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 .
This is response to preliminary amendment filed 05/16/2025.
Status of the claims
Claims 2-21 are currently pending for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/21/2025 is being considered by the examiner.
Claim Objections
Claims 3, 12 and 21 are objected to because of the following informalities: “an analytical too” at line 1 should replace as “an analytical tool”. Appropriate correction is required.
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 2-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12235834. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 2-21 of instant application recited similar limitations. Therefore, they are rejected on the ground of nonstatutory double patenting.
Instant application
2. (New) A computer-implemented method comprising:
receiving, at a user interface displaying an initial report generated based on one or more data fields selected from a list of data fields associated with a data source, a user selection to modify a selection of a first data field as part of the one or more data fields used for generating the initial report, wherein the user selection modifies the selection of the first data field to a new selection of a sub-dimension of the first data field as a second data field to be used for generating a new report;
dynamically identifying a third data field from the list of data fields based on executing predictive logic to identify trends in data from the data source, wherein the trends are identified between the second data field and at least one other data field from the list of data fields; and
in response to identifying the third data field, presenting, at the user interface, a first report generated based on data associated with the second data field and the third data field and the at least one other data field.
in response to identifying the third data field, selecting the third data field; and
executing the predictive logic comprising identifying the third data field to correspond to a dimension correlated with a dimension corresponding to the second data field to identify trends in one or more measurements from data from the data source.
3. (New) The method of claim 2, wherein the user interface is associated with an analytical too, and wherein dynamically identifying the third data field from the data fields comprises: in response to identifying the third data field, selecting the third data field; and
executing the predictive logic comprising identifying the third data field to correspond to
a dimension correlated with a dimension corresponding to the second data field to identify trends
in one or more measurements from data from the data source.
4. (New) The method of claim 2, wherein the list of data fields comprising data fields corresponding to model entities of a data model for data from the data source, and wherein the method further comprises: updating the user interface to display a new selection of fields from the list of data fields,
wherein the new selection includes the second data field, the third data field, and the at least one
other data field.
5. (New) The method of claim 2, wherein the data fields of the list are model entities of a data model representing dimensions of the data from the data source, wherein the dimensions refer to data objects representing categorical, transactional, and numerical data in the data from the data source, and wherein the list of data fields are presented in a hierarchical structure corresponding to relationships of respective dimensions at the data model.
6. (New) The method of claim 2, wherein the data fields of the list are model entities representing different types of dimensions including generic dimensions, aggregate measures, and time dimensions defined in the data from the data source.
7. (New) The method of claim 2, wherein dynamically identifying the third data field comprises: computing at least one correlation between data associated with the second data field and
at least one more dimension of other dimensions of the data from the data source.
8. (New) The method of claim 2, wherein dynamically identifying the third data field comprises identifying historical data trends in a plurality of stored reports generated based on the data source.
storing the first report in a report repository based on receiving instructions from a user.
9. (New) The method of claim 2, further comprising: storing the first report in a report repository based on receiving instructions from a user.
10. (New) The method of claim 2, further comprising:
receiving a request to connect to the data source through the user interface;
generating a data model associated with data from the data source to be used for generating
reports; and
exposing the list of data fields at the user interface based on the generated data model.
11. (New) A system comprising: one or more computer-readable memories coupled to the one or more processors and having instructions stored thereon that are executable by the one or more processors to perform operations comprising:
receiving, at a user interface displaying an initial report generated based on one or more data fields selected from a list of data fields associated with a data source, a user selection to modify a selection of a first data field as part of the one or more data fields used for generating the initial report, wherein the user selection modifies the selection of the first data field to a new selection of a sub-dimension of the first data field as a second data field to be used for generating a new report;
dynamically identifying a third data field from the list of data fields based on executing predictive logic to identify trends in data from the data source, wherein the trends are identified between the second data field and at least one other data field from the list of data fields;
and
in response to identifying the third data field, presenting, at the user interface, a first report generated based on data associated with the second data field and the third data field and the at least one other data field.
12. (New) The system of claim 11, wherein the user interface is associated with an analytical too, and wherein dynamically identifying the third data field from the data fields comprises:
in response to identifying the third data field, selecting the third data field; and
executing the predictive logic comprising identifying the third data field to correspond to a dimension correlated with a dimension corresponding to the second data field to identify trends in one or more measurements from data from the data source.
13. (New) The system of claim 11, wherein the list of data fields comprising data fields corresponding to model entities of a data model for data from the data source, and wherein the one or more computer-readable memories stores further instructions that are executable by the one or more processors to perform operations comprising:
updating the user interface to display a new selection of fields from the list of data fields, wherein the new selection includes the second data field, the third data field, and the at least one other data field.
14. (New) The system of claim 11, wherein the data fields of the list are model entities of a data model representing dimensions of the data from the data source, wherein the dimensions refer to data objects representing categorical, transactional, and numerical data in the data from the data source, and wherein the list of data fields are presented in a hierarchical structure corresponding to relationships of respective dimensions at the data model.
15. (New) The system of claim 11, wherein the data fields of the list are model entities representing different types of dimensions including generic dimensions, aggregate measures, and time dimensions defined in the data from the data source.
16. (New) The system of claim 11, wherein dynamically identifying the third data field comprises:
17. (New) The system of claim 11, wherein dynamically identifying the third data field comprises identifying historical data trends in a plurality of stored reports generated based on the data source.
18. (New) The system of claim 11, wherein the one or more computer-readable memories stores further instructions that are executable by the one or more processors to perform operations comprising:
storing the generated first report in a report repository based on receiving instructions from
a user.
19. (New) The system of claim 11, wherein the one or more computer-readable memories stores further instructions that are executable by the one or more processors to perform operations comprising: receiving a request to connect to the data source through the user interface;
generating a data model associated with data from the data source to be used for generating
reports; and
exposing the list of data fields at the user interface based on the generated data model.
20. (New) A non-transitory, computer-readable medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving, at a user interface displaying an initial report generated based on one or more data fields selected from a list of data fields associated with a data source, a user selection to modify a selection of a first data field as part of the one or more data fields used for generating the initial report, wherein the user selection modifies the selection of the first data field to a new selection of a sub-dimension of the first data field as a second data field to be used for generating a new report;
dynamically identifying a third data field from the list of data fields based on executing predictive logic to identify trends in data from the data source, wherein the trends are identified between the second data field and at least one other data field from the list of data fields; and
in response to identifying the third data field, presenting, at the user interface, a first report generated based on data associated with the second data field and the third data field and the at least one other data field.
21. (New) The non-transitory, computer-readable medium of claim 20, wherein the user interface is associated with an analytical too, and wherein dynamically identifying the third data field from the data fields comprises:
in response to identifying the third data field, selecting the third data field; and
executing the predictive logic comprising identifying the third data field to correspond to a dimension correlated with a dimension corresponding to the second data field to identify trends in one or more measurements from data from the data source.
U.S. Patent No. 12235834
1. A computer-implemented method comprising: receiving, at an interface associated with an analytical tool, a first selection by a user of a first data field from a list of data fields exposed for report generation, the list of fields associated dimensions of data stored in a data source; in response to receiving the first selection, executing predictive logic to identify trends in data from the data source; identifying, based on the executed predictive logic, i) a first dimension of the data corresponding to the selected first data field and ii) at least one other dimension corresponding to at least one other data field from the list of data fields that was exposed for the report generation and was not selected; identifying a second data field from the at least one other data field from the data fields for report generation, wherein the second data field corresponds to a second dimension correlated with the first dimension to define trends in one or more measurements from the data from the data source; and in response to identifying the second data field, presenting, at the interface associated with the analytical tool, i) a selection of the first and the second data fields from the list of data fields and ii) a first report generated based on data associated with the selected first and second data fields.
2. The computer-implemented method of claim 1, further comprising: updating the interface for generating reports based on the data from the data source, wherein the interface provides the list of data fields comprising data fields corresponding to model entities of a data model for the data from the data source.
3. The computer-implemented method of claim 1, wherein the data fields of the list are model entities of a data model representing dimensions of the data from the data source, wherein the dimensions refer to data objects representing categorical, transactional, and numerical data in the data from the data source, and wherein the list of data fields are presented in a hierarchical structure corresponding to relationships of respective dimensions at the data model.
4. The computer-implemented method of claim 1, wherein the data fields of the list are model entities representing different types of dimensions including generic dimensions, aggregate measures, and time dimensions defined in the data from the data source.
5. The computer-implemented method of claim 1, wherein executing the predictive logic comprises: computing at least one correlation between data associated with the first dimension and at least one more dimension of other dimensions of the data from the data source.
6. The computer-implemented method of claim 1, wherein executing the predictive logic comprises identifying historical data trends identified in a plurality of stored reports generated based on past report generation executed by a plurality of users based on the data source.
7. The computer-implemented method of claim 1, further comprising: storing the generated first report in a report repository based on receiving instructions from the user.
8. The computer-implemented method of claim 1, further comprising: receiving a second user selection at the interface to modify the selection of the first and second data fields from the list of data fields, wherein the second user selection modifies the selection of the second data field to a selection of a sub-dimension of the second data field as a new second data field; and dynamically identifying a third field from the data fields based on executing the predictive logic to identify trends in the data between i) the first data field and the new second data field and ii) other data fields.
9. The computer-implemented method of claim 1, further comprising: receiving a request for connection to the data source through the interface; and generating the data model associated with the data source for data to be used for generating a report.
10. A system comprising: one or more processors; and one or more computer-readable memories coupled to the one or more processors and having instructions stored thereon that are executable by the one or more processors to perform operations comprising: receiving, at an interface associated with an analytical tool, a first selection by a user of a first data field from a list of data fields exposed for report generation, the list of fields associated dimensions of data stored in a data source; in response to receiving the first selection, executing predictive logic to identify trends in data from the data source; identifying, based on the executed predictive logic, i) a first dimension of the data corresponding to the selected first data field and ii) at least one other dimension corresponding to at least one other data field from the list of data fields that was exposed for the report generation and was not selected; identifying a second data field from the at least one other data field from the data fields for report generation, wherein the second data field corresponds to a second dimension correlated with the first dimension to define trends in one or more measurements from the data from the data source; and in response to identifying the second data field, presenting, at the interface associated with the analytical tool, i) a selection of the first and the second data fields from the list of data fields and ii) a first report generated based on data associated with the selected first and second data fields.
11. The system of claim 10, wherein the one or more computer-readable memories store instructions, which when executed cause the one or more processors to perform operations comprising: updating the interface for generating reports based on the data from the data source, wherein the interface provides the list of data fields comprising data fields corresponding to model entities of a data model for the data from the data source.
12. The system of claim 10, wherein the data fields of the list are model entities of a data model representing dimensions of the data from the data source, wherein the dimensions refer to data objects representing categorical, transactional, and numerical data in the data from the data source, and wherein the list of data fields are presented in a hierarchical structure corresponding to relationships of respective dimensions at the data model.
13. The system of claim 10, wherein the data fields of the list are model entities representing different types of dimensions including generic dimensions, aggregate measures, and time dimensions defined in the data from the data source.
14. The system of claim 10, wherein executing the predictive logic comprises: computing at least one correlation between data associated with the first dimension and at least one more dimension of other dimensions of the data from the data source.
15. The system of claim 10, wherein executing the predictive logic comprises identifying historical data trends identified in a plurality of stored reports generated based on past report generation executed by a plurality of users based on the data source.
16. The system of claim 10, wherein the one or more computer-readable memories store instructions, which when executed cause the one or more processors to perform operations comprising: storing the generated first report in a report repository based on receiving instructions from the user.
17. A non-transitory, computer-readable medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving, at an interface associated with an analytical tool, a first selection by a user of a first data field from a list of data fields exposed for report generation, the list of fields associated dimensions of data stored in a data source; in response to receiving the first selection, executing predictive logic to identify trends in data from the data source; identifying, based on the executed predictive logic, i) a first dimension of the data corresponding to the selected first data field and ii) at least one other dimension corresponding to at least one other data field from the list of data fields that was exposed for the report generation and was not selected; identifying a second data field from the at least one other data field from the data fields for report generation, wherein the second data field corresponds to a second dimension correlated with the first dimension to define trends in one or more measurements from the data from the data source; and in response to identifying the second data field, presenting, at the interface associated with the analytical tool, i) a selection of the first and the second data fields from the list of data fields and ii) a first report generated based on data associated with the selected first and second data fields.
18. The non-transitory, computer-readable medium of claim 17, further store instructions, which when executed cause the one or more processors to perform operations comprising: updating the interface for generating reports based on the data from the data source, wherein the interface provides the list of data fields comprising data fields corresponding to model entities of a data model for the data from the data source.
19. The non-transitory, computer-readable medium of claim 17, wherein the data fields of the list are model entities of a data model representing dimensions of the data from the data source, wherein the dimensions refer to data objects representing categorical, transactional, and numerical data in the data from the data source, and wherein the list of data fields are presented in a hierarchical structure corresponding to relationships of respective dimensions at the data model.
20. The non-transitory, computer-readable medium of claim 17, wherein the data fields of the list are model entities representing different types of dimensions including generic dimensions, aggregate measures, and time dimensions defined in the data from the data source.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-21 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 20210342766, hereafter Wu) in view of Hall et al. (US 20230376981, hereafter Hall).
Regarding claim 2, Wu disclose: A computer-implemented method comprising:
receiving, at a user interface displaying an initial report generated based on one or more data fields selected from a list of data fields associated with a data source, a user selection to modify a selection of a first data field as part of the one or more data fields used for generating the initial report, wherein the user selection modifies the selection of the first data field to a new selection of a sub-dimension of the first data field as a second data field to be used for generating a new report (Wu [0043] discloses: the report (as an initial report) includes a contents panel, a data set panel and an editor panel; [0045] discloses: The data set panel 512 lists selectable data sets. Specifically, the data set panel lists selectable attributes 512a and metrics 512b. Attributes 512a generally represent dimensions of the data, such as in time attributes (e.g., year, quarter, month, etc.), geographical dimensions (e.g., country, region, state, etc.), product (category, subcategory, item, etc.); [0046; 0047] discloses: The editor panel 514 allows a user to edit the visualization/report);
Wu didn’t disclose, but Hall discloses: dynamically identifying a third data field from the list of data fields based on executing predictive logic to identify trends in data from the data source, wherein the trends are identified between the second data field and at least one other data field from the list of data fields (Hall [0055; 0110] discloses: The model service 107 can generate and evaluate deviation metrics to determine if the model 119 is under-predictive or over-predictive for one or more types of predictions 125, such as, for example, sales volume, sale trend, and consumer demand. According to one embodiment, the model service 107 generates models 119 such that the models 119 a) account for and evaluate any combination of attributes within a category (e.g., any number of permutations 123), b) generate a prediction 125 on-request or automatically in a virtually instantaneous manner (e.g., as opposed to previous prediction approaches that may require a user to wait weeks or months to develop a product performance forecast). In one or more embodiments, the prediction system 101 captures and updates product data 113 and historical data 115 such that the model service 107 may reuse the categorical features and other information therein to scalably predict sales of new products across one or more product categories); and
in response to identifying the third data field, presenting, at the user interface, a first report generated based on data associated with the second data field and the third data field and the at least one other data field (Hall [0056] discloses: the report service 109 generates an electronic communication including a predicted sales volume for a particular product, an indication of the particular product, and one or more most positively or negatively predictive attributes of the particular product. The report service 109 can transmit the electronic communication to a computing device 102 from which an original prediction request was received).
Wu and Hall are analogous art because they are in the same field of endeavor, for creating business analytic reports. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wu, to include the teaching of Hall, in order for predicting the performance of various products and product attributes. The suggestion to combine is to generate predictions for the performance of new products and/or product attributes.
Regarding claim 3, Wu as modified discloses: The method of claim 2, wherein the user interface is associated with an analytical too, and wherein dynamically identifying the third data field from the data fields comprises: in response to identifying the third data field, selecting the third data field (Wu [0047] discloses: The system will then automatically generate one or more KPI cards 508a. In the exemplary embodiment illustrated in FIG. 6, the KPI 508 includes a plurality of KPI cards 508a arranged in an array. Each KPI card 508a includes a visualization of data based on the metric and/or attributes selected in the editor panel 514For example, referring to FIG. 6, the user has selected the metric 514b “Cost” and the attributes 514c “Break by: Supplier” and “Trend: Month.” Each of the KPI cards 508a displays the attribute (e.g., in this case the name of supplier) 508b and the current value of the metric (e.g., in this case cost) 508c. Additionally, corresponding to the “Trend” attribute, the KPI cards 508a display the value of the metric in a previous time period (e.g., in this case the previous month's cost) 508d and a “Trend Indicator” badge 508e providing a comparison between the current value of the metric and the value of the metric in the previous time period (e.g., in this case displaying the percentage change in cost from the previous month to the current month) ; and
executing the predictive logic comprising identifying the third data field to correspond to a dimension correlated with a dimension corresponding to the second data field to identify trends in one or more measurements from data from the data source (Wu [0047] discloses: Each of the KPI cards 508a displays the attribute (e.g., in this case the name of supplier) 508b and the current value of the metric (e.g., in this case cost) 508c. Additionally, corresponding to the “Trend” attribute, the KPI cards 508a display the value of the metric in a previous time period (e.g., in this case the previous month's cost) 508d and a “Trend Indicator” badge 508e providing a comparison between the current value of the metric and the value of the metric in the previous time period (e.g., in this case displaying the percentage change in cost from the previous month to the current month). Specifically, in each KPI card 508a, the large number is used to show the key metric while the smaller number below is used to show the previous data (e.g., previous month's data). A percentage of previous time unit is provided in the badge 508e. The KPI cards 508a also include a trend area 508h, which includes a graph illustrating the changes (trend) in the metric over a set period of time (e.g., year). The specific attributes and metrics illustrated in FIG. 6 are merely exemplary and any other known metric and/or attributes can use calculated and displayed. For example, for the “Trend” attribute, the user can select day, week, quarter, year, etc. instead of month as illustrated in FIG. 6. Additionally, the “Trend Indicator” badge 508e could display the change in metric (e.g., cost) by total change as opposed to percentage of change).
Regarding claim 4, Wu as modified discloses: The method of claim 2, wherein the list of data fields comprising data fields corresponding to model entities of a data model for data from the data source, and wherein the method further comprises: updating the user interface to display a new selection of fields from the list of data fields, wherein the new selection includes the second data field, the third data field, and the at least one other data field (Hall [0055] discloses: The model service 107 can generate and evaluate deviation metrics to determine if the model 119 is under-predictive or over-predictive for one or more types of predictions 125, such as, for example, sales volume, sale trend, and consumer demand. According to one embodiment, the model service 107 generates models 119 such that the models 119 a) account for and evaluate any combination of attributes within a category (e.g., any number of permutations 123), b) generate a prediction 125 on-request or automatically in a virtually instantaneous manner (e.g., as opposed to previous prediction approaches that may require a user to wait weeks or months to develop a product performance forecast). In one or more embodiments, the prediction system 101 captures and updates product data 113 and historical data 115 such that the model service 107 may reuse the categorical features and other information therein to scalably predict sales of new products across one or more product categories).
Regarding claim 5, Wu as modified discloses: The method of claim 2, wherein the data fields of the list are model entities of a data model representing dimensions of the data from the data source, wherein the dimensions refer to data objects representing categorical, transactional, and numerical data in the data from the data source, and wherein the list of data fields are presented in a hierarchical structure corresponding to relationships of respective dimensions at the data model (Hall [0037; 0059] discloses: Historical data 115 can include any historical product data (e.g., historical product attributes, econometric indicators, and psychographic indicators), historical product sales data, and historical product performance data (e.g., derived from historical product sales data and/or other sources, such as historical reviews, historical accolades, etc.). Non-limiting examples of product performance data include unit and/or revenue sales. The unit and/or revenue sales can be organized by product, by product category and/or subcategory, by time period (e.g., daily, weekly, quarterly, or any suitable period), by channel (e.g., physical retailer, virtual retailer, shopping aggregation services, digital platform, social media account, etc.), by location (e.g., particular address, neighborhood, city, region, state, country, etc.), or combinations thereof. Product categories can include any classification of products, such as, for example, sporting goods, furniture, men's shoes, children's books, hair products, makeup, do-it-yourself projects, and camping gear).
Regarding claim 6, Wu as modified discloses: The method of claim 2, wherein the data fields of the list are model entities representing different types of dimensions including generic dimensions, aggregate measures, and time dimensions defined in the data from the data source (Wu [0045] discloses: The data set panel 512 lists selectable data sets. Specifically, the data set panel lists selectable attributes 512a and metrics 512b. Attributes 512a generally represent dimensions of the data, such as in time attributes (e.g., year, quarter, month, etc.), geographical dimensions (e.g., country, region, state, etc.), product (category, subcategory, item, etc.), etc. Metrics 512b, however, generally represent quantifiable aspects of the data such as revenue, cost, product count, etc.).
Regarding claim 7, Wu as modified discloses: The method of claim 2, wherein dynamically identifying the third data field comprises: computing at least one correlation between data associated with the second data field and at least one more dimension of other dimensions of the data from the data source (Hall [0109] discloses: the model service 107 can generate various correlations between the product data 113 and the one or more product performance attributes. The model service 107, can for example, employ a first predictive model that correlates the likelihood someone will purchase the particular item based on the location in which the particular product is placed in a physical store. In another example, the model service 107 can employ a second predictive model that uses the psychographic indicators 118 to predict the likelihood that the subsequent generation of the particular item will have greater sales than the previous generation).
Regarding claim 8, Wu as modified discloses: The method of claim 2, wherein dynamically identifying the third data field comprises identifying historical data trends in a plurality of stored reports generated based on the data source (Hall [0035; 0061) discloses: Product data 113 and historical data 115 can include values for various macroeconomic indicators and search trends (e.g., values being sampled on a weekly, monthly, daily, or any suitable basis). The macroeconomic indicators and search trend values can be stored in association with additional product data 113 or historical data 115, such as data points associated with a time period, channel, or location corresponding to the data value. The intake service 103 can expand the amount of data gathered around each product attribute 114 (e.g., or other element of product data 113 or historical data 115) by capturing additional information, such as search data around the product attribute or the volume and sentiment of reviews and social media data related to the product attribute).
Regarding claim 9, Wu as modified discloses: The method of claim 2, further comprising: storing the first report in a report repository based on receiving instructions from a user (Wu [0009] discloses: storing instructions is configured for execution by a computer for retrieving a dataset from a database, creating a report including a graphical representation of the dataset, the graphical representation of the dataset including a customizable, responsive visualization of a key performance indicator and displaying the report on a graphical user interface).
Regarding claim 10, Wu as modified discloses: The method of claim 2, further comprising: receiving a request to connect to the data source through the user interface (Wu [0025] discloses: The user engine 202 may include a query input module 216 to accept a plurality of searches, queries or other requests, via a query box or on a graphical user interface (GUI) or another similar interface. The user engine 202 may communicate with an analytical engine 204. The analytical engine 204 may include a set of extensible modules to run a plurality of statistical analyses, to apply filtering criteria, to perform a neural net technique or another technique to condition and treat data extracted from data resources hosted in the system 200, according to a query received from the user engine 202);
generating a data model associated with data from the data source to be used for generating reports (Wu [0035] discloses: generate a quantitative report 210, which may include a table or other output indicating the results 214 extracted from the data storage devices 208a, 208b . . . 208n. The report 210 may be presented to the user via the user engine 202) ; and
exposing the list of data fields at the user interface based on the generated data model (Wu [0037] discloses: the system generates and displays the report based on the selected layout and selected dataset(s). At step 308, the user formats the layout and specific containers included in the layout).
Regarding claim 11, Wu as modified discloses: A system comprising: one or more processors ([0009]); and
one or more computer-readable memories coupled to the one or more processors and having instructions stored thereon that are executable by the one or more processors to perform operations comprising (Wu [0009]):
receiving, at a user interface displaying an initial report generated based on one or more data fields selected from a list of data fields associated with a data source, a user selection to modify a selection of a first data field as part of the one or more data fields used for generating the initial report, wherein the user selection modifies the selection of the first data field to a new selection of a sub-dimension of the first data field as a second data field to be used for generating a new report (Wu [0043] discloses: the report (as an initial report) includes a contents panel, a data set panel and an editor panel; [0045] discloses: The data set panel 512 lists selectable data sets. Specifically, the data set panel lists selectable attributes 512a and metrics 512b. Attributes 512a generally represent dimensions of the data, such as in time attributes (e.g., year, quarter, month, etc.), geographical dimensions (e.g., country, region, state, etc.), product (category, subcategory, item, etc.); [0046; 0047] discloses: The editor panel 514 allows a user to edit the visualization/report);
Wu didn’t disclose, but Hall discloses: dynamically identifying a third data field from the list of data fields based on executing predictive logic to identify trends in data from the data source, wherein the trends are identified between the second data field and at least one other data field from the list of data fields (Hall [0055; 0110] discloses: The model service 107 can generate and evaluate deviation metrics to determine if the model 119 is under-predictive or over-predictive for one or more types of predictions 125, such as, for example, sales volume, sale trend, and consumer demand. According to one embodiment, the model service 107 generates models 119 such that the models 119 a) account for and evaluate any combination of attributes within a category (e.g., any number of permutations 123), b) generate a prediction 125 on-request or automatically in a virtually instantaneous manner (e.g., as opposed to previous prediction approaches that may require a user to wait weeks or months to develop a product performance forecast). In one or more embodiments, the prediction system 101 captures and updates product data 113 and historical data 115 such that the model service 107 may reuse the categorical features and other information therein to scalably predict sales of new products across one or more product categories); and
in response to identifying the third data field, presenting, at the user interface, a first report generated based on data associated with the second data field and the third data field and the at least one other data field (Hall [0056] discloses: the report service 109 generates an electronic communication including a predicted sales volume for a particular product, an indication of the particular product, and one or more most positively or negatively predictive attributes of the particular product. The report service 109 can transmit the electronic communication to a computing device 102 from which an original prediction request was received).
Wu and Hall are analogous art because they are in the same field of endeavor, for creating business analytic reports. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wu, to include the teaching of Hall, in order for predicting the performance of various products and product attributes. The suggestion to combine is to generate predictions for the performance of new products and/or product attributes.
Regarding claim 12, Wu as modified discloses: The system of claim 11, wherein the user interface is associated with an analytical too, and wherein dynamically identifying the third data field from the data fields comprises: in response to identifying the third data field, selecting the third data field (Hall [0032, table 1] discloses: the prediction system may identify trends that are most relevant to particular consumer groups. Demand Average The average of the demand score prediction over a particular interval (e.g., past 6 months, past year, past 3 weeks, or any suitable interval). To the prediction system, the demand average may provide intelligence as to avoiding entering a product trend too early or too late. Demand Growth The growth of the demand score prediction over a particular interval (e.g., past 6 months, past year, past 3 weeks, or any suitable interval). To the prediction system, the demand growth may indicate if a product attribute trend or other fad is increasing, stable, or decreasing. Competition Average How often an attribute appears in product descriptions, on average, over a particular interval (e.g., past 6 months, past year, past 3 weeks, or any suitable interval)); and
executing the predictive logic comprising identifying the third data field to correspond to a dimension correlated with a dimension corresponding to the second data field to identify trends in one or more measurements from data from the data source (Hall [0056] discloses: the report service 109 generates an electronic communication including a predicted sales volume for a particular product, an indication of the particular product, and one or more most positively or negatively predictive attributes of the particular product. The report service 109 can transmit the electronic communication to a computing device 102 from which an original prediction request was received).
Regarding claim 13, Wu as modified discloses: The system of claim 11, wherein the list of data fields comprising data fields corresponding to model entities of a data model for data from the data source, and wherein the one or more computer-readable memories stores further instructions that are executable by the one or more processors to perform operations comprising: updating the user interface to display a new selection of fields from the list of data fields, wherein the new selection includes the second data field, the third data field, and the at least one other data field (Hall [0055] discloses: The model service 107 can generate and evaluate deviation metrics to determine if the model 119 is under-predictive or over-predictive for one or more types of predictions 125, such as, for example, sales volume, sale trend, and consumer demand. According to one embodiment, the model service 107 generates models 119 such that the models 119 a) account for and evaluate any combination of attributes within a category (e.g., any number of permutations 123), b) generate a prediction 125 on-request or automatically in a virtually instantaneous manner (e.g., as opposed to previous prediction approaches that may require a user to wait weeks or months to develop a product performance forecast). In one or more embodiments, the prediction system 101 captures and updates product data 113 and historical data 115 such that the model service 107 may reuse the categorical features and other information therein to scalably predict sales of new products across one or more product categories).
Regarding claim 14, Wu as modified discloses: The system of claim 11, wherein the data fields of the list are model entities of a data model representing dimensions of the data from the data source, wherein the dimensions refer to data objects representing categorical, transactional, and numerical data in the data from the data source, and wherein the list of data fields are presented in a hierarchical structure corresponding to relationships of respective dimensions at the data model (Hall [0037; 0059] discloses: Historical data 115 can include any historical product data (e.g., historical product attributes, econometric indicators, and psychographic indicators), historical product sales data, and historical product performance data (e.g., derived from historical product sales data and/or other sources, such as historical reviews, historical accolades, etc.). Non-limiting examples of product performance data include unit and/or revenue sales. The unit and/or revenue sales can be organized by product, by product category and/or subcategory, by time period (e.g., daily, weekly, quarterly, or any suitable period), by channel (e.g., physical retailer, virtual retailer, shopping aggregation services, digital platform, social media account, etc.), by location (e.g., particular address, neighborhood, city, region, state, country, etc.), or combinations thereof. Product categories can include any classification of products, such as, for example, sporting goods, furniture, men's shoes, children's books, hair products, makeup, do-it-yourself projects, and camping gear).
Regarding claim 15, Wu as modified discloses: The system of claim 11, wherein the data fields of the list are model entities representing different types of dimensions including generic dimensions, aggregate measures, and time dimensions defined in the data from the data source (Wu [0045] discloses: The data set panel 512 lists selectable data sets. Specifically, the data set panel lists selectable attributes 512a and metrics 512b. Attributes 512a generally represent dimensions of the data, such as in time attributes (e.g., year, quarter, month, etc.), geographical dimensions (e.g., country, region, state, etc.), product (category, subcategory, item, etc.), etc. Metrics 512b, however, generally represent quantifiable aspects of the data such as revenue, cost, product count, etc.).
Regarding claim 16, Wu as modified discloses: The system of claim 11, wherein dynamically identifying the third data field comprises: wherein dynamically identifying the third data field comprises:
computing at least one correlation between data associated with the second data field and at least one more dimension of other dimensions of the data from the data source (Hall [0109] discloses: the model service 107 can generate various correlations between the product data 113 and the one or more product performance attributes. The model service 107, can for example, employ a first predictive model that correlates the likelihood someone will purchase the particular item based on the location in which the particular product is placed in a physical store. In another example, the model service 107 can employ a second predictive model that uses the psychographic indicators 118 to predict the likelihood that the subsequent generation of the particular item will have greater sales than the previous generation).
Regarding claim 17, Wu as modified discloses: The system of claim 11, wherein dynamically identifying the third data field comprises identifying historical data trends in a plurality of stored reports generated based on the data source (Hall [0035; 0061) discloses: Product data 113 and historical data 115 can include values for various macroeconomic indicators and search trends (e.g., values being sampled on a weekly, monthly, daily, or any suitable basis). The macroeconomic indicators and search trend values can be stored in association with additional product data 113 or historical data 115, such as data points associated with a time period, channel, or location corresponding to the data value. The intake service 103 can expand the amount of data gathered around each product attribute 114 (e.g., or other element of product data 113 or historical data 115) by capturing additional information, such as search data around the product attribute or the volume and sentiment of reviews and social media data related to the product attribute).
Regarding claim 18, Wu as modified discloses: The system of claim 11, wherein the one or more computer-readable memories stores further instructions that are executable by the one or more processors to perform operations comprising: storing the generated first report in a report repository based on receiving instructions from a user(Wu [0009] discloses: storing instructions is configured for execution by a computer for retrieving a dataset from a database, creating a report including a graphical representation of the dataset, the graphical representation of the dataset including a customizable, responsive visualization of a key performance indicator and displaying the report on a graphical user interface).
Regarding claim 19, Wu as modified discloses: The system of claim 11, wherein the one or more computer-readable memories stores further instructions that are executable by the one or more processors to perform operations comprising: receiving a request to connect to the data source through the user interface (Wu [0025] discloses: The user engine 202 may include a query input module 216 to accept a plurality of searches, queries or other requests, via a query box or on a graphical user interface (GUI) or another similar interface. The user engine 202 may communicate with an analytical engine 204. The analytical engine 204 may include a set of extensible modules to run a plurality of statistical analyses, to apply filtering criteria, to perform a neural net technique or another technique to condition and treat data extracted from data resources hosted in the system 200, according to a query received from the user engine 202);
generating a data model associated with data from the data source to be used for generating reports(Wu [0035] discloses: generate a quantitative report 210, which may include a table or other output indicating the results 214 extracted from the data storage devices 208a, 208b . . . 208n. The report 210 may be presented to the user via the user engine 202) ; and
exposing the list of data fields at the user interface based on the generated data model (Wu [0037] discloses: the system generates and displays the report based on the selected layout and selected dataset(s). At step 308, the user formats the layout and specific containers included in the layout).
Regarding claim 20, Wu as modified discloses: A non-transitory, computer-readable medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising (Wu [0009]):
receiving, at a user interface displaying an initial report generated based on one or more data fields selected from a list of data fields associated with a data source, a user selection to modify a selection of a first data field as part of the one or more data fields used for generating the initial report, wherein the user selection modifies the selection of the first data field to a new selection of a sub-dimension of the first data field as a second data field to be used for generating a new report (Wu [0043] discloses: the report (as an initial report) includes a contents panel, a data set panel and an editor panel; [0045] discloses: The data set panel 512 lists selectable data sets. Specifically, the data set panel lists selectable attributes 512a and metrics 512b. Attributes 512a generally represent dimensions of the data, such as in time attributes (e.g., year, quarter, month, etc.), geographical dimensions (e.g., country, region, state, etc.), product (category, subcategory, item, etc.); [0046; 0047] discloses: The editor panel 514 allows a user to edit the visualization/report);
Wu didn’t disclose, but Hall discloses: dynamically identifying a third data field from the list of data fields based on executing predictive logic to identify trends in data from the data source, wherein the trends are identified between the second data field and at least one other data field from the list of data fields (Hall [0055; 0110] discloses: The model service 107 can generate and evaluate deviation metrics to determine if the model 119 is under-predictive or over-predictive for one or more types of predictions 125, such as, for example, sales volume, sale trend, and consumer demand. According to one embodiment, the model service 107 generates models 119 such that the models 119 a) account for and evaluate any combination of attributes within a category (e.g., any number of permutations 123), b) generate a prediction 125 on-request or automatically in a virtually instantaneous manner (e.g., as opposed to previous prediction approaches that may require a user to wait weeks or months to develop a product performance forecast). In one or more embodiments, the prediction system 101 captures and updates product data 113 and historical data 115 such that the model service 107 may reuse the categorical features and other information therein to scalably predict sales of new products across one or more product categories); and
in response to identifying the third data field, presenting, at the user interface, a first report generated based on data associated with the second data field and the third data field and the at least one other data field (Hall [0056] discloses: the report service 109 generates an electronic communication including a predicted sales volume for a particular product, an indication of the particular product, and one or more most positively or negatively predictive attributes of the particular product. The report service 109 can transmit the electronic communication to a computing device 102 from which an original prediction request was received).
Wu and Hall are analogous art because they are in the same field of endeavor, for creating business analytic reports. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wu, to include the teaching of Hall, in order for predicting the performance of various products and product attributes. The suggestion to combine is to generate predictions for the performance of new products and/or product attributes.
Regarding claim 21, Wu as modified discloses: The non-transitory, computer-readable medium of claim 20, wherein the user interface is associated with an analytical too, and wherein dynamically identifying the third data field from the data fields comprises: in response to identifying the third data field, selecting the third data field (Hall [0032, table 1] discloses: the prediction system may identify trends that are most relevant to particular consumer groups. Demand Average The average of the demand score prediction over a particular interval (e.g., past 6 months, past year, past 3 weeks, or any suitable interval). To the prediction system, the demand average may provide intelligence as to avoiding entering a product trend too early or too late. Demand Growth The growth of the demand score prediction over a particular interval (e.g., past 6 months, past year, past 3 weeks, or any suitable interval). To the prediction system, the demand growth may indicate if a product attribute trend or other fad is increasing, stable, or decreasing. Competition Average How often an attribute appears in product descriptions, on average, over a particular interval (e.g., past 6 months, past year, past 3 weeks, or any suitable interval)); and
executing the predictive logic comprising identifying the third data field to correspond to a dimension correlated with a dimension corresponding to the second data field to identify trends in one or more measurements from data from the data source (Hall [0056] discloses: the report service 109 generates an electronic communication including a predicted sales volume for a particular product, an indication of the particular product, and one or more most positively or negatively predictive attributes of the particular product. The report service 109 can transmit the electronic communication to a computing device 102 from which an original prediction request was received).
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/CINDY NGUYEN/Examiner, Art Unit 2156