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 amendment filed 04/15/2026.
Status of the claims
Claims 2-21 were pending, claims 2, 3, 5, 7, 8, 11, 12, 14, 16, 17 20 and 21 have been amended. Therefore, claims 2-21 are currently pending for examination.
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. 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 defining dimensions of data stored in a data source in a hierarchical structure, 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 removes the selection of the first data field and adds a new selection of a second data field from the list, the second data field being a dimension that is a sub-dimension of the first data field in the hierarchical structure;
dynamically identifying a third data field from the list of data fields based on executing predictive logic to identify trends in at least a portion of the 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, the at least one other data field including the third data field; 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. The method of claim 2, wherein the user interface is associated with an analytical tool, and wherein dynamically identifying the third data field from the data fields comprises: executing the predictive logic comprising identifying that a dimension corresponding to the third data field is correlated with the dimension corresponding to the second data field to identify trends in one or more measurements from the data from the data source.
4. 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. The method of claim 2, wherein the data fields of the list are model entities of a data model representing the hierarchical structure comprising the 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 hierarchical structure corresponding to relationships of respective dimensions at the data model.
6. 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. The method of claim 2, 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 data associated with a t least one other data field associated with at least one more dimension of other dimensions of the data from the data source.
8. 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 stored in the data source.
storing the first report in a report repository based on receiving instructions from a user.
9. The method of claim 2, further comprising: storing the first report in a report repository based on receiving instructions from a user.
10. 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. 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 defining dimensions of data stored in a data source in a hierarchical structure, 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 removes the selection of the first data field and adds a new selection of a second data field from the list, the second data field being a dimension that is a sub-dimension of the first data field in the hierarchical structure;
dynamically identifying a third data field from the list of data fields based on executing predictive logic to identify trends in at least a portion of the 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, the at least one other data field including the third data field;
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. The system of claim 11, wherein the user interface is associated with an analytical tool, and wherein dynamically identifying the third data field from the data fields comprises:
executing the predictive logic comprising identifying that a dimension corresponding to the third data field is correlated with the dimension corresponding to the second data field to identify trends in one or more measurements from the data from the data source.
13. 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. The system of claim 11, wherein the data fields of the list are model entities of a data model representing the hierarchical structure comprising the 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 hierarchical structure corresponding to relationships of respective dimensions at the data model.
15. 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. 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 stored in the data source.
18. 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. 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. 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 defining dimensions of data stored in a data source in a hierarchical structure, 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 removes the selection of the first data field and adds a new selection of a second data field from the list, the second data field being a dimension that is a sub-dimension of the first data field in the hierarchical structure;
dynamically identifying a third data field from the list of data fields based on executing predictive logic to identify trends in at least a portion of the 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, the at least one other data field including the third data field; 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. The non-transitory, computer-readable medium of claim 20, wherein the user interface is associated with an analytical tool, and wherein dynamically identifying the third data field from the data fields comprises:
executing the predictive logic comprising identifying that a dimension corresponding to the third data field is correlated with the dimension corresponding to the second data field to identify trends in one or more measurements from the 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 Wang et al. (US 20230113369, hereafter Wang) in view of Hall et al. (US 20230376981, hereafter Hall).
Regarding claim 2, Wang 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 defining dimensions of data stored in a data source in a hierarchical structure, 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 removes the selection of the first data field and adds a new selection of a second data field from the list, the second data field being a dimension that is a sub-dimension of the first data field in the hierarchical structure (Wang [0141; 0144; 0146] discloses: user interfaces may be utilized by a user (e.g., an administrator) to define the organizational structure/hierarchy of the company (e.g., composed of parts such as “Departments” and “Categories”) and Users may utilize the tree to select, view and edit different parts of the organization and to alter the structure of organization; [0161; 0163; 0165] discloses: Edit controls may be provided that enables a user to be added or deleted categories; [0214] discloses: select products from the subset of the product catalog);
dynamically identifying a third data field from the list of data fields based on executing predictive logic to identify trends in at least a portion of the 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, the at least one other data field including the third data field (Wang [0085;088] discloses: to identify trends using historical data (e.g., data published by supplier entities and/or acquirer entities, data acquired from enterprise databases of the entities, data obtained from purchase orders, sales data from remote databases, etc.) to identify trends with respect to the popularity of products and product types may be used and generate forecasts in the purchase or sales of a product or product type, perform prescriptive analytics, identify value drivers that make a given product more desirable (e.g., increases sales and/or enables an increase in prices without reducing sales), identify key segments correlations, identify anomalies, compare and rank sales performs of various items (e.g., where an item is associated with a corresponding SKU (Stock Keeping Unit)), and perform a what-if analysis and recommend to an acquirer what products to acquire (e.g., for resale) in the future); and
Wang didn’t disclose, but Hall discloses: 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).
Wang 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 Wang, 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, Wang as modified discloses: The method of claim 2, wherein the user interface is associated with an analytical tool, and wherein dynamically identifying the third data field from the data fields comprises: executing the predictive logic comprising identifying that a dimension corresponding to the third data field is correlated with the dimension corresponding to the second data field to identify trends in one or more measurements from the data from the data source (Wang[0076; 0079] discloses: an interface may be provided (e.g., via the front end module 104B) that enables queries to be submitted with respect to analytics. [0083] discloses: process and analyze vast amounts of data being published by acquirers and suppliers of products and/or services to identify trends. Examples of trends which may be identified include which products are or are becoming popular (e.g., becoming increasingly offered or purchased over a specified time frame), and which products are unpopular or becoming unpopular (e.g., trending downwards with respect to offers and/or purchases over a specified time frame). Such trend information may be reported to a user ).
Regarding claim 4, Wang 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 (Wang [0158] discloses: the Catalog Structure user interface enables a user to select a product category, and once selected, an edit user interface enables the user to edit product categories and to create new “nested” children product categories. A product catalog section of the user interface lists users (e.g., including a user photograph, name, title, connected accounts) that have access to the selected product category).
Regarding claim 5, Wang as modified discloses: The method of claim 2, wherein the data fields of the list are model entities of a data model representing the hierarchical structure comprising the 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 hierarchical structure corresponding to relationships of respective dimensions at the data model (Wang [0141] discloses: a control that enables navigation to a dashboard, and a hierarchy tree of departments/categories, including departments/categories (e.g., products for the “Home”), and sub-departments/sub-categories (e.g., storage, furniture, etc.); [0146; 0172] discloses: a Department is selected then this section of the user interface may display a table element containing information on order requirements, order product requirements, and/or order product component requirements, including both those that are assigned to and those inherited by that department).
Regarding claim 6, Wang 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 (Wang [0118; 0141] discloses: generate an order via a corresponding user interface provided via the communication/collaboration system which may specify products, quantity, ship date, case pack/cube dimensions, unit price, total price, etc.).
Regarding claim 7, Wang 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 data associated with a least one other data field associated with at least one more dimension of other dimensions of the data from the data source (Wang [0107; 0109] discloses: the prediction system 101 can employ the historical data 115 to generate a correlation between the color of the particular object and its initial starting retail price. The product performance attribute can be substantially similar to the product attribute 114. The prediction system 101 can choose from any particular model 119 to generate a forecast model that draws a correlation between the historical data 115 and the product performance attribute. The prediction system 101 can employ the process 200 to train, test, and validate the predictive model on its ability to forecast one or more product performance attributes based on the historical data 115).
Regarding claim 8, Wang 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 stored in the data source (Wang [0085) discloses: neural network model trained using historical data (e.g., data published by supplier entities and/or acquirer entities, data acquired from enterprise databases of the entities, data obtained from purchase orders, sales data from remote databases, etc.) to identify trends with respect to the popularity of products and product types may be used).
Regarding claim 9, Wang 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 (Wang [0087] discloses: the trend spotting data stored in data store; [0090; 0172] discloses: receives a user instruction to update the data from an enterprise database or other data store).
Regarding claim 10, Wang as modified discloses: The method of claim 2, further comprising: receiving a request to connect to the data source through the user interface (Wang [0186] discloses: user interface that enables a retail user to review product information in depth, request any changes or customizations, and/or negotiate price/terms of an Offer Product. This user interface is configured to enable high-touch, fast paced, communication, collaboration, and negotiation);
generating a data model associated with data from the data source to be used for generating reports (Wang [0083] discloses: The backend processing system 106B is configured to perform computations, data processing, filtering, query servicing, business intelligence, analytics, graph generation, diagram generation, dashboard generation, and report generation) ; and
exposing the list of data fields at the user interface based on the generated data model (Wang [0144] discloses: Users may utilize the tree to select, view and edit different parts of the organization and to alter the structure of organization; [0192] discloses: user interface displays a set of products filtered to match the values that the retail user entered for certain fields (e.g., “Supplier”) in the create order form illustrated in FIG. 4I. The Add Products to Order user interface enables the user to select which products the user wants to add to the order. In this example, the user interface includes distinct sections. A container of orders on the left side of the user interface (That functions similarly to the container of Offers illustrated in FIGS. 4D, 4E. The user can select to view draft orders, pending orders, or approved orders (e.g., via respective tabs). An order search field is provided via which a user can enter an order search query, and a search engine will return and display matching orders (e.g., in ranked order based at least in part on relevance or closeness of match)).
Regarding claim 11, Wang as modified discloses: A system comprising: one or more processors ([0094]); 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 (Wang [0352]):
receiving, at a user interface displaying an initial report generated based on one or more data fields selected from a list of data fields defining dimensions of data stored in a data source in a hierarchical structure, 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 removes the selection of the first data field and adds a new selection of a second data field from the list, the second data field being a dimension that is a sub-dimension of the first data field in the hierarchical structure (Wang [0143; 0144; 0146] discloses: user interfaces may be utilized by a user (e.g., an administrator) to define the organizational structure/hierarchy of the company (e.g., composed of parts such as “Departments” and “Categories”) and Users may utilize the tree to select, view and edit different parts of the organization and to alter the structure of organization; [0161; 0163; 0165] discloses: Edit controls may be provided that enables a user to be added or deleted categories; [0214] discloses: select products from the subset of the product catalog);
dynamically identifying a third data field from the list of data fields based on executing predictive logic to identify trends in at least a portion of the 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, the at least one other data field including the third data field (Wang [0085;088] discloses: to identify trends using historical data (e.g., data published by supplier entities and/or acquirer entities, data acquired from enterprise databases of the entities, data obtained from purchase orders, sales data from remote databases, etc.) to identify trends with respect to the popularity of products and product types may be used and generate forecasts in the purchase or sales of a product or product type, perform prescriptive analytics, identify value drivers that make a given product more desirable (e.g., increases sales and/or enables an increase in prices without reducing sales), identify key segments correlations, identify anomalies, compare and rank sales performs of various items (e.g., where an item is associated with a corresponding SKU (Stock Keeping Unit)), and perform a what-if analysis and recommend to an acquirer what products to acquire (e.g., for resale) in the future); and
Wang didn’t disclose, but Hall discloses: 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).
Wang 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 Wang, 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, Wang as modified discloses: The system of claim 11, wherein the user interface is associated with an analytical tool, and wherein dynamically identifying the third data field from the data fields comprises: executing the predictive logic comprising identifying that a dimension corresponding to the third data field is correlated with the dimension corresponding to the second data field to identify trends in one or more measurements from the data from the data source (Wang[0076; 0079] discloses: an interface may be provided (e.g., via the front end module 104B) that enables queries to be submitted with respect to analytics. [0083] discloses: process and analyze vast amounts of data being published by acquirers and suppliers of products and/or services to identify trends. Examples of trends which may be identified include which products are or are becoming popular (e.g., becoming increasingly offered or purchased over a specified time frame), and which products are unpopular or becoming unpopular (e.g., trending downwards with respect to offers and/or purchases over a specified time frame). Such trend information may be reported to a user ).
Regarding claim 13, Wang 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 (Wang [0007]): 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 (Wang [0158] discloses: the Catalog Structure user interface enables a user to select a product category, and once selected, an edit user interface enables the user to edit product categories and to create new “nested” children product categories. A product catalog section of the user interface lists users (e.g., including a user photograph, name, title, connected accounts) that have access to the selected product category).
Regarding claim 14, Wang as modified discloses: The system of claim 11, wherein the data fields of the list are model entities of a data model representing the hierarchical structure comprising the 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 hierarchical structure corresponding to relationships of respective dimensions at the data model(Wang [0141] discloses: a control that enables navigation to a dashboard, and a hierarchy tree of departments/categories, including departments/categories (e.g., products for the “Home”), and sub-departments/sub-categories (e.g., storage, furniture, etc.); [0146] discloses: a Department is selected then this section of the user interface may display a table element containing information on order requirements, order product requirements, and/or order product component requirements, including both those that are assigned to and those inherited by that department).
Regarding claim 15, Wang 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 (Wang [0118; 0141] discloses: generate an order via a corresponding user interface provided via the communication/collaboration system which may specify products, quantity, ship date, case pack/cube dimensions, unit price, total price, etc.).
Regarding claim 16, Wang as modified discloses: The system of claim 11, wherein dynamically identifying the third data field comprises: 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 data associated with a t least one other data field associated with 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, Wang 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 stored in the data source (Wang [0085) discloses: neural network model trained using historical data (e.g., data published by supplier entities and/or acquirer entities, data acquired from enterprise databases of the entities, data obtained from purchase orders, sales data from remote databases, etc.) to identify trends with respect to the popularity of products and product types may be used).
Regarding claim 18, Wang 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 (Wang [0087] discloses: the trend spotting data stored in data store; [0090; 0172] discloses: receives a user instruction to update the data from an enterprise database or other data store).
Regarding claim 19, Wang 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 (Wang [0186] discloses: user interface that enables a retail user to review product information in depth, request any changes or customizations, and/or negotiate price/terms of an Offer Product. This user interface is configured to enable high-touch, fast paced, communication, collaboration, and negotiation);
generating a data model associated with data from the data source to be used for generating reports (Wang [0083] discloses: The backend processing system 106B is configured to perform computations, data processing, filtering, query servicing, business intelligence, analytics, graph generation, diagram generation, dashboard generation, and report generation) ; and
exposing the list of data fields at the user interface based on the generated data model (Wang [0144] discloses: Users may utilize the tree to select, view and edit different parts of the organization and to alter the structure of organization; [0192] discloses: user interface displays a set of products filtered to match the values that the retail user entered for certain fields (e.g., “Supplier”) in the create order form illustrated in FIG. 4I. The Add Products to Order user interface enables the user to select which products the user wants to add to the order. In this example, the user interface includes distinct sections. A container of orders on the left side of the user interface (That functions similarly to the container of Offers illustrated in FIGS. 4D, 4E. The user can select to view draft orders, pending orders, or approved orders (e.g., via respective tabs). An order search field is provided via which a user can enter an order search query, and a search engine will return and display matching orders (e.g., in ranked order based at least in part on relevance or closeness of match)).
Regarding claim 20, Wang 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 (Wang [0094]):
receiving, at a user interface displaying an initial report generated based on one or more data fields selected from a list of data fields defining dimensions of data stored in a data source in a hierarchical structure, 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 removes the selection of the first data field and adds a new selection of a second data field from the list, the second data field being a dimension that is a sub-dimension of the first data field in the hierarchical structure (Wang [0143; 0144; 0146] discloses: user interfaces may be utilized by a user (e.g., an administrator) to define the organizational structure/hierarchy of the company (e.g., composed of parts such as “Departments” and “Categories”) and Users may utilize the tree to select, view and edit different parts of the organization and to alter the structure of organization; [0161; 0163; 0165] discloses: Edit controls may be provided that enables a user to be added or deleted categories; [0214] discloses: select products from the subset of the product catalog);
dynamically identifying a third data field from the list of data fields based on executing predictive logic to identify trends in at least a portion of the 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, the at least one other data field including the third data field (Wang [0085;088] discloses: to identify trends using historical data (e.g., data published by supplier entities and/or acquirer entities, data acquired from enterprise databases of the entities, data obtained from purchase orders, sales data from remote databases, etc.) to identify trends with respect to the popularity of products and product types may be used and generate forecasts in the purchase or sales of a product or product type, perform prescriptive analytics, identify value drivers that make a given product more desirable (e.g., increases sales and/or enables an increase in prices without reducing sales), identify key segments correlations, identify anomalies, compare and rank sales performs of various items (e.g., where an item is associated with a corresponding SKU (Stock Keeping Unit)), and perform a what-if analysis and recommend to an acquirer what products to acquire (e.g., for resale) in the future); and
Wang didn’t disclose, but Hall discloses: 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).
Wang 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 Wang, 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, Wang as modified discloses: The non-transitory, computer-readable medium of claim 20, wherein the user interface is associated with an analytical tool, and wherein dynamically identifying the third data field from the data fields comprises: executing the predictive logic comprising identifying that a dimension corresponding to the third data field is correlated with the dimension corresponding to the second data field to identify trends in one or more measurements from the data from the data source (Wang[0076; 0079] discloses: an interface may be provided (e.g., via the front end module 104B) that enables queries to be submitted with respect to analytics. [0083] discloses: process and analyze vast amounts of data being published by acquirers and suppliers of products and/or services to identify trends. Examples of trends which may be identified include which products are or are becoming popular (e.g., becoming increasingly offered or purchased over a specified time frame), and which products are unpopular or becoming unpopular (e.g., trending downwards with respect to offers and/or purchases over a specified time frame). Such trend information may be reported to a user ).
Response to Arguments
Regarding claim objections
The examiner withdraws claim objection in light of the amendments.
Regarding Double Patenting
Applicant requested to hold the double patenting rejections. Therefore, the rejection still stand.
Regarding Examiner's Rejections under 35 USC 103
Applicant’s arguments with respect to the rejection(s) of claim(s) under 35 USC 103 have been fully considered in light of the amendments and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Wang.
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.
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/CINDY NGUYEN/Examiner, Art Unit 2156
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