DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is responsive to the Application filed on 9/23/2024. Claims 1-20 are pending in the case.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5, 9-13, and 17 of U.S. Patent No. 12505312 B2 and claims 1-20 of U.S. Patent No. 12019998 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-5, 9-13, and 17 of U.S. Patent No. 12505312 B2 and claims 1-20 of U.S. Patent No. 12019998 B1 contain elements of claims 1-20 of the instant application. Claims 1-20 of the instant application are not patently distinct from the earlier patents claims as such unpatentable over obvious-type double patenting.
Instant Application 18/893885
U.S. Patent No.
12505312 B2
U.S. Patent No.
12019998 B1
1. A method performed at a computing system having memory and one or more processors, the method comprising:
presenting a data visualization page to a user, the data visualization page including a first region for displaying a data visualization and a second region for phrase recommendations;
obtaining a dataset selected by the user, the dataset including a plurality of fields;
generating a first set of phrase recommendations based on the dataset, each phrase recommendation in the first set of phrase recommendations corresponding to a respective field in the plurality of fields;
displaying the first set of phrase recommendations in the second region;
receiving a user selection of a first phrase of the first set of phrase recommendations; and
in response to the user selection:
presenting a data visualization in the first region, the data visualization generated using the first phrase; and
displaying a second set of phrase recommendations generated based on the user selection of the first phrase.
1. A method performed at a computing system having memory and one or more processors, the method comprising:
presenting a data visualization page to a user, the data visualization page including a first region for displaying a data visualization and a second region for recommendations;
obtaining a dataset selected by the user, the dataset including a plurality of fields;
generating, by a machine learning model, a first set of recommendations based on the dataset, each recommendation in the first set of phrase recommendations corresponding to a respective field in the plurality of fields;
displaying the first set of recommendations in the second region;
receiving a user selection of a first recommendation of the first set of recommendations; and
in response to the user selection:
presenting a data visualization in the first region, the data visualization generated using the first recommendation;
generating a second set of recommendations, which include an updated set of the first set of recommendations, based on the user selection of the first recommendation, each recommendation in the second set specifying updates to one or more visual characteristics of the data visualization; and
displaying the second set of recommendations in the second region.
1. A method performed at a computing system having memory and one or more processors, the method comprising:
presenting a data visualization page to a user, the data visualization page including a first region for displaying a data visualization and a second region for phrase recommendations;
obtaining a dataset selected by the user, the dataset including a plurality of fields;
generating a first set of phrase recommendations based on the dataset, each phrase recommendation in the first set of phrase recommendations corresponding to a respective field in the plurality of fields;
displaying the first set of phrase recommendations in the second region;
receiving user selection of a first phrase of the first set of phrase recommendations; and
in response to the user selection:
presenting a data visualization in the first region, the data visualization generated using the first phrase;
generating a second set of phrase recommendations based on the user selection of the first phrase, each phrase recommendation in the second set specifying updates to one or more visual characteristics of the data visualization; and
displaying the second set of phrase recommendations in the second region.
2. The method of claim 1, wherein each phrase in the first set of phrase recommendations comprises a respective field of the plurality of fields and a respective operator, and wherein each phrase corresponds to a valid command in a visualization language.
2. The method of claim 1, wherein each recommendation generated by the machine learning model in the first set of recommendations comprises a respective field of the plurality fields and a respective operator, and wherein each recommendation corresponds to a valid command in a visualization language.
2. The method of claim 1, wherein each phrase in the first set of phrase recommendations comprises a respective field of the plurality of fields and a respective operator, and wherein each phrase corresponds to a valid command in a visualization language.
3. The method of claim 1, wherein the first set of phrase recommendations includes an aggregation phrase and a filter phrase; wherein the aggregation phrase comprises a first field of the plurality of fields and an aggregation operator; and wherein the filter phrase comprises a second field of the plurality of fields, a filter operator, and a value.
3. The method of claim 1, wherein the first set of phrase recommendations generated by the machine learning model includes an aggregation recommendation and a filter recommendation; wherein the aggregation recommendation comprises a first field of the plurality of fields and an aggregation operator; and wherein the filter recommendation comprises a second field of the plurality of fields, a filter operator, and a value.
3. The method of claim 1, wherein the first set of phrase recommendations includes an aggregation phrase and a filter phrase; wherein the aggregation phrase comprises a first field of the plurality of fields and an aggregation operator; and wherein the filter phrase comprises a second field of the plurality of fields, a filter operator, and a value.
4. The method of claim 1, wherein at least one phrase in the first set of phrase recommendations has not previously been selected by a user to visualize the dataset.
4. The method of claim 1, wherein at least one phrase in the first set of recommendations is generated using a machine learning model rather than selected from a list of previously used recommendations and has not previously been selected by a user to visualize the dataset.
4. The method of claim 1, wherein at least one phrase in the first set of phrase recommendations has not previously been selected by a user to visualize the dataset.
5. The method of claim 1, further comprising, prior to generating the first set of phrase recommendations, identifying a collection of phrases for the dataset; wherein the first set of phrase recommendations is generated from the collection of phrases; and wherein incompatible phrases from the collection of phrases are excluded from the first set of phrase recommendations.
5. The method of claim 1, further comprising, prior to generating the first set of recommendations, identifying a collection of recommendations for the dataset; wherein the first set of phrase recommendations is generated from the collection of recommendations; and wherein incompatible phrases from the collection of phrases are excluded from the first set of recommendations.
5. The method of claim 1, further comprising, prior to generating the first set of phrase recommendations, identifying a collection of phrases for the dataset; wherein the first set of phrase recommendations is generated from the collection of phrases; and wherein incompatible phrases from the collection of phrases are excluded from the first set of phrase recommendations.
6. The method of claim 1, wherein the first set of phrase recommendations are generated based on prior visualization data associated with the dataset.
6. The method of claim 1, wherein the first set of phrase recommendations are generated based on prior visualization data associated with the dataset.
7. The method of claim 6, wherein the prior visualization data comprises information about occurrences and concurrences of phrases in historical visualizations created by users for the dataset.
7. The method of claim 6, wherein the prior visualization data comprises information about occurrences and concurrences of phrases in historical visualizations created by users for the dataset.
8. The method of claim 1, further comprising generating the second set of phrase recommendations based on the first phrase and prior visualization data associated with the dataset.
8. The method of claim 1, further comprising generating the second set of phrase recommendations based on the first phrase and prior visualization data associated with the dataset.
9. The method of claim 8, wherein generating the second set of phrase recommendations includes selecting phrases based on respective concurrence probabilities with the first phrase.
9. The method of claim 8, wherein generating the second set of phrase recommendations includes selecting phrases based on respective concurrence probabilities with the first phrase.
10. The method of claim 1, further comprising: after displaying the second set of phrase recommendations, receiving a second user selection of a second phrase from the second set of phrase recommendations; and in response to the second user selection: updating the data visualization in the first region based on the first phrase and the second phrase; and displaying a third set of phrase recommendations in the second region.
10. The method of claim 1, further comprising: after displaying the second set of phrase recommendations, receiving a second user selection of a second phrase from the second set of phrase recommendations; and in response to the second user selection: updating the data visualization in the first region based on the first phrase and the second phrase; and displaying a third set of phrase recommendations in the second region.
11. The method of claim 10, further comprising generating the third set of phrase recommendations, the generating comprising: identifying a collection of phrases for the dataset; identifying phrases from the collection of phrases that have historically occurred with at least one of the first phrase and the second phrase; and ranking the identified phrases based on concurrence probabilities between the identified phrases and the first and second phrases, wherein the ranking includes prioritizing identified phrases that have historical concurrence with both the first phrase and the second phrase.
11. The method of claim 10, further comprising generating the third set of phrase recommendations, the generating comprising: identifying a collection of phrases for the dataset; identifying phrases from the collection of phrases that have historically occurred with at least one of the first phrase and the second phrase; and ranking the identified phrases based on concurrence probabilities between the identified phrases and the first and second phrases, wherein the ranking includes prioritizing identified phrases that have historical concurrence with both the first phrase and the second phrase.
Claims 12-20 are substantially the same as claims 1-11.
Claims 9-13 and 17 are substantially the same as claims 1-5.
Claims 12-20 are substantially the same as claims 1-11.
Claim Rejections - 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-10 and 12-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Venkata et al. (US 20170039281 A1, hereinafter Venkata) in view of Ericson et al. (US 2020/0089700 A1, hereinafter Ericson).
As to independent claim 1, Venkata teaches a method performed at a computing system having memory and one or more processors, the method comprising:
presenting a data visualization page to a user, the data visualization page including a first region for displaying a data visualization (Fig. 10-17 data visualization page includes a first region for displaying a data visualization);
obtaining a dataset selected by the user, the dataset including a plurality of field (“2010” for “revenue” by the user in Fig. 11);
generating a first set of phrase recommendations based on the dataset, each phrase recommendation in the first set of phrase recommendations corresponding to a respective field in the plurality of fields (“Data analytic system 150 may perform a query process to parse the input and determine a semantic meaning of the input. A list of matching terms may be identified by data analytic system 150 and shown to user in graphical interface 1100 as a drop down list 56 of suggested other parameters.” Paragraph 0153);
displaying the first set of phrase recommendations (list 56 in Fig. 11);
receiving a user selection of a first phrase of the first set of phrase recommendations (“product” selected by the user, paragraph 0154); and
in response to the user selection:
presenting a data visualization in the first region, the data visualization generated using the first phrase (“FIG. 12 illustrates a graphical interface 1200 of a visual representation of results in response to the additional input of “product” with the input described in FIG. 11. The system may process the new input string to identify its meaning and generate a query to obtain new results. The new results may be displayed in a different visual representation based on the new measures and dimensions in the input.” Paragraph 0154); and
displaying a second set of phrase recommendations generated based on the user selection of the first phrase (“FIG. 13 illustrates a graphical interface 1300 of a visual representation of results in response to the additional input of “target revenue” with the input described in FIG. 12” paragraph 0155, this implies that a second list 56 is presented for the user to select “target revenue”).
Venkata does not appear to expressly teach the data visualization page including a second region for phrase recommendations.
Ericson teaches the user interface including a first region and a second region (data visualization region 112 for displaying the data visualization generated based on the input 128 in the user interface control 129).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Venkata to comprise the data visualization page including a second region for phrase recommendations. One would have been motivated to make such a combination to display data clearly and effectively.
As to dependent claim 2, Venkata teaches the method of claim 1, Venkata further teaches wherein each phrase in the first set of phrase recommendations comprises a respective field of the plurality of fields and a respective operator, and wherein each phrase corresponds to a valid command in a visualization language (“The terms may be listed in order of best possible match. The terms may be identified based on their association with terms in a subject area matching the terms in the input the search process may document and save the search terms and their associated queries for future use. In one embodiment, they are stored by user, so the system can access a list of terms used by the user and the statistics of the most frequently used terms. Accessing this list would allow the system to populate the drop down list 56.” Paragraph 0153).
As to dependent claim 3, Venkata teaches the method of claim 1, Venkata does not appear to expressly teach wherein the first set of phrase recommendations includes an aggregation phrase and a filter phrase;
wherein the aggregation phrase comprises a first field of the plurality of fields and an aggregation operator; and
wherein the filter phrase comprises a second field of the plurality of fields, a filter operator, and a value.
Ericson teaches wherein the first set of phrase recommendations includes an aggregation phrase and a filter phrase (Fig. 3A, Fig. 4A-E - phrase 130-1 through 130-7);
wherein the aggregation phrase comprises a first field of the plurality of fields and an aggregation operator (Fig. 3A, phrase 130-1 and Fig. 5B, for a quantitative data field such as Population 534, an aggregation type must also be selected (e.g., SUM, COUNT, or AVERAGE).); and
wherein the filter phrase comprises a second field of the plurality of fields, a filter operator, and a value (Fig. 5C, phrase 550-3 specifies a filter that limits the data to those whose country names include the text string “South” 556).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Venkata to comprise wherein the first set of phrase recommendations includes an aggregation phrase and a filter phrase; wherein the aggregation phrase comprises a first field of the plurality of fields and an aggregation operator; and wherein the filter phrase comprises a second field of the plurality of fields, a filter operator, and a value. One would have been motivated to make such a combination to provide relevant information.
As to dependent claim 4, Venkata teaches the method of claim 1, Venkata further teaches wherein at least one phrase in the first set of phrase recommendations has not previously been selected by a user to visualize the dataset (“The terms may be listed in order of best possible match. The terms may be identified based on their association with terms in a subject area matching the terms in the input” paragraph 0153).
As to dependent claim 5, Venkata teaches the method of claim 1, Venkata further teaches the method comprising, prior to generating the first set of phrase recommendations, identifying a collection of phrases for the dataset;
wherein the first set of phrase recommendations is generated from the collection of phrases (the terms are stored, so the system can access a list of terms, paragraph 0153); and
wherein incompatible phrases from the collection of phrases are excluded from the first set of phrase recommendations (“The terms may be listed in order of best possible match. The terms may be identified based on their association with terms in a subject area matching the terms in the input.” Paragraph 0153).
As to dependent claim 6, Venkata teaches the method of claim 1, Venkata further teaches wherein the first set of phrase recommendations are generated based on prior visualization data associated with the dataset (“the search process may document and save the search terms and their associated queries for future use. In one embodiment, they are stored by user, so the system can access a list of terms used by the user and the statistics of the most frequently used terms. Accessing this list would allow the system to populate the drop down list 56” paragraph 0153).
As to dependent claim 7, Venkata teaches the method of claim 6, Venkata further teaches wherein the prior visualization data comprises information about occurrences and concurrences of phrases in historical visualizations created by users for the dataset (“the search process may document and save the search terms and their associated queries for future use. In one embodiment, they are stored by user, so the system can access a list of terms used by the user and the statistics of the most frequently used terms. Accessing this list would allow the system to populate the drop down list 56” paragraph 0153).
As to dependent claim 8, Venkata teaches the method of claim 1, Venkata further teaches the method comprising generating the second set of phrase recommendations based on the first phrase and prior visualization data associated with the dataset (“FIG. 13 illustrates a graphical interface 1300 of a visual representation of results in response to the additional input of “target revenue” with the input described in FIG. 12” paragraph 0155, this implies that a second list 56 may be presented based on the first phrase and prior visualization data associated with the dataset and the user selects “target revenue”).
As to dependent claim 9, Venkata teaches the method of claim 8, Venkata teaches wherein generating the second set of phrase recommendations includes selecting phrases based on respective concurrence probabilities with the first phrase (“the system can access a list of terms used by the user and the statistics of the most frequently used terms. Accessing this list would allow the system to populate the drop down list 56.” Paragraph 0153).
As to dependent claim 10, Venkata teaches the method of claim 1, Venkata further teaches the method comprising:
after displaying the second set of phrase recommendations (list 56 displaying the second set of phrase), receiving a second user selection of a second phrase from the second set of phrase recommendations (“Target Revenue” selected and added as shown in Fig. 13); and
in response to the second user selection:
updating the data visualization in the first region based on the first phrase and the second phrase (Fig.13 displaying an updated data visualization); and
displaying a third set of phrase recommendations in the second region (Fig. 14, “discount amount” from third set of phrase list 56 selected).
Claims 12-18 reflect a computing device embodying the limitations of claims 1-7 and are therefore rejected under the same rationale above.
Claims 19-20 reflect a non-transitory computer-readable medium embodying the limitations of claims 1 and 10 and are therefore rejected under the same rationale above.
Claim 11 is rejected under 35 U.S.C. § 103 as being unpatentable over Venkata et al. in view of Ericson et al., and Zhang et al. (US 2010/0250190 A1, hereinafter Zhang).
As to dependent claim 11, Venkata teaches the method of claim 10, Venkata does not appear to expressly teach the method comprising generating the third set of phrase recommendations, the generating comprising:
identifying a collection of phrases for the dataset;
identifying phrases from the collection of phrases that have historically occurred with at least one of the first phrase and the second phrase; and
ranking the identified phrases based on concurrence probabilities between the identified phrases and the first and second phrases, wherein the ranking includes prioritizing identified phrases that have historical concurrence with both the first phrase and the second phrase.
Zhang teaches identifying a collection of phrases for the dataset; identifying phrases from the collection of phrases that have historically occurred with at least one of the first phrase and the second phrase (Fig. 2, boosted tag ranking 150 of tags 130a of media instance 130); and
ranking the identified phrases based on concurrence probabilities between the identified phrases and the first and second phrases, wherein the ranking includes prioritizing identified phrases that have historical concurrence with both the first phrase and the second phrase (“boosted tag ranking 150 suitable at least for search result ranking, tag recommendation, and group recommendation. Search result ranking is typically to provide a relevance ranking for each media instance result of a search, the relevance ranking relative to the search term. Tag recommendation is typically to recommend a set of tags for a media instance, such as media instance 130, based on the content of a media repository, such as media repository 140. Tag recommendation thus allows a user to select relevant tags from the recommended set of tags, which tend to be highly relevant.” paragraph 0014).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Venkata to comprise identifying a collection of phrases for the dataset; identifying phrases from the collection of phrases that have historically occurred with at least one of the first phrase and the second phrase; and ranking the identified phrases based on concurrence probabilities between the identified phrases and the first and second phrases, wherein the ranking includes prioritizing identified phrases that have historical concurrence with both the first phrase and the second phrase. One would have been motivated to make such a combination to provide highly relevant data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Du et al. 11836172 Facilitating generation of data visualizations via natural language processing.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAHELET SHIBEROU whose telephone number is (571)270-7493. The examiner can normally be reached Monday-Friday 9:00 AM-5:00 PM Eastern Time.
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/MAHELET SHIBEROU/Primary Examiner, Art Unit 2171
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