of outcome vectors that balances user-friendliness and the expected information gain.], evaluating candidate elicitation questions based on maximum expected information gain, measured as a reduction in an expected number of remaining alternatives).
One of ordinary skill in the art would recognize incorporating users feedback of the inquires of potential reductions of candidate data stories of Deleris into the invention of Xu to have users feedback for the inquires to reduce candidate data stories. Xu and Deleris are analogues arts because all the art teaches searching and presenting search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Deleris into the invention of Xu to have a system which will improve accuracy and reducing irreverent results and also improving efficiency of the recommendation system (Deleris, [0006, The decision model is used for determining a solution to a decision problem based on attributes and uncertainties of the decision problem]).
Xu and Deleris do not explicitly teach determined by generating expected values, for candidate inquiries, based on potential reductions of the set of candidate data stories in accordance with potential responses associated with the candidate inquiries, wherein an expected value for a particular candidate inquiry is determined based on an average of data story number sizes for corresponding potential responses.
However, Erath teaches determined by generating expected values, for candidate inquiries, based on potential reductions of the set of candidate data stories in accordance with potential responses associated with the candidate inquiries ([0012, calculating on the basis of the identified first absolute reductions of each test a first prioritization of the set of possible tests to be carried out, by determining an average expected absolute reduction in the number of elements of the set of possible defective components of the technical system], [0040, the prioritization parameter rank.sub.KKF(t.sub.i) depends on the absolute expected reduction r.sub.KKF(k,i) in the number of elements of the set KKF of consistent component faults], determining possible tests to be carried out, a test is an candidate inquiry because it obtains information/feedback about which candidate remains valid. For a particular possible test, determining an average expected absolute reduction (expected value) in the number of candidate elements), wherein an expected value for a particular candidate inquiry is determined based on an average of data story number sizes for corresponding potential responses ([0012, calculating on the basis of the identified first absolute reductions of each test a first prioritization of the set of possible tests to be carried out, by determining an average expected absolute reduction in the number of elements of the set of possible defective components of the technical system], [0035, The prioritization parameter rank(t.sub.i) can indicate, for example, an average expected reduction in the number of elements of the set MDK of possible defective components], [0040, The prioritization parameter rank.sub.KKF(t.sub.i) can likewise indicate an average expected reduction. In contrast to the prioritization parameter rank(t.sub.i), the prioritization parameter rank.sub.KKF(t.sub.i) depends on the absolute expected reduction r.sub.KKF(k,i) in the number of elements of the set KKF of consistent component faults], determining number of elements in a candidate set to calculate expected reduction, the candidate-set elements are data stories, so the number of elements corresponds to the data story number size and the expected value is calculated, data story in taught by Xu in fig. 8A-8C, [0054, 0055]).
One of ordinary skill in the art would recognize incorporating users generating expected values on potential reductions using average of data story number sizes of Erath into the invention of Xu/Deleris to have expected values to reduce the data set. Xu, Deleris, Erath are analogues arts because all the art teaches searching and presenting search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Erath into the invention of Xu/ Deleris to have a system which will reduce the stories to have only relevant stories to save users time and cost of a system (Erath, [0039, T an indication that represents the benefit of the test in terms of a reduction in the number of elements of the set MDK of possible defective components.]).
With respect to claim 2, Xu, Deleris, Erath in combination teach the media of claim 1, Xu further teaches wherein the set of candidate data stories based on a user-selected dataset ([0051, As also shown in FIG. 2, the visual-data-story system 106 selects visual data stories in the act 208 by receiving visual data story selections from a client device and combining selected visual data stories to generate a larger (or final) visual data story for the input dataset 202. For example, the visual-data-story system 106 provides visual data stories from the visual-data-story graph as selectable visual data stories (e.g., as recommendations) in a graphical user interface of a client device], users select the candidate data stories from the dataset).
With respect to claim 3, Xu, Deleris, Erath in combination teach the media of claim 1, Xu and Erath do not explicitly teach wherein the adaptive elicitation of user feedback via a set of inquiries comprises a sequence of system-selected inquiries presented to obtain user feedback selecting at least one presented response option, wherein each inquiry of the sequence of system-selected inquiries is subsequently selected based on prior user feedback provided in response to prior presented inquiries.
However, Deleris teaches wherein the adaptive elicitation of user feedback via a set of inquiries comprises a sequence of system-selected inquiries presented to obtain user feedback selecting at least one presented response option ([0019, es, based on the decision problem, N pairs of outcome vectors to present to the decision maker for preference assessment. The computing system presents N elicitation questions, based on the identified N pairs of outcome vectors, to the decision maker. An elicitation question asks the decision maker which outcome vector the decision maker prefers between two outcome vectors.], [0020, The computing system selects the N pair of outcome vectors that balances user-friendliness and the expected information gain. User-friendliness can be modeled based on, including but not limited to: how quickly a user can answer a question, whether a question is qualitative or quantitative, whether an outcome vector is one of existing outcome vectors of the decision problem, whether a user wants to skip answering to a question]), the questions are presented for the user to select and present their answers, the N elicitation questions are the sequence of inquires), wherein each inquiry of the sequence of system-selected inquiries is subsequently selected based on prior user feedback provided in response to prior presented inquiries ([0020, User-friendliness can be modeled based on, including but not limited to: how quickly a user can answer a question, whether a question is qualitative or quantitative, whether an outcome vector is one of existing outcome vectors of the decision problem, whether a user wants to skip answering to a question], [0021, The computing system repeats steps 115, 120, 135, 145 and 150 until a set of recommended decisions becomes manageable by the decision maker so that the number of the recommended decisions is small enough or so that the recommended decisions are contrasted enough for the decision maker to choose], the questions (inquires) are selected based on users prior feedback).
One of ordinary skill in the art would recognize incorporating system selected inquires to obtain users feedback and also inquires are subsequently selected based on feedback of prior user feedback provided in response to prior presented inquires of Deleris into the invention of Xu/Erath to have users feedback for the inquires/prior inquires to have multiple inquiry options to reduce the search results. Xu and Deleris are analogues arts because all the art teaches search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Deleris into the invention of Xu/Erath to have a system which will improve present accurate inquires for the users to select from which is produce accurate search results (Deleris, [0006, The decision model is used for determining a solution to a decision problem based on attributes and uncertainties of the decision problem]).
With respect to claim 6, Xu, Deleris, Erath in combination teach the media of claim 1, Xu, Erath do not explicitly teach selecting a first inquiry, from a set of candidate inquiries, based on a first potential reduction of the set of candidate data stories in accordance with a first set of potential responses associated with the first inquiry;
obtaining a first user feedback associated with the first inquiry, wherein the first user feedback includes a selection of at least one response option associated with the first inquiry; reducing the set of candidate data stories in accordance with the selection of the at least one response option associated with the first inquiry; selecting a second inquiry, from the set of candidate inquiries, based on a second potential reduction of the reduced set of candidate data stories in accordance with a second set of potential responses associated with the second inquiry; obtaining a second user feedback associated with the second inquiry, wherein the second user feedback includes a selection of at least one response option associated with the second inquiry; and
reducing the reduced set of candidate data stories in accordance with the selection of the at least one response option associated with the second inquiry.
However, Deleris teaches selecting a first inquiry, from a set of candidate inquiries, based on a first potential reduction of the set of candidate data stories in accordance with a first set of potential responses associated with the first inquiry ([0019, The decision maker provides answers to the N elicitation questions. The decision maker's answers to the N elicitation questions represent the partially specified preference information.], questions are selected and users answer is provided);
obtaining a first user feedback associated with the first inquiry, wherein the first user feedback includes a selection of at least one response option associated with the first inquiry ([0019, The decision maker provides answers to the N elicitation questions. The decision maker's answers to the N elicitation questions represent the partially specified preference information.], questions are selected and users answer is provided);
reducing the set of candidate data stories in accordance with the selection of the at least one response option associated with the first inquiry ([0014, minimized preference information elicited from a decision maker can also reduce difficulty of questions (e.g., by using outcome vectors and comparison queries)], [0020, The expected information gain can be measured, for example, as a reduction in an expected number of undominated strategies], reducing the undominated strategies, the data stories are taught by Xu in fig. 8A-8C);
selecting a second inquiry, from the set of candidate inquiries, based on a second potential reduction of the reduced set of candidate data stories in accordance with a second set of potential responses associated with the second inquiry ([0019, . The decision maker's answers to the N elicitation questions represent the partially specified preference information], [0020, The expected information gain can be measured, for example, as a reduction in an expected number of undominated strategies. The computing system selects the N pair of outcome vectors that balances user-friendliness and the expected information gain. User-friendliness can be modeled based on, including but not limited to: how quickly a user can answer a question, whether a question is qualitative or quantitative, whether an outcome vector is one of existing outcome vectors of the decision problem, whether a user wants to skip answering to a question], reducing the undominated strategies, the data stories are taught by Xu in fig. 8A-8C);
obtaining a second user feedback associated with the second inquiry, wherein the second user feedback includes a selection of at least one response option associated with the second inquiry ([0018, Examples of the one or more questions include, but are not limited to: “are you comfortable making a decision ?”, “would you like to elicit additional preference information to help reduce the set of undominated strategies?”], [0019,The decision maker's answers to the N elicitation questions represent the partially specified preference information.], [0029, a relatively small number of pairs of outcome vectors (e.g., 10-12 pairs of outcome vectors for a decision problem that includes ten attributes) is enough to reduce a set of undominated strategies to a few strategies (in most cases, being a singleton) so that the decision maker can actually choose a decision strategy.], receiving answers from the users); and
reducing the reduced set of candidate data stories in accordance with the selection of the at least one response option associated with the second inquiry (([0018, Examples of the one or more questions include, but are not limited to: “are you comfortable making a decision ?”, “would you like to elicit additional preference information to help reduce the set of undominated strategies?”], [0029, a relatively small number of pairs of outcome vectors (e.g., 10-12 pairs of outcome vectors for a decision problem that includes ten attributes) is enough to reduce a set of undominated strategies to a few strategies (in most cases, being a singleton) so that the decision maker can actually choose a decision strategy.], reducing data by the answers to the questions and Xu teaches the data stories).
One of ordinary skill in the art would recognize incorporating users feedback of the inquires and reducing data sets based on the response to the inquires of Deleris into the invention of Xu/Erath to have users feedback for the inquires to reduce dataset. Xu, Deleries, Erath are analogues arts because all the art teaches searching and presenting search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Deleris into the invention of Xu/Erath to have a system which will improve accuracy and reducing irreverent results and also improving efficiency of the recommendation system (Deleris, [0006, The decision model is used for determining a solution to a decision problem based on attributes and uncertainties of the decision problem]).
With respect to claim 7, Xu, Deleris and Erath in combination teach the media of claim 6, Xu and Erath do not explicitly teach wherein reducing the set of candidate data stories in accordance with the selection of the at least one response option associated with the first inquiry comprises aligning the first user feedback with the reduced set of candidate data stories.
However, Deleris teaches wherein reducing the set of candidate data stories in accordance with the selection of the at least one response option associated with the first inquiry comprises aligning the first user feedback with the reduced set of candidate data stories ([0026, The computing system assumes that the decision maker prefers an outcome vector u over an outcome vector v and then sets a score of the pair of outcome vectors (u, v) to be the number of undominated strategies obtained by solving the decision problem under the assumption that the outcome vector u is preferred to the outcome vector v. At step 220]; [0027, At step 235-240, for each pair of outcome vector (u, v) in CandidatePairs, if the decision confirms that the outcome vector u is preferred over the outcome vector v, the computing system adds the pair of outcome vector (u, v) to Cone. Preferences represented by the pairs of outcome vectors in Cone are consistent if and only if for every pair (u, v) in Cone, it is not possible to infer that vector v is preferred to vector u], the decisions model teaches the relationships between inquiries, responses, feedback and alignment because it presents and elicitation questions asking the decision maker to choose between two outcome vectors, when the decision maker confirms a preference between outcome vectors, the confirmed preference pair is added to the set called “cone” and the system then solves the decision problem using that current Cone preference information to determine the remaining undominated strategies or recommended decision (aligning), Xu teaches data stories in fig. 8A-8C). One of ordinary skill in the art would recognize incorporating users feedback of the inquires and displaying reduced search results that matches users feedback of Deleris into the invention of Xu, Erath to have users feedback for the inquires to reduce search results. Xu, Deleris, Erath are analogues arts because all the art teaches searching and presenting search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Deleris into the invention of Xu/Erath to have a system which will improve accuracy and reducing irreverent results and also improving efficiency of the recommendation system (Deleris, [0006, The decision model is used for determining a solution to a decision problem based on attributes and uncertainties of the decision problem]).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. (US 2022/0237228) and in view of Deleris et.al. (US 216/0155053) and in view of Erath et al. (US 2014/0188433) and in view of Sengupta et al. (US 2018/0293502).
With respect to claim 5, Xu, Deleris, Erath in combination teach the media of claim 1, but do not explicitly teach wherein the data story recommendation is provided concurrently with a plurality of candidate data visualizations for use in modifying the data story recommendation.
However, Sengupta teaches wherein the data story recommendation is provided concurrently with a plurality of candidate data visualizations for use in modifying the data story recommendation (fig. 6B, [0128, FIGS. 6A-6B illustrate user interfaces that enable a user to share (FIG. 6A) or download (FIG. 6B) a story. FIG. 6A illustrates that a user can share the story with other users in the organization and either authorize them to view the story or edit the story as well. Any edits made by authorized users can be seen by every other user. The user can use the ‘History’ link below each graph to revert to his preferred version of the story. BeyondCore users can be grouped into organizations and, in some embodiments, users can share stories only with people within their organization], the left side (data story recommendation) are provided in the same interface (concurrently) with right side with different template (candidate data visualization) to select from to display the data assets and modify the data visualization).
One of ordinary skill in the art would recognize incorporating data visualizations with data to modify the data of Sengupta into the invention of Xu/Deleris/Erath to display data with an users preferred data visualizations to modify the data. Xu, Deleris, Erath/Sengupta are analogues arts because all the art teaches searching and presenting search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Sengupta into the invention of Xu/Deleris/Erath to have a system which will improve data modifications/visualizations for the users to navigate and modify data faster to make the system more efficient (Sengupta, [0141, appropriate, another variable that in combination with the first variable improves the explanatory power [0145] (iv) Next single variable that best explains the variability on its own (2nd best single predictor)]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. (US 2022/0237228) and in view of Deleris et.al. (US 216/0155053) and in view of Erath et al. (US 2014/0188433) and in view of Sengupta et al. (US 2018/0293502).
With respect to claim 8, Xu, Deleris and Erath in combination teach the media of claim 1, Rubinstein does not explicitly teach wherein the set of inquiries include response options related to content of a desired data story and structure of the desired data story.
However, Sengupta teaches wherein the set of inquiries include response options related to content of a desired data story and structure of the desired data story ([0118, On the Select a Data Set page (FIG. 3B), the user can access his stories (UI element 320), access his data sets (UI element 325), upload his data file (UI element 330), use an existing data set (UI element 335), or connect to remote servers with data (UI element 340).], [0129, Referring to FIG. 7A, the user may ‘Change Data Format’ 710 to specify how his data should be treated. BeyondCore may guess the format of a variable (e.g., at 715), or the user may manually specify the format of a field (e.g., at 720).], selecting a story (content of the desired story); changing the format (structure)).
One of ordinary skill in the art would recognize incorporating users feedback of the inquires related to content and structure of the content of Sengupta into the invention of Xu/Deleris/Erath o have users feedback for the inquires to reduce search results. Xu/Deleris/Erath/Sengupta are analogues arts because all the art teaches searching and presenting search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Sengupta into the invention of Xu/Deleris/Erath to have a system which will improve accuracy and reducing irreverent results and also improving efficiency of the recommendation system (Sengupta, [0141, another variable that in combination with the first variable improves the explanatory power [0145] (iv) Next single variable that best explains the variability on its own (2nd best single predictor)]).
With respect to claim 9, Xu, Deleris, Erath in combination teach the media of claim 1, but do not explicitly teach wherein the set of inquiries comprise content inquiries requesting interest in content of the data story recommendation and structure inquiries requesting interest in structure of the data story recommendation.
However, Sengupta teaches wherein the set of inquiries comprise content inquiries requesting interest in content of the data story recommendation and structure inquiries requesting interest in structure of the data story recommendation ([0118, On the Select a Data Set page (FIG. 3B), the user can access his stories (UI element 320), access his data sets (UI element 325), upload his data file (UI element 330), use an existing data set (UI element 335), or connect to remote servers with data (UI element 340).], [0129, Referring to FIG. 7A, the user may ‘Change Data Format’ 710 to specify how his data should be treated. BeyondCore may guess the format of a variable (e.g., at 715), or the user may manually specify the format of a field (e.g., at 720).], users create/access a story (content inquiries); changing the format (structure inquiries)).
One of ordinary skill in the art would recognize incorporating structure of data visualizations of Sengupta into the invention of Xu/Deleris/Erath to display data with an users preferred data visualizations. Xu/Deleris/Erath are analogues arts because all the art teaches searching and search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Sengupta into the invention of Xu/Deleris/Erath to have a system which will improve data visualizations according to users preference to view the data in an organized and efficient way (Sengupta, [0141, another variable that in combination with the first variable improves the explanatory power [0145] (iv) Next single variable that best explains the variability on its own (2nd best single predictor)]).
Claim(s) 10, 11, 12, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rubinstein et al. (US 2014/0040243) and in view of Petricek et.al. (US 11,055,305) and in view of Erath et al. (US 2014/0188433).
With respect to claim 10, Rubinstein teaches a computer-implemented method comprising ([0137, processor 1502 includes hardware for executing instructions, such as those making up a computer program. As an example and not by way of limitation, to execute instructions], memory and processors):
selecting, via the data story engine, a first inquiry, from a set of candidate inquiries, the first inquiry messages], the queries (inquires) are associated with scores and the queries that the user selects reduces the stories to only matching stories);
reducing, via the data story engine, the set of candidate data stories
selecting, via the data story engine, a second inquiry, from the set of candidate inquiries, the second inquiry selected based on a second potential reduction of the reduced set of candidate data stories
reducing, via the data story engine, the reduced set of candidate data stories
generating a data story recommendation based on a candidate data story from the reduced set of candidate data stories ([0106, The search-results page may also include a field for modifying search results (e.g., field 1220 in FIG. 12B], the refining filter is the second inquiry and the filter refines the results which includes data stories).
Rubinstein does not explicitly teach a first potential reduction of a set of candidate data stories, the first potential reduction comprising an average of data story number sizes in accordance with a first set of potential responses associated with the first inquiry; obtaining, via the data story engine, the first user feedback associated with the first inquiry, wherein the first user feedback includes a selection of at least one response option associated with the first inquiry; reducing, candidate data in accordance with the selection of the at least one response option associated with the second inquiry.
However, Petricek teaches for eliciting a first user feedback, the first inquiry being selected a and a women's department filter 244b) to refine the search results to different departments of shoes such as women's shoes, men's shoes”; col. 11, lines 25-30, “Moreover, responsive to selection of the recommended question 270b, an answer 272a has been presented in the question interface view 208. The answer 272a corresponds to the recommended question 270b. In some examples, the recommended question 270b and the corresponding answer 272a are selected because other customers have indicated that they are helpful (e.g., by a process of up-voting or down-voting answers with respect to helpfulness, and, in some examples, the answers as well)”]; the users answer to the questions/selection of the answer to reduce the data associated with the search results);
obtaining,the first user feedback associated with the first inquiry, wherein the first user feedback includes a selection of at least one response option associated with the first inquiry ([fig. 5, 6, 7, col. 8, lines 35-40, “The refinement bot 242 can present suggestions for filters in the chat view 234. For example, as illustrated in FIG. 5, the chat view 234 includes options for refining the search based on a particular department. Thus, the refinement interface view 205 may include recommended department filters 244 (e.g., a men's department filter 244a and a women's department filter 244b) to refine the search results to different departments of shoes such as women's shoes, men's shoes”; col. 11, lines 25-30, “Moreover, responsive to selection of the recommended question 270b, an answer 272a has been presented in the question interface view 208. The answer 272a corresponds to the recommended question 270b. In some examples, the recommended question 270b and the corresponding answer 272a are selected because other customers have indicated that they are helpful (e.g., by a process of up-voting or down-voting answers with respect to helpfulness, and, in some examples, the answers as well)”]; the users answer to the questions/selection of the answer to reduce the data associated with the search results);
reducing, the set of candidate data in accordance with the selection of the at least one response option associated with the first inquiry ([fig. 5, 6, 7, col. 8, lines 35-40, “The refinement bot 242 can present suggestions for filters in the chat view 234. For example, as illustrated in FIG. 5, the chat view 234 includes options for refining the search based on a particular department. Thus, the refinement interface view 205 may include recommended department filters 244 (e.g., a men's department filter 244a and a women's department filter 244b) to refine the search results to different departments of shoes such as women's shoes, men's shoes”; col. 11, lines 25-30, “Moreover, responsive to selection of the recommended question 270b, an answer 272a has been presented in the question interface view 208. The answer 272a corresponds to the recommended question 270b]; the users answer to the questions/selection of the answer to reduce the data associated with the search results);;
for eliciting a second user feedback, the second inquiry selected based on a second potential reduction of the reduced set of candidate data in accordance with a second set of potential responses associated with the second inquiry ([fig. 5, 6, 7, col. 8, lines 35-40, “The refinement bot 242 can present suggestions for filters in the chat view 234. For example, as illustrated in FIG. 5, the chat view 234 includes options for refining the search based on a particular department. Thus, the refinement interface view 205 may include recommended department filters 244 (e.g., a men's department filter 244a and a women's department filter 244b) to refine the search results to different departments of shoes such as women's shoes, men's shoes”; col. 11, lines 25-30, “Moreover, responsive to selection of the recommended question 270b, an answer 272a has been presented in the question interface view 208. The answer 272a corresponds to the recommended question 270b. In some examples, the recommended question 270b and the corresponding answer 272a are selected because other customers have indicated that they are helpful (e.g., by a process of up-voting or down-voting answers with respect to helpfulness, and, in some examples, the answers as well)”]; the users answer to the questions/selection of the answer to reduce the data associated with the search results);
obtaining, via the data story engine, the second user feedback associated with the second inquiry, wherein the second user feedback includes a selection of at least one response option associated with the second inquiry ([fig. 5, 6, 7, col. 8, lines 35-40, “The refinement bot 242 can present suggestions for filters in the chat view 234. For example, as illustrated in FIG. 5, the chat view 234 includes options for refining the search based on a particular department. Thus, the refinement interface view 205 may include recommended department filters 244 (e.g., a men's department filter 244a and a women's department filter 244b) to refine the search results to different departments of shoes such as women's shoes, men's shoes”; col. 11, lines 25-30, “Moreover, responsive to selection of the recommended question 270b, an answer 272a has been presented in the question interface view 208. The answer 272a corresponds to the recommended question 270b. In some examples, the recommended question 270b and the corresponding answer 272a are selected because other customers have indicated that they are helpful (e.g., by a process of up-voting or down-voting answers with respect to helpfulness, and, in some examples, the answers as well)”]; the users answer to the questions/selection of the answer to reduce the data associated with the search results);
reducing,the reduced set of candidate data in accordance with the selection of the at least one response option associated with the second inquiry ([fig. 5, 6, 7, col. 8, lines 35-40, “The refinement bot 242 can present suggestions for filters in the chat view 234. For example, as illustrated in FIG. 5, the chat view 234 includes options for refining the search based on a particular department. Thus, the refinement interface view 205 may include recommended department filters 244 (e.g., a men's department filter 244a and a women's department filter 244b) to refine the search results to different departments of shoes such as women's shoes, men's shoes”; col. 11, lines 25-30, “Moreover, responsive to selection of the recommended question 270b, an answer 272a has been presented in the question interface view 208. The answer 272a corresponds to the recommended question 270b. In some examples, the recommended question 270b and the corresponding answer 272a are selected because other customers have indicated that they are helpful (e.g., by a process of up-voting or down-voting answers with respect to helpfulness, and, in some examples, the answers as well)”]; the users answer to the questions/selection of the answer to reduce the data associated with the search results).
One of ordinary skill in the art would recognize incorporating users feedback of the inquires and reducing results based on users response to feedback of Petricek into the invention of Rubinstein to have users feedback for the inquires. Rubinstein and Petricek are analogues arts because all the art teaches searching and presenting search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Petricek into the invention of Rubinstein to have a system which will improve accuracy and reducing irreverent results and also improving efficiency of the recommendation system (Petricek, [col. 2, lines 50-55, “a large result set based on generic search term can quickly and efficiently be narrowed by a user interacting with an refinement bot, and information about an item can be presented by the user interacting with an item description bot”]).
Rubinstein and Petricek do not explicitly teach a first potential reduction of a set of candidate data stories, the first potential reduction comprising an average of data story number sizes in accordance with a first set of potential responses associated with the first inquiry.
However, Erath teaches the first inquiry being selected based on an expected value determined based on a first potential reduction of a set of candidate data stories ([0012, calculating on the basis of the identified first absolute reductions of each test a first prioritization of the set of possible tests to be carried out, by determining an average expected absolute reduction in the number of elements of the set of possible defective components of the technical system], [0040, the prioritization parameter rank.sub.KKF(t.sub.i) depends on the absolute expected reduction r.sub.KKF(k,i) in the number of elements of the set KKF of consistent component faults], determining possible tests to be carried out, a test is an candidate inquiry because it obtains information/feedback about which candidate remains valid. For a particular possible test, determining an average expected absolute reduction (expected value) in the number of candidate elements), the first potential reduction comprising an average of data story number sizes in accordance with a first set of potential responses associated with the first inquiry ([0012, calculating on the basis of the identified first absolute reductions of each test a first prioritization of the set of possible tests to be carried out, by determining an average expected absolute reduction in the number of elements of the set of possible defective components of the technical system], [0035, The prioritization parameter rank(t.sub.i) can indicate, for example, an average expected reduction in the number of elements of the set MDK of possible defective components], [0040, The prioritization parameter rank.sub.KKF(t.sub.i) can likewise indicate an average expected reduction. In contrast to the prioritization parameter rank(t.sub.i), the prioritization parameter rank.sub.KKF(t.sub.i) depends on the absolute expected reduction r.sub.KKF(k,i) in the number of elements of the set KKF of consistent component faults], determining number of elements in a candidate set to calculate expected reduction, the candidate-set elements are data stories, so the number of elements corresponds to the data story number size and the expected value is calculated).
One of ordinary skill in the art would recognize incorporating users generating expected values on potential reductions using average of data story number sizes of Erath into the invention of Rubinstein/Petricek to have expected values to reduce the data set. Rubinstein, Petricek, Erath are analogues arts because all the art teaches searching and presenting search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Erath into the invention of Rubinstein/Petricek to have a system which will reduce the stories to have only relevant stories to save users time and cost of a system (Erath, [0039, T an indication that represents the benefit of the test in terms of a reduction in the number of elements of the set MDK of possible defective components.]).
With respect to claim 11, Rubinstein, Petricek, Erath in combination teach the media of claim 10, Rubinstein, Erath do not explicitly generating, via the data story engine, the set of candidate inquiries based on a dataset for which a data story recommendation is to be generated.
However, Petricek teaches generating, via the data story engine, the set of candidate inquiries based on a dataset for which a data story recommendation is to be generated ([col. 8, lines 5-10, “For example, the refinement buttons 235 include the options of filtering based on “Top Rated” (235a), “Get Tomorrow” (235b), and “Brown” (235c). The service provider may determine which options to present based on the context of the search, customer information, and any other suitable information”], the refinement options (inquires) are generated based on context/customer information (dataset), Rubinstein teaches data story recommendation in fig. 5-7, [0111, posting it as a story on the user's profile page]).
One of ordinary skill in the art would recognize incorporating generating candidate inquires based on dataset of Petricek into the invention of Rubinstein/Erath to have inquires according to users preference. Rubinstein, Petricek, Erath are analogues arts because all the art teaches searching and presenting search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Petricek into the invention of Rubinstein/Erath to have a system which will provide users with more accurate inquires to improve search results data according to users preference to same users time and make navigation experience better (Petricek, [col. 2, lines 50-55, “a large result set based on generic search term can quickly and efficiently be narrowed by a user interacting with an refinement bot, and information about an item can be presented by the user interacting with an item description bot”]).
With respect to claim 12, Rubinstein, Petricek, Erath in combination teach the media of claim 10, Rubinstein, Erath do not explicitly providing, via the data story engine, the data story recommendation for display to a user providing the first user feedback and the second user feedback.
However, Petricek teaches providing, via the data story engine, the data story recommendation for display to a user providing the first user feedback and the second user feedback ([col. 6, lines 27-35, “At 124, the process 102 may include providing a second user interface view 126 to enable refinement of the search results or to ask questions about an item represented by the search results. In some examples, this may be performed by the service provider. The second user interface view may be a refinement interface view for interacting with the refinement tool or a question interface view for interacting with the item description tool. In some examples, the second user interface view may depend on which enhancement tool selector is presented at 122”], multiple feedback of users).
One of ordinary skill in the art would recognize incorporating users feedback of the inquires of Petricek into the invention of Rubinstein/Erath to have users feedback for the inquires. Rubinstein, Petricek, Erath are analogues arts because all the art teaches search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Petricek into the invention of Rubinstein/Erath to have a system which will improve accuracy and reducing irreverent results and also improving efficiency of the recommendation system (Petricek, [col. 2, lines 50-55, “a large result set based on generic search term can quickly and efficiently be narrowed by a user interacting with an refinement bot, and information about an item can be presented by the user interacting with an item description bot”]).
With respect to claim 13, Rubinstein, Petricek, Erath in combination teach the media of claim 10, Rubinstein teaches wherein the reducing the set of candidate data stories in accordance with the selection of the at least one response option associated with the first inquiry comprises generating alignment rewards to evaluate alignment of candidate data stories, in the set of candidate data stories, using user feedback for a set of attributes ([0056, The resources may be ranked and presented to the user according to their relative degrees of relevance to the search query. The search results may also be ranked and presented to the user according to their relative degree of relevance to the user. In other words, the search results may be personalized for the querying user based on, for example, social-graph information, user information, search or browsing history of the user, or other suitable information related to the user. In particular embodiments, ranking of the resources may be determined by a ranking algorithm implemented by the search engine. As an example and not by way of limitation, resources that are more relevant to the search query or to the user may be ranked higher than the resources that are less relevant to the search query or the user], [0068, The structured queries may be scored based on a variety of factors, such as, for example, the page or type of page the user is accessing, user-engagement factors, business-intelligence data, the click-thru rate of particular queries, the conversion-rate of particular queries, user-preferences of the querying user], scoring/ranking is alignment reward, users preference/click-thru (feedback for set of attributes), Petricek teaches responses to inquires in fig. 5, 6, 7).
Claim(s) 14, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rubinstein et al. (US 2014/0040243) and in view of Petricek et.al. (US 11,055,305) and in view of Erath et al. (US 2014/0188433) and in view of Chanda et al. (US 2024/0004923).
With respect to claim 14, Rubinstein, Petricek, Erath in combination teach the media of claim 10, Rubinstein teaches wherein the set of attributes comprise content attributes ([0041, The connection information may indicate users who have similar or common work experience, group memberships, hobbies, educational history, or are in any way related or share common attributes], content attributes).
Rubinstein and Petricek do not explicitly teach structure attributes.
However, Chanda teaches structure attributes ([0082, A user interface element 427 can be used to select a template for arranging the display of search results or digital assets on the workspace. The template can include a pattern or a format that can describe the arrangement of digital assets in a particular manner. When a particular template is selected, the technology disclosed arranges the digital assets on the workspace using the pattern or the arrangement described in the template], the output template is structure of the data visualizations which contains attributes).
One of ordinary skill in the art would recognize incorporating structure of data visualizations of Chanda into the invention of Rubinstein/Petricek/Erath to display data with an users preferred data visualizations. Rubinstein, Petricek, Chanda are analogues arts because all the art teaches searching and search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Chanda into the invention of Rubinstein/Petricek/Erath to have a system which will improve data visualizations according to users preference to view the data in an organized and efficient way (Chanda, [0088, The curating of the search results can help users in review and selection of digital assets and to efficiently select digital assets that meet the needs of their respective projects]).
With respect to claim 15, Rubinstein, Petricek, Erath in combination teach the media of claim 10, Rubinstein teaches generating, via the data story engine, the set of candidate data stories ([0090, the sent structured queries may be displayed as one or more stories in the newsfeed of the querying user as a suggested query separate from query field 350], [0124, The content may include users, profile pages, posts, news stories, headlines, instant messages], [0127, A coefficient may be used when generating or presenting any type of objects to a user, such as advertisements, search results, news stories, media, messages, notifications, or other suitable objects], fig. 12A; the news stories are generated and the candidate stories are displayed (data visualizations)).
Rubinstein, Petricek do not explicitly teach wherein each candidate data story comprises one or more data visualizations.
However, Chanda teaches wherein each candidate data story comprises one or more data visualizations ([0082, A user interface element 427 can be used to select a template for arranging the display of search results or digital assets on the workspace. The template can include a pattern or a format that can describe the arrangement of digital assets in a particular manner. When a particular template is selected, the technology disclosed arranges the digital assets on the workspace using the pattern or the arrangement described in the template], the output template is structure of the data visualizations, each data is associated with data visualization).
One of ordinary skill in the art would recognize incorporating structure of data visualizations of Chanda into the invention of Rubinstein/Petricek/Erath to display data with an users preferred data visualizations. Rubinstein, Petricek, Chanda are analogues arts because all the art teaches search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Chanda into the invention of Rubinstein/Petricek/Erath to have a system which will improve data visualizations according to users preference to view the data in an organized and efficient way (Chanda, [0088, The curating of the search results can help users in review and selection of digital assets and to efficiently select digital assets that meet the needs of their respective projects]).
Claim(s) 16, 17, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rubinstein et al. (US 2014/0040243) and in view of Petricek et.al. (US 11,055,305) and in view of Chanda et al. (US 2024/0004923) and in view of Stolze et al. (US 2002/0004764).
With respect to claim 16, Rubinstein teaches a computing system comprising: a processor; and computer storage memory having computer-executable instructions stored thereon which, when executed by the processor, configure the computing system to ([0137, processor 1502 includes hardware for executing instructions, such as those making up a computer program. As an example and not by way of limitation, to execute instructions], memory and processors):
cause display ofinquiriesassociated with a user interest in content , wherein an inquiry, , is selected for display based on an expected value to reduce a candidate set of data stories ([0090, each sent structured query may have a score greater than a threshold score to the querying user. After scoring the structured queries, the social-networking system 160 may then send only those structured queries having a score greater than a threshold score…. the sent structured queries may be displayed as one or more stories in the newsfeed of the querying user as a suggested query separate from query field 350], [0124, The content may include users, profile pages, posts, news stories, headlines, instant messages], [0127, A coefficient may be used when generating or presenting any type of objects to a user, such as advertisements, search results, news stories, media, messages, notifications, or other suitable objects], fig. 12A; the news stories are generated and the candidate stories are displayed, the queries (inquiries) are displayed based on a rank/score (expected value) to reduce the search results only to relevant search results);
and cause display of a data story recommendation selected, from among the candidate set of data stories,
Rubinstein does not explicitly teach cause display of a sequence of inquiries to elicit user feedback associated with a user interest in content and structure of a data
However, Petricek teaches cause display of a sequence of inquiries to elicit user feedback associated with a user interest in content refinement bot 242 can present suggestions for filters in the chat view 234. For example, as illustrated in FIG. 5, the chat view 234 includes options for refining the search based on a particular department. Thus, the refinement interface view 205 may include recommended department filters 244 (e.g., a men's department filter 244a and a women's department filter 244b) to refine the search results to different departments of shoes such as women's shoes, men's shoes, etc”]), refinement options (sequence of inquires are displayed to users to give answer (feedback) regarding the items (content)), , of the sequence of inquiries, ([col. 7, lines 33-40, “when the number of search results 224 exceeds some fixed threshold (which may be particular to a category of items), when the search results 224 include items from more than one item category, when current contextual information about the user's interactions indicates that user would benefit from an enhancement tool, when user actions with respect to the search results interface view 203 indicate an intent to refine the search results 223, and/or based on any other suitable triggering information”], the refinement (inquiries/feedbacks) are presented));
obtain the elicited user feedback in response to the sequence of inquiries ([fig. 5, 6, 7, col. 8, lines 35-45, “the refinement bot 242 can present suggestions for filters in the chat view 234. For example, as illustrated in FIG. 5, the chat view 234 includes options for refining the search based on a particular department. Thus, the refinement interface view 205 may include recommended department filters 244 (e.g., a men's department filter 244a and a women's department filter 244b) to refine the search results to different departments of shoes such as women's shoes, men's shoes, etc”]), refinement options (sequence of inquiries are displayed to users to give answer (feedback) regarding the items (content));
and cause display of a data recommendation selected, from among the candidate set of data , based on alignment with the elicited user feedback in response to the sequence of inquiries ([col. 9, lines 15-20, “the additional filters include a rating filter 250a that limits the search results to items rating four stars and up. Other additional filters 250 may also be applied such as, for example, items associated with a VIP shopping program, prices, ranking, and any other suitable data by which the search results can be filtered”], based on the users feedback the search results are updated).
One of ordinary skill in the art would recognize incorporating sequence of inquires and feedback of the inquires of Petricek into the invention of Rubinstein to have users feedback for the inquires and sequence of inquires to reduce search results. Rubinstein and Petricek are analogues arts because all the art teaches search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Petricek into the invention of Rubinstein to have a system which will improve accuracy and reducing irreverent results and also improving efficiency of the recommendation system (Petricek, [col. 2, lines 50-55, “a large result set based on generic search term can quickly and efficiently be narrowed by a user interacting with an refinement bot, and information about an item can be presented by the user interacting with an item description bot”]).
Rubinstein and Petricek do not explicitly teach structure of a data story having a plurality of data visualizations.
However, Chanda teaches structure of a data story having a plurality of data visualizations ([0082, A user interface element 427 can be used to select a template for arranging the display of search results or digital assets on the workspace. The template can include a pattern or a format that can describe the arrangement of digital assets in a particular manner. When a particular template is selected, the technology disclosed arranges the digital assets on the workspace using the pattern or the arrangement described in the template], the output template is structure of the data visualizations).
One of ordinary skill in the art would recognize incorporating structure of data visualizations of Chanda into the invention of Rubinstein/Petricek to display data with an users preferred data visualizations. Rubinstein, Petricek, Chanda are analogues arts because all the art teaches search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Chanda into the invention of Rubinstein/Petricek to have a system which will improve data visualizations according to users preference to view the data in an organized and efficient way (Chanda, [0088, The curating of the search results can help users in review and selection of digital assets and to efficiently select digital assets that meet the needs of their respective projects]).
Rubinstein, Petricek, Chanda in combination teach do not explicitly wherein the expected value to reduce the candidate set of data stories is based on an average of expected reductions for the candidate set of data stories in accordance with potential response options associated with the inquiry.
However, Stolze teaches wherein the expected value to reduce the candidate set of data stories is based on an average of expected reductions for the candidate set of data stories in accordance with potential response options associated with the inquiry ([0020, calculate question scores for respective said questions such that the question score for each question is dependent on one of (a) the product scores of any products excluded from said set if a said rule relating to an answer associated with that question is effective], [0042, Next, in step 30 the QA planner initializes an answer score variable A.sub.S to zero for each answer appearing in the rule condition], [0053, The rule weight is then distributed, via the answer scores, to the answers necessary for the rule to fire, and then to the question scores in accordance with the answers associated with each question], [0104, n step 61 the QA planner calculates the current focus set value by averaging the product utility values over the focus set as described above. In step 62, the QA planner determines, for the first rule, which (if any) products will be rejected if that rule fires. Then, in step 63, the focus set value for the resultant product set, i.e. excluding any rejected products, is calculated], the average of the scores (average expected value) to reduce the set of data based on answer). One of ordinary skill in the art would recognize incorporating generating average expected value of inquires in accordance with potential responses of Stolze into the invention of Rubinstein/Petricek/Chanda to have produce more accurate search results.
Rubinstein, Petricek, Chanda, Stolze are analogues arts because all the art teaches searching and presenting search results. Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Stolze into the invention of Rubinstein/Petricek/Chanda to have a system which display search results according to users preference to improve accuracy, enhance users experience, and increase efficiency of presenting relevant stories (Stolze, [0109, provide systems which allow effective needs-based question selection for an arbitrary initial product set, and thus allow efficient use of needs-based interviewing in combination with feature-based filtering according to the type of assistance required by users during the product selection process.])
With respect to claim 17, Rubinstein, Petricek, Chanda, Stolze in combination teach the media of claim 16, Rubinstein, Chanda, Stolze do not explicitly wherein the inquiries of the sequence of inquiries are selected from a set of candidate inquiries generated based on a dataset.
However, Petricek teaches wherein the inquiries of the sequence of inquiries are selected from a set of candidate inquiries generated based on a dataset (col. 8, lines 45-50, “ The refinement bot 242 may determine which filters to present based on a policy learned from customer behavior and implicit feedback (e.g., did users purchase more frequently when a particular filter was suggested), explicit choice of past users when browsing an online store by navigating the category drill-down, filters the user used in the past (e.g., personalized filters), popular filters, business rules, user filter choices selected from available filter choices, and/or any other suitable information”], the inquires are generated based on users preference, rules etc (dataset)).
One of ordinary skill in the art would recognize incorporating generating inquires based on a dataset of Petricek into the invention of Rubinstein/Chanda/Stolze to have relevant questions. Rubinstein, Petricek, Chanda, Stolze are analogues arts because all the art teaches search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Petricek into the invention of Rubinstein/Chanda/Stolze to have a system which will generate inquires that are relevant to the topic/users preference to find accurate search results faster (Petricek, [col. 2, lines 50-55, “a large result set based on generic search term can quickly and efficiently be narrowed by a user interacting with an refinement bot, and information about an item can be presented by the user interacting with an item description bot”]).
With respect to claim 18, Rubinstein, Petricek, Chanda, Stolze in combination teach the media of claim 16, Rubinstein, Petricek, Stolze do not explicitly to generate the candidate set of data stories based on a user-selected dataset.
However, Chandra teaches to generate the candidate set of data stories based on a user-selected dataset ([0077, FIG. 3B presents a user interface 311 that shows the search dialog box 303 with a drop-down menu (or drop-down list) 315 including a list of sources of digital assets. A user can select a user interface element 313 causing the drop-down menu 315 to display. The user can select an option 317 to search all sources of digital assets or select one or more sources of digital assets in a list 319], the user selects a dataset from the 315 to generate data; Rubinstein teaches data stories in para. [0090, the sent structured queries may be displayed as one or more stories in the newsfeed of the querying user as a suggested query separate from query field 350]).
One of ordinary skill in the art would recognize incorporating generating data based on a dataset of Chanda into the invention of Rubinstein/Petricek/Stolze to have relevant data stories. Rubinstein, Petricek, Chanda are analogues arts because all the art teaches search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Chanda into the invention of Rubinstein/Petricek/Stolze to have a system which will generate inquires that are relevant to the topic/users preference to find accurate search results faster (Chanda, [0088, The curating of the search results can help users in review and selection of digital assets and to efficiently select digital assets that meet the needs of their respective projects]).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rubinstein et al. (US 2014/0040243) and in view of Petricek et.al. (US 11,055,305) and in view of Chanda et al. (US 2024/0004923) and in view of Stolze et al. (US 2002/0004764) and in view of Patodia et al. (US 2021/0357770).
With respect to claim 19, Rubinstein, Petricek, Chanda, Stolze in combination teach the media of claim 16, Rubinstein teaches story recommendation ([0010, "Posts by my friends about [news story from yesterday]").
Rubinstein, Petricek, Chanda, Stolze do not explicitly teach cause display of a flow chart that represents a structure of the data story recommendation and alternative data visualizations for use in modifying the data story recommendation.
However, Patodia teaches cause display of a flow chart that represents a structure of the data story recommendation and alternative data visualizations for use in modifying the data story recommendation ([0026, A decision blueprint for a smart decision service may be visually represented by a flow chart or diagram that displays a set of rules interconnected and organized according to the possible flow paths (an example of a visualization of a decision blueprint according to some embodiments is shown in FIG. 3). A flow chart may visualize a representation of all the possible flow paths interconnected by nodes in a tree-like structure between a source (input) and a sink (output)], displaying data in flowchart or in other data visualization to modify the data).
One of ordinary skill in the art would recognize incorporating flowchart and other data visualizations on a dataset of Patodia into the invention of Rubinstein/Petricek/Chanda/Stolze to display data in flowchart and other visualization options. Rubinstein, Petricek, Chanda, Patodia are analogues arts because all the art teaches searching and presenting search results.
Therefore, it would have been obvious to one of the ordinary skills in the art before the elective filing date to incorporate features of Patodia into the invention of Rubinstein/Petricek/Chanda/Stolze to have a system which will display data in flowcharts and other visualizations to view and understand data faster and also to select most accurate data (Patodia, [0028, use cases where a full data load process may be more efficient in time consumption, processing power, or memory usage than a selective data load process]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FATIMA P MINA whose telephone number is (571)270-3556. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm.
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/FATIMA P MINA/ Examiner, Art Unit 2159
/ANN J LO/ Supervisory Patent Examiner, Art Unit 2159
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