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 .
Detailed Action
This is a final Office Action for application 17/579,262 in response to in response to arguments and amendments filed on 03/23/2026. Claims 1, 25 and 26 are currently amended. Claim 23 is cancelled. Claims 1-22 and 24-26 are pending and examined below.
Response to Arguments
Applicant’s arguments, see pgs. 15-18, filed 03/23/2026 with respect to the rejection(s) of claim(s) 1, 25 and 26 under 35 USC § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Morales (US Pub. 2011/0093361).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5, 7-8, 11-15, 21-22 and 24-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stajanovic et al. (US Pat. 10,296,192) in view of Tresser et al. (US Pub. 2007/0255707) and Morales (US Pub. 2011/0093361).
Regarding claim 1, Stajanovic teaches
A method for data ingestion for a data visualization platform, the method comprising: receiving a plurality of data sets, wherein a first data set of the plurality of data sets is received from a first data source and wherein the second data set of the plurality of data sets is received from a second data source; (Col. 2 [Lines 15-26], Col. 26 [Lines 37-45] one or more sources are identified and profiled, including a first and second portion of data)
generating and store a merged data set based on the plurality of received data sets; (Col. 2 [Lines 15-26] this data enrichment service can extract, repair, enrich heterogenous (i.e. the plurality of) datasets)
causing display of a graphical user interface configured to receive user instructions for data processing; (Fig. 3A; Col. 2 [Lines 15-26], Col. 11 [Lines 4-46] a user interface (i.e. graphical user interface; #306) receives user instructions to perform various transform actions)
receiving, via the graphical user interface, a first input comprising an instruction to perform one or more data standardization operations; (Fig. 3A; Col. 2 [Lines 15-26], Col. 11 [Lines 4-46] a user interface (#306) receives user instructions to perform various transform actions (i.e. standardization operations))
in response to receiving the first input, applying the one or more data standardization operations to the merged data set to process the merged data set to generate a standardized data set; (Fig. 3; Col. 2 [Lines 15-26], Col. 11 [Lines 4-46] a user interface (#306) receives user instructions to perform (i.e. apply) various transform actions)
receiving, via the graphical user interface, a second input comprising an instruction to perform one or more data analytics operations; (Fig. 10; Col. 30 [Lines 53-64] cloud infrastructure system includes services for document collaboration between multiple users (e.g. #1002-6))
in response to receiving the second input, applying the one or more data analytics operations to the standardized data set to generate insights data; (Fig. 3; Col. 2 [Lines 15-26], Col. 11 [Lines 4-46] a user interface (#306) receives user instructions to perform (i.e. apply) various transform actions)
receiving, via the graphical user interface, a third user input comprising an instruction to perform one or more data visualization operations; and (Fig. 10; Col. 30 [Lines 53-64] cloud infrastructure system includes services for document collaboration between multiple users (e.g. #1002-6))
in response to receiving the third input, generating one or more data visualizations based on the insights data, wherein the data visualization includes an association visualization, wherein the association visualization depicts a plurality of association scores between a plurality of pairs of entities, wherein each of the plurality of pairs of entities includes a first entity in a first set of entities and a second entity in a second set of entities, and wherein the association visualization depicts a respective association score for each pair of the plurality of pairs of entities (Fig. 3A; Col. 11 [Lines 4-46], Col. 15 [Lines 3-22], Col. 22 [Lines 21-37] a similarity metric (i.e. association score) is provided by the knowledge service (#310) to the profile engine (#326) that can be visualized in the user interface (#306) with other statistical information about the data)
Stajanovic does not explicitly teach
and in response to receiving the third input, generating a product family co-purchase score visualization based on the insights data, depicting a plurality of association scores between a plurality of pairs of product families, wherein each of the plurality of pairs of product families includes a first product family in a first set of product families and a second product family in a second set of product families, and wherein the product family co-purchase score visualization depicts a respective co-purchase score for each pair of the plurality of pairs of product families,
However, from the same field, Tresser teaches
and in response to receiving the third input, generating a product family co-purchase score visualization based on the insights data, depicting a plurality of association scores between a plurality of pairs of product families, wherein each of the plurality of pairs of product families includes a first product family in a first set of product families and a second product family in a second set of product families(Fig. 4; Par. [0026, 53-4] the graph clustering component (#430) uses a set of graphs corresponding to customers for clustering customers (i.e. customer overlap), or like in the example given, two users with similar graph listening profiles (i.e. a first and second plurality of product families) for improving suggestions for song genre (i.e. product families) sales among other subcategories; Examiner notes that product family co-purchase score is taught as shown below but left here for referential clarity)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the customer graph clustering of Tresser into the data enrichment of Stajanovic. The motivation for this combination would have been to improve customer suggestions by the system as explained in Tresser (Par. [0044]).
Stajanovic and Tresser do not explicitly teach
product family co-purchase score
and wherein the product family co-purchase score visualization depicts a respective co-purchase score for each pair of the plurality of pairs of product families, wherein determining the respective co-purchase score for each pair of the plurality of pairs of product families comprises determining a likelihood that a first product family of the plurality of product families will be purchased together with a second product family of the plurality of product families
However, from the same field, Morales teaches
product family co-purchase score (Par. [0087] similarity score (i.e. co-purchase score) can be calculated for individual or grouped (i.e. family) items)
and wherein the product family co-purchase score visualization depicts a respective co-purchase score for each pair of the plurality of pairs of product families, wherein determining the respective co-purchase score for each pair of the plurality of pairs of product families comprises determining a likelihood that a first product family of the plurality of product families will be purchased together with a second product family of the plurality of product families (Par. [0087] similarity score (i.e. co-purchase score) can be calculated for individual or grouped (i.e. family) items; Examiner notes that Tresser is still being relied on for the depiction)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the group similarity scoring of Morales into the data enrichment of Stajanovic. The motivation for this combination would have been to improve the shopping experience for users as explained in Morales (Par. [0056]).
Regarding claim 2, Stajanovic, Tresser and Morales claim 1 as shown above, and Stajanovic further teaches
The method of claim 1, wherein: the first input comprising an instruction to perform one or more data standardization operations comprises an instruction to perform a data mapping operation; and (Fig. 3A; Col. 2 [Lines 15-26], Col. 11 [Lines 4-46] a user interface (#306) receives user instructions to perform various transform actions (i.e. standardization operations), including data enrichment (i.e. mapping operations))
applying the one or more data standardization operations comprises applying the data mapping operation to the merged data set. (Fig. 3A; Col. 2 [Lines 15-26], Col. 11 [Lines 4-46] a user interface (#306) receives user instructions to perform (i.e. apply) various transform actions (i.e. standardization operations), including data enrichment (i.e. mapping operations))
Regarding claim 3, Stajanovic, Tresser and Morales claim 1 as shown above, and Stajanovic further teaches
The method of claim 2, wherein applying the data mapping operation to the merged data set comprises mapping one or more data objects in the merged data set to a predefined data format. (Col. 5 [Lines 20-44] part of the data preparation involves determining a data source format for being normalized into a format that can be processed by the data enrichment service)
Regarding claim 4, Stajanovic, Tresser and Morales teach claim 2 as shown above, and Stajanovic further teaches
The method of claim 2, wherein: the instruction to perform a data mapping operation comprises a specification of a characteristic of a user-defined data format; and (Fig. 3A; Col. 2 [Lines 15-26], Col. 11 [Lines 4-46] a user interface (#306) receives user instructions to perform various transform actions, including data enrichment (i.e. mapping operations) based on user input)
applying the data mapping operation to the merged data set comprises mapping one or more data objects in the merged data set to the user-defined data format in accordance with the specification of the characteristic of the user-defined data format. (Fig. 3A; Col. 2 [Lines 15-26], Col. 10 [Line 43] - Col. 11 [Line 46] a user interface (#306) receives user instructions to perform (i.e. apply) various transform actions, including data enrichment (i.e. mapping operations) based on user input)
Regarding claim 5, Stajanovic, Tresser and Morales teach claim 4 as shown above, and Stajanovic further teaches
The method of claim 4, wherein mapping one or more data objects in the merged data set to the user-defined data format comprises generating one or both of a wide-form data set and a long-form data set. (Col. 9 [Lines 45-53] data set normalization can include tabular (i.e. wide form) data)
Regarding claim 7, Stajanovic, Tresser and Morales claim 1 as shown above, and Stajanovic further teaches
The method of claim 1, wherein applying the one or more data standardization operations comprises applying one or more join operations to the merged data set. (Col. 11 [Lines 48-67] enrichment operations on the data includes joining data)
Regarding claim 8, Stajanovic, Tresser and Morales teach claim 1 as shown above, and Tresser further teaches
The method of claim 1, wherein generating the merged data set based on the plurality of received data sets comprises applying one or more data cleaning operations one or more data objects in the plurality of received data sets. (Col. 2 [Lines 15-26] this data enrichment service can extract, repair (i.e. clean), and enrich heterogenous datasets)
Regarding claim 11, Stajanovic, Tresser and Morales teach claim 1 as shown above, and Tresser further teaches
The method of claim 1, wherein: the second input comprises a selection of a predefined rule; and (Fig. 3A; Col. 2 [Lines 15-26], Col. 11 [Lines 4-46] a user interface (#306) receives user instructions to perform various transform actions, including selecting how the recommended enrichments are performed (i.e. selection of pre-defined or user-defined rule))
applying the one or more data analytics operation comprises applying the predefined rule. (Fig. 3A; Col. 2 [Lines 15-26], Col. 11 [Lines 4-46] a user interface (#306) receives user instructions to perform (i.e. apply) various transform actions, including selecting how the recommended enrichments are performed (i.e. selection of pre-defined or user-defined rule))
Regarding claim 12, Stajanovic, Tresser and Morales teach claim 1 as shown above, and Tresser further teaches
The method of claim 1, wherein: the second input comprises an instruction to define a user-defined rule; and (Fig. 3A; Col. 2 [Lines 15-26], Col. 11 [Lines 4-46] a user interface (#306) receives user instructions to perform various transform actions, including selecting how the recommended enrichments are performed (i.e. selection of pre-defined or user-defined rule))
applying the one or more data analytics operation comprises applying the user-defined rule. (Fig. 3A; Col. 2 [Lines 15-26], Col. 11 [Lines 4-46] a user interface (#306) receives user instructions to perform (i.e. apply) various transform actions, including selecting how the recommended enrichments are performed (i.e. selection of pre-defined or user-defined rule))
Regarding claim 13, Stajanovic, Tresser and Morales teach claim 1 as shown above, and Tresser further teaches
The method of claim 1, the method further comprising: receiving updated data for one or more of the plurality of data sets; (Col. 2 [Lines 27-38] data sampling for the visualizations are updated in real-time as sources are updated)
in response to receiving the updated data, automatically updating the one or more data visualizations based on the data insights data based on the received updated data. (Col. 2 [Lines 27-38] data sampling for the visualizations are updated in real-time as sources are updated)
Regarding claim 14, Stajanovic, Tresser and Morales teach claim 1 as shown above, and Tresser further teaches
The method of claim 1, the method further comprising: receiving updated data for one or more of the plurality of data sets; (Col. 2 [Lines 27-38] data sampling for the visualizations are updated in real-time as sources are updated)
in response to receiving the updated data, automatically updating the merged data set based on the received updated data. (Col. 2 [Lines 27-38] data sampling for the visualizations are updated in real-time as sources are updated)
Regarding claim 15, Stajanovic, Tresser and Morales teach claim 1 as shown above, and Tresser further teaches
The method of claim 14, wherein receiving the updated data comprises performing a scheduled batch update operation. (Col. 24 [Line 65] - Col. 25 [Line 13] the data updates can be periodic with a refresh rate configurable by the user (i.e. scheduled batch update))
Regarding claim 21, Stajanovic, Tresser and Morales teach claim 1 as shown above, and Tresser further teaches
The method of claim 1, wherein one or more entities in the first set of entities or one or more entities in the second set of entities is selected in accordance with the third user input. (Fig. 3A; Col. 2 [Lines 15-26], Col. 11 [Lines 4-46] a user interface (#306) receives user instructions to perform various transform actions, including selecting how the recommended enrichments are performed)
Regarding claim 22, Stajanovic, Tresser and Morales teach claim 2 as shown above, and Tresser further teaches
The method of claim 21, wherein the third user input indicates a criteria based on which the one or more entities in the first set of entities or one or more entities in the second set of entities is automatically selected. (Fig. 3A; Col. 2 [Lines 15-26], Col. 11 [Lines 4-46] a user interface (#306) receives user instructions to perform various transform actions, including selecting how the recommended enrichments are performed)
Regarding claim 24, Stajanovic, Tresser and Morales teach claim 1 as shown above, and Tresser further teaches
The method of claim 1, wherein each association score is calculated as a respective quotient, wherein each quotient comprises a respective numerator representing an intersection of a respective pair of entities and each quotient comprises a respective denominator representing a union of the respective pair of entities. (Col. 19 Lines [50-63] similarity metric (i.e. association score) is calculated as the ratio of the size of the intersection of a data set to the union of data of the two data sets)
Regarding claim 25, while worded slightly differently, is rejected under the same rationale as claim 26.
Regarding claim 26, while worded slightly differently, is rejected under the same rationale as claim 1. Stajanovic further teaches
A non-transitory computer-readable storage medium storing instructions for data ingestion for a data visualization platform, the instructions configured to be executed by one or more processors (Fig. 11 #1132, #1122)
Claim(s) 6, 9-10, 16-20 and 23 is/are also rejected under 35 U.S.C. 103 as being unpatentable over Stajanovic et al. (US Pat. 10,296,192) in view of Tresser et al. (US Pub. 2007/0255707) and Morales (US Pub. 2011/0093361), and further in view of Dobiesz (US Pub. 2020/0341903).
Regarding claim(s) 6, Stajanovic, Tresser and Morales teach claim 1 as shown above, but do not explicitly teach
The method of claim 1, wherein applying the one or more data standardization operations comprises applying one or more roll-up operations to the merged data set.
However, from the same field Dobiesz teaches
The method of claim 1, wherein applying the one or more data standardization operations comprises applying one or more roll-up operations to the merged data set. (Par. [0062] system rolls up each level of the group by sections)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the metric summarization of Dobiesz into the data enrichment of Stajanovic. The motivation for this combination would have been to improve performance in storage and processing of data, allowing visualizations to be presented and changed more quickly as explained in Dobiesz (Par. [0036]).
Regarding claim(s) 9, Stajanovic, Tresser and Morales teach claim 1 as shown above, but do not explicitly teach
The method of claim 1, wherein generating the merged data set based on the plurality of received data sets comprises applying one or more data aggregation operations to one or more data objects in the plurality of received data sets to join the plurality of data sets.
However, from the same field Dobiesz teaches
The method of claim 1, wherein generating the merged data set based on the plurality of received data sets comprises applying one or more data aggregation operations to one or more data objects in the plurality of received data sets to join the plurality of data sets. (Fig. 15; Par. [0109] a designer can conduct operations on data fields, including how they are summarized (i.e. aggregated))
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the data summarization of Dobiesz into the data enrichment of Stajanovic. The motivation for this combination would have been to improve performance in storage and processing of data, allowing visualizations to be presented and changed more quickly as explained in Dobiesz (Par. [0036]).
Regarding claim(s) 10, Stajanovic, Tresser and Morales teach claim 1 as shown above, but do not explicitly teach
The method of claim 1, wherein applying the one or more data analytics operations comprises applying one or more segmentation operations to segment the merged data set.
However, from the same field Dobiesz teaches
The method of claim 1, wherein applying the one or more data analytics operations comprises applying one or more segmentation operations to segment the merged data set. (Par. [0075] the end user may define a narrowed range of data (i.e. segmentation) for data caching)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the data narrowing of Dobiesz into the data enrichment of Stajanovic. The motivation for this combination would have been to improve performance in storage and processing of data, allowing visualizations to be presented and changed more quickly as explained in Dobiesz (Par. [0036]).
Regarding claim(s) 16, Stajanovic, Tresser and Morales teach claim 1 as shown above, but do not explicitly teach
The method of claim 1, wherein the first set of entities includes one or more product families, one or more products, or one or more SKU's.
However, from the same field Dobiesz teaches
The method of claim 1, wherein the first set of entities includes one or more product families, one or more products, or one or more SKU's. (Par. [0060] a user can select from various product lines (i.e. product families))
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the product lines of Dobiesz into the data enrichment of Stajanovic. The motivation for this combination would have been to improve performance in storage and processing of data, allowing visualizations to be presented and changed more quickly as explained in Dobiesz (Par. [0036]).
Regarding claim(s) 17, Stajanovic, Tresser and Morales teach claim 1 as shown above, but do not explicitly teach
The method of claim 1, wherein the second set of entities includes one or more product families, one or more products, or one or more SKU's.
However, from the same field Dobiesz teaches
The method of claim 1, wherein the second set of entities includes one or more product families, one or more products, or one or more SKU's. (Par. [0060] a user can select from various product lines (i.e. product families))
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the product lines of Dobiesz into the data enrichment of Stajanovic. The motivation for this combination would have been to improve performance in storage and processing of data, allowing visualizations to be presented and changed more quickly as explained in Dobiesz (Par. [0036]).
Regarding claim(s) 18, Stajanovic, Tresser and Morales teach claim 1 as shown above, and Stajanovic further teaches
The method of claim 1, wherein: the first set of entities is depicted in the data visualization along a first axis, (Fig. 6A-7C data is visualized in an x-y fashion)
the second set of entities is depicted in the data visualization along a second axis; (Fig. 6A-7C data is visualized in an x-y fashion)
the first set of entities and the second set of entities define a plurality of intersecting regions in a grid; and (Fig. 6A-7C data is visualized in an x-y fashion as lines (i.e. plurality of regions in a grid))
Stajanovic and Tresser do not explicitly teach each of the plurality of intersection regions includes a respective depiction of a respective one of the association scores.
However, from the same field Dobiesz teaches
each of the plurality of intersection regions includes a respective depiction of a respective one of the association scores. (Fig. 22a-b; Par. [0115] scorecard tabs are shown which include various key metrics (e.g. association scores) are displayed (i.e. depicted))
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the key metric visualization of Dobiesz into the data visualization of Stajanovic. The motivation for this combination would have been to improve performance in storage and processing of data, allowing visualizations to be presented and changed more quickly as explained in Dobiesz (Par. [0036]).
Regarding claim(s) 19, Stajanovic, Tresser and Dobiesz teach claim 18 as shown above, and Dobiesz further teaches
The method of claim 18, wherein the respective depictions include an alphanumeric depiction of the respective association score. (Fig. 22a-b; Par. [0115] scorecard tabs are shown which include various key metrics (e.g. association scores) are displayed (i.e. depicted) and labeled (i.e. alphanumerically depicted))
Regarding claim(s) 20, Stajanovic, Tresser and Dobiesz teach claim 19 as shown above, and Dobiesz further teaches The method of claim 18, wherein the respective depiction includes one or more of a color-coding or a shading determined in accordance with the respective association score. (Fig. 18a-b; Par. [0111] a color tab for data visualization allows a user to specify a color scheme)
Regarding claim(s) 23, Stajanovic, Tresser and Morales teach claim 1 as shown above, but do not explicitly teach
The method of claim 1, wherein each association score is calculated based on one or more of customer overlap and order overlap.
However, from the same field Dobiesz teaches
The method of claim 1, wherein each association score is calculated based on one or more of customer overlap and order overlap. (Par. [0075] the end user (i.e. customer) may define a narrowed range of data for data caching)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the data narrowing of Dobiesz into the data enrichment of Stajanovic. The motivation for this combination would have been to improve performance in storage and processing of data, allowing visualizations to be presented and changed more quickly as explained in Dobiesz (Par. [0036]).
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 J MITCHELL CURRAN whose telephone number is (469)295-9081. The examiner can normally be reached M-F 8:00am - 5:00pm.
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/J MITCHELL CURRAN/Examiner, Art Unit 2161
/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169
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