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Patent Application 18528520 - GENERATION OF RECOMMENDATIONS FROM - Rejection

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Patent Application 18528520 - GENERATION OF RECOMMENDATIONS FROM

Title: GENERATION OF RECOMMENDATIONS FROM DYNAMICALLY-MAPPED DATA

Application Information

  • Invention Title: GENERATION OF RECOMMENDATIONS FROM DYNAMICALLY-MAPPED DATA
  • Application Number: 18528520
  • Submission Date: 2025-04-10T00:00:00.000Z
  • Effective Filing Date: 2023-12-04T00:00:00.000Z
  • Filing Date: 2023-12-04T00:00:00.000Z
  • National Class: 705
  • National Sub-Class: 026700
  • Examiner Employee Number: 94688
  • Art Unit: 2162
  • Tech Center: 2100

Rejection Summary

  • 102 Rejections: 0
  • 103 Rejections: 2

Cited Patents

The following patents were cited in the rejection:

Office Action Text


    DETAILED ACTION
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending in this office correspondence.

Drawings
The Drawings filed on 12/04/2023, have been acknowledged.

Information Disclosure Statement
The information disclosure statement (IDS)s submitted on 1/17/2024 and 6/5/2024 are in compliance with the provisions of 37 CFR 1.97.  Accordingly, the information disclosure statement is being considered by the examiner.

Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.

Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Independent claim 20 recites the following: “A non-transitory computer-readable storage medium having instructions encoded thereon which, when executed by a processing device, cause the processing device to: …”
The aforementioned recited claim language of - “A non-transitory computer-readable medium …”, wherein the instant application specification references this “non-transitory computer-readable medium” in paragraph [0076] as follows: “The data storage device 320 may include a computer-readable storage medium 324 (e.g., a non-transitory computer-readable storage medium) on which is stored one or more sets of instructions 326 (e.g., software) embodying any one or more of the methodologies or functions described herein.” 
However, when the instant application specification discusses the structure of this this “non-transitory computer-readable medium”, the following is recited in paragraph [0029]: “The software code can be stored as non-transitory instructions on any type of tangible computer-readable storage medium (referred to herein as a "non-transitory computer-readable storage medium"). Examples of suitable media include random access memory (RAM), read-only memory (ROM), magnetic media such as a hard-drive or a floppy disk, or …” (Emphasis Added).  
As recited above, even though the specification discusses this “non-transitory computer-readable medium” but when the specification tries to ascertain the meets and bounds of the “non-transitory computer-readable medium”, instead the specification references some “suitable media” hardware structures rather than referencing this “non-transitory computer-readable medium”.  The specification seems lacking any further definition of this “computer-readable medium” except for this aforementioned media references, which leaves the examiner unclear of the meets and bounds of this “computer-readable medium.”
Furthermore, and consistent with the well-established axiom in patent law that a patentee or applicant is free to be his or her own lexicographer, a patentee or applicant may use terms in a manner contrary to or inconsistent with one or more of their ordinary meanings, see MPEP 2173.05(a).  Therefore, the term “non-transitory” is not sufficient to render a claim statutory, it must be supported by the specification.  Accordingly, the claimed language is not supported by the specification.    
Non-limiting examples of claims that are not directed to any of the statutory categories include: Products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations, see MPEP 2106.03. Software per se is not patentable subject matter.  Accordingly, the aforementioned claim(s) is/are rejected as non-statutory for failing to disclose such hardware.  
Appropriate correction is required.

Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to abstract idea without significantly more. 
Step 1: The aforementioned claims are directed to a system and a process. The claimed system processes deal with generating recommendations from dynamically-mapped data.  A database system receives a first request to generate a recommendation objection and a second request to retrieve additional data to include in the recommendation object.  Then, the system retrieves the recommendation data from a first database table and identifies the additional data in a second database table that is stored separately from the first database table.  The database system generates the recommendation object to include the recommendation data from the first database, and maps the additional data to one or more fields of the recommendation object.
Step 2A – Prong One – The claims recite an abstract idea 
Independent claim 1 (and similarly claims 14 and 20) recites: “generating, by the processing device, one or more queries based on the second request”, which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components.  That is, other than reciting “computer”, “processing device” or “memory device”, nothing in the claim element precludes the steps from practically being performed in a human mind.  For example, and given some information at hand, a person is mentally (or with the aid of pen and paper) capable of evaluating information at hand and producing a request/query based on this information, which is a mental process. 
Furthermore, the claim recites the following steps: “identifying, by the processing device, the additional data in a second database table that is stored separately from the first database table”, which is again a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components.  That is, other than reciting “computer”, “processing device” or “memory device”, nothing in the claim element precludes the steps from practically being performed in a human mind.  For example, and given some information at hand, a person is mentally (or with the aid of pen and paper) capable of evaluating information at hand to identify a set of information of interest from this information, which is again a mental process. 
Additionally, the aforementioned claim recites the following steps: “identifying, by the processing device, the additional data in a second database table that is stored separately from the first database table”, which is again a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components.  That is, other than reciting “computer”, “processing device” or “memory device”, nothing in the claim element precludes the steps from practically being performed in a human mind.  For example, and given some information at hand, a person is mentally (or with the aid of pen and paper) capable of evaluating information at hand to identify a set of information of interest from this information, which is again a mental process.
Furthermore, the aforementioned claim recites the following steps: “mapping, by the processing device, the additional data to one or more fields of the recommendation object”, which is again a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components.  That is, other than reciting “computer”, “processing device” or “memory device”, nothing in the claim element precludes the steps from practically being performed in a human mind.  For example, and given some information at hand, a person is mentally (or with the aid of pen and paper) capable of evaluating this information at hand to associate this information against another set of information, which is again a mental process.
As explained above, a process of “generating, …, one or more queries”, “identifying, …, the additional data …”, and “mapping, …, the additional data …” are nothing more than an abstract idea.  Consequently, if a claim limitation, under its broadest reasonable interpretation, covers an abstract idea that includes a series of steps that recite mental steps, but for the recitation of generic computer components, then it falls within the “Mental Processes” and grouping of “Abstract Ideas”.  Accordingly, the aforementioned claim(s) recite abstract ideas.
Step 2A – Prong Two - The abstract idea is not integrated into a practical application
This judicial exception is not integrated into a practical application. In particular, the aforementioned claims recite the additional limitation of – “receiving, by a processing device of a database system, a first request to generate a recommendation object”, which is considered to be an insignificant extra-solution activity of mere data gathering steps, for which an extra-solution activity includes both pre-solution and post-solution activity.  For example, the courts have decided that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea, does not integrate a judicial exception into a practical application or provide significantly more, see MPEP 2106.05(f) and MPEP 2106.05(g).
Furthermore, the aforementioned claims recite the additional limitation – “retrieving, by the processing device, recommendation data from a first database table to include in the recommendation object”, which is considered to be an insignificant extra-solution activity of mere data gathering step. As explained above an extra-solution activity includes both pre-solution and post-solution activity and does not integrate a judicial exception into a practical application or provide significantly more, which is considered to be an insignificant extra-solution activity of mere data gathering step for obtaining information, which is considered to be data-gathering insignificant extra-solution activities to the judicial exception, see MPEP 2106.05(f) and MPEP 2106.05(g).
Additionally, the aforementioned claims recite the additional limitation – “receiving, by the processing device, a second request to retrieve additional data to include in the recommendation object, …”, which is considered to be an insignificant extra-solution activity of mere data gathering step. As explained above an extra-solution activity includes both pre-solution and post-solution activity and does not integrate a judicial exception into a practical application or provide significantly more, see MPEP 2106.05(f) and MPEP 2106.05(g).
Furthermore, the aforementioned claims recite the additional limitation – “providing, in the GUI to a device, the GUI providing functionality to visually insert, edit, and connect nodes representative of a plurality of operations via one or more connectors without requiring a user to write code depicted visually as interconnected nodes for which connectivity of the nodes is modifiable, wherein one or more of the nodes is associated with a load operation of the recommendation data and the additional data.”  The recited claim language of “providing … a load operation of the recommendation data…”, is/are considered to be an insignificant extra-solution activities of mere data transmission steps to user to deal with information for a later use. As explained above an extra-solution activity includes both pre-solution and post-solution activity and does not integrate a judicial exception into a practical application or provide significantly more, see MPEP 2106.05(f) and MPEP 2106.05(g).
Additionally, the aforementioned claims recite the additional limitation – “receiving the process flow from the device resulting from interactions received by the GUI”, which is again considered to be an insignificant extra-solution activity of mere data gathering step. As explained above an extra-solution activity includes both pre-solution and post-solution activity and does not integrate a judicial exception into a practical application or provide significantly more, see MPEP 2106.05(f) and MPEP 2106.05(g).
Finally, the aforementioned claims recite the additional limitation – “generating, by the processing device, the recommendation object to include the recommendation data from the first database table.”  The recited language of “generating … the recommendation object”, in the context of the claim language, amounts to mere application of a step in a process to further transmit information, which is considered an insignificant extra-solution activity of data transmission step, which is considered an extra-solution activity to the judicial exception.  As explained above an extra-solution activity includes both pre-solution and post-solution activity and does not integrate a judicial exception into a practical application or provide significantly more, see MPEP 2106.05(f) and MPEP 2106.05(g).
The additional elements recited in the aforementioned claim(s) are: “computer”, “processing device” and/or “memory device”.  The additional elements of using a computer, storage device(s) and processor(s) to obtain information, analyze information, and manipulate information amounts to no more than mere instructions to apply the exception using a generic computer components.  Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.  The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f).
The additional element of using a computer to obtain information and/or present information, amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.  The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Step 2B: 
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception.  The insignificant extra-solution activities identified above, which include the data-gathering steps: (“receiving, …, a first request …”, “retrieving, …, recommendation data …”,  “receiving, …, a second request …”, and “receiving the process flow from the device …”); and data-transmission steps (“providing, …, wherein one or more of the nodes is associated with a load operation …” and “generating, …, the recommendation object …”) are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); (v) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).  
Additionally, the computer, storage device(s) and/or processor(s) are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and cannot provide an inventive concept.
Thus, there are no additional elements that amount to significantly more than the above-identified judicial exception (the abstract idea).  Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that any combination of elements improves the functioning of a computer or improves any other technology. 
The claim(s) is not patent eligible.

Claim 2 is dependent on claim 1 and includes all the limitations of claim 1.  Further, the aforementioned claim recites the additional limitation of “mapping the additional data comprises generating structured query language (SQL) statements to retrieve the additional data of the second database table.” The recited language of “mapping” amounts to mere instruction to apply step in a process using generic computing.  Further, and given the broadest reasonable interpretation, the recited step of “generating the revised version of the input script by matching a script identifier corresponding to the input script data …”, which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components.  For example, a person is capable of information evaluation and then associating this information with another set of information, which is again a mere mental process.
Further, the aforementioned claim recites the following: “the recommendation object is hydrated with the additional data at runtime.” This recited claim language of “hydrated with the additional data at runtime” is considered an insignificant extra solution activity of data gathering, which does not integrate a judicial exception into a practical application or provide significantly more, see MPEP 2106.05(f) and MPEP 2106.05(g).
Claim 3 is dependent on claim 2 and includes all the limitations of claim 2.  Further, the aforementioned claim recites the additional limitation of “the additional data is stored as a structured data object, and the mapping of the additional data is agnostic to a data hierarchy of the structured data object.” This claim language merely reciting an abstract idea of information specification, which is again a mere mental process.  
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1.  Further, the aforementioned claim recites the additional limitation of “wherein the additional data is mapped to the recommendation object without duplicating the additional data in the first database table.” The recited language of “data is mapped to the recommendation object …”, and given the broadest reasonable interpretation, recite mere mental steps to evaluate information at hand along with another set of information and produce an outcome “without duplicating”, which is again a mere mental process.  
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1.  Further, the aforementioned claim recites the additional limitation of “generating for presentation, by the processing device, the GUI which visually represents retrieval of the recommendation data, retrieval of additional data for inclusion in the recommendation object, mapping of the additional data, and generation of the recommendation object as a process flow.” The recited language of “retrieval of additional data for inclusion in the recommendation object …”, and given the broadest reasonable interpretation, is considered to be an insignificant extra-solution activity of mere data gathering steps which does not integrate a judicial exception into a practical application or provide significantly more, see MPEP 2106.05(f) and MPEP 2106.05(g).  
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1.  Further, the aforementioned claim recites the additional limitation of “wherein each of the retrieval of the recommendation data, the retrieval of additional data for inclusion in the recommendation object, the mapping of the additional data, and the generation of the recommendation object is visually as associated with a node within the process flow.” The recited language of “is visually as associated with a node within the process flow …”, which is considered to be insignificant extra-solution activities to the judicial exception, for which an extra-solution activity includes both pre-solution and post-solution activity, see MPEP 2106.05(g).
Claim 7 is dependent on claim 6 and includes all the limitations of claim 6.  The claim further recites the additional limitations of “wherein the mapping of the additional data is visually represented as a map node which links nodes representative of the retrieval of the additional data and the generation of the recommendation object.”  At this step, the additional limitation of “visually represented as a map node which links nodes representative of the retrieval …” is considered to be insignificant extra-solution activities to the judicial exception, for which an extra-solution activity includes both pre-solution and post-solution activity, see MPEP 2106.05(g).
Claim 8 is dependent on claim 1 and includes all the limitations of claim 1.  The claim further recites the additional limitations of “wherein the recommendation object is based on an up next recommendation”, which recites a mere mental process of an abstract idea to define an association between two sets of information, which is again a mere mental process of an abstract idea.
Claim 9 is dependent on claim 1 and includes all the limitations of claim 1.  The claim further recites the additional limitations of “wherein the recommendation data comprises a customer relationship management (CRM) record.”  At this step, the additional limitation of “a customer relationship management (CRM) …” is considered to be insignificant extra-solution activities that recites mere instruction of well-understood, routine and conventional in the art, see MPEP 2106.05(d)(II).
Claim 10 is dependent on claim 1 and includes all the limitations of claim 1.  The claim further recites the additional limitations of “wherein the additional data comprises scheduling information”, which recites a mere mental process of an abstract idea to define an association between two sets of information, which is again a mere mental process of an abstract idea.
Claim 11 is dependent on claim 1 and includes all the limitations of claim 1.  The claim further recites the additional limitations of “wherein the additional data in the second database table is mapped to the recommendation object”, which recites a mere mental process of an abstract idea to define an association between two sets of information, which is again a mere mental process of an abstract idea.
Claim 12 is dependent on claim 1 and includes all the limitations of claim 1.  The claim further recites the additional limitations of “wherein the additional data in a second database table is mapped to the one or more fields via a schema”, which recites a mere mental process of an abstract idea to define an association between two sets of information, which is again a mere mental process of an abstract idea.
Claim 13 is dependent on claim 1 and includes all the limitations of claim 1.  The claim further recites the additional limitations of “wherein the additional data comprises a prompt to take an action.”  At this step, the additional limitation of “prompt to take an action” is considered to be data-gathering insignificant extra-solution activities to the judicial exception, for which an extra-solution activity includes both pre-solution and post-solution activity, see MPEP 2106.05(g).

Independent claims 14 and 20 recite similar limitations to claim 1, and therefore rejected for the same reasons as explained above.  
Further, dependent claims 15-19 recite similar limitations to claims 2-13, and therefore rejected for the same reasons as explained above.
Therefore, the aforementioned claims are not patent eligible.

Claim Rejections - 35 USC § 103
  In the event the determination of the status of the application as subject to AIA  35 U.S.C. 102 and 103 (or as subject to pre-AIA  35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA  to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.  
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.

This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary.  Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.

Claims 1-3 and 5-20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication (US 2017/0236131 A1) issued to NATHENSON (hereinafter as “NATHENSON”), and in view of US Patent (US 8,433,705 B1) issued to Dredze et al. (hereinafter as “DREDZE”).
Regarding claim 1, NATHENSON teaches a computer-implemented method comprising: 
receiving, by a processing device of a database system, a first request to generate a recommendation object (NATHENSON Para. [0020]: “…, the invention is directed to a system for generating a recommendation of a product for a customer or a suggested action for the customer to take, and for providing guidance to a customer service representative regarding the presentation of the recommendation or suggested action to the customer, ...”; and
Para. [0024]: “…, access data representing a customer's interactions with the organization from the database or data store; [0025] process the accessed data, including implementing a decision process to generate the recommendation or the suggested action; [0026] generate a workflow or process for interacting with the customer to enable the organization's representative to present the recommendation or suggested action to the customer”; and
Para. [0050]: “Embodiments of the inventive system and methods employ various data processing and analysis techniques to generate recommended actions for companies and for their employees (such as customer service or sales representatives) when interacting with customers.”, 
the examiner notes that the reference discloses system and methods for generating a recommendation of product or action to a customer based on a customer interactions wherein the reference discloses in Fig. 4 a process flow taking input data to generate recommendation, to that of receiving a request to generate recommendation object); 
retrieving, by the processing device, recommendation data from a first database table to include in the recommendation object (NATHENSON Para. [0020]: “…, the invention is directed to a system for generating a recommendation of a product for a customer or a suggested action for the customer to take, and for providing guidance to a customer service representative regarding the presentation of the recommendation or suggested action to the customer, where the system includes: [0021] a database or data store containing a plurality of records, the plurality of records including records corresponding to customer interactions with an organization providing the products, and records corresponding to the business operations of the organization; [0022] a processor programmed with a set of instructions, wherein when executed by the processor, the instructions cause the system to [0023] access data representing a status of an aspect of the organization's business operations from the database or data store; [0024] access data representing a customer's interactions with the organization from the database or data store; [0025] process the accessed data, including implementing a decision process to generate the recommendation or the suggested action”, 
the examiner notes that the reference accesses the customer interactive data stored in database records to generate recommendation to that of retrieving recommendation data from a first database table to include in the recommendation object);
generating, by the processing device, one or more queries based on the second request (NATHENSON Fig. 4, Para. [0125]: “Once one or more recommended actions have been identified or generated, the process illustrated in FIG. 4 may determine if one or more of those actions may be implemented by an automated process (as suggested by step or stage 412). If an automated execution is possible (as indicated by the “Yes” branch of step or stage 412), then the process may initiate a workflow to execute that action (as suggested by step or stage 414). Such actions may include, but are not limited to, generating a message for delivery by text or email, contacting a service representative and requesting that certain information be provided to the customer, providing/shipping a sample of a new product to the customer, automatically processing an adjustment to a customer's loyalty or credit account, etc.”); 
identifying, by the processing device, the additional data in a second database table that is stored separately from the first database table (NATHENSON Para. [0017]: “Embodiments of the inventive system, and methods provide the ability to access and process real-time data, such as customer data (e.g., purchase history, browsing history, inquiry history, etc.), inventory data (current levels, in-shipment amounts, in-transit locations, etc.), … to provide an integrated shopping experience for end users, such as a vendor's customers. “; and
Fig. 4, Para. [0121]: “…, the sources of data that are processed in order to identify possible actions and generate recommendations may include product data (element 408) and supply chain data (element 410). This data may be obtained from the underlying database that contains the information reflecting the operational status of a business. Note that other sources of data may also be accessed and processed (e.g., sales/CRM data, HR data, loyalty group data, financial data, etc.) as part of generating a recommendation”; and
Para. [0141]: “…, inputs to the data analysis and decision processes may include customer data, product data, supply chain data, or financial operating data, among others. One aspect of the inventive system and methods is to not only leverage the techniques to generate recommendations, but to do so in a manner which ranks possible suggestions by taking into account the predicted outcomes and ordering them according to a rule or heuristic (such as by the likelihood of success in producing a desired outcome).”, 
the examiner notes that product data or supply chain data are additional data); 
generating, by the processing device, the recommendation object to include the recommendation data from the first database table (NATHENSON Para. [0046]: “The recommendations may include not only products that are expected to be of interest to a customer, but also “hints” or a suggested workflow for the service representative that are intended to increase the likelihood of the customer making a purchase or engaging in another desired action.”; and 
Para. [0052]: “The recommendation(s) may balance product recommendations (data not requested by the shopper) with product data or other information actively requested by the shopper”; and
Fig. 4, Para. [0125]: “Once one or more recommended actions have been identified or generated, the process illustrated in FIG. 4 may determine if one or more of those actions may be implemented by an automated process (as suggested by step or stage 412). If an automated execution is possible (as indicated by the “Yes” branch of step or stage 412), then the process may initiate a workflow to execute that action (as suggested by step or stage 414). Such actions may include, but are not limited to, generating a message for delivery by text or email, contacting a service representative and requesting that certain information be provided to the customer, providing/shipping a sample of a new product to the customer, automatically processing an adjustment to a customer's loyalty or credit account, etc.”).
mapping, by the processing device, the additional data to one or more fields of the recommendation object (NATHENSON Para. [0049]: “… provide the capability to access and process real-time data, including customer related data (e.g., purchase history, browsing history, inquiry history, etc.), inventory data (current levels, in-shipment amounts, in-transit locations, etc.), product margin data (and other financial data, such as sales levels, sales trajectories, revenue, etc.), aggregated customer behavioral data (such as identifying strong influencers, collaborative filtering based associations or correlations, statistical analysis, sentiment analysis, or machine learning to develop models of the relevant factors in determining a desired action, etc.), and web-site system data (e.g., page load time, content load order, complexity of content, and other potential tradeoffs between content selection, delivery method, and performance in order to increase conversion rates and customer satisfaction), to provide an integrated shopping experience for end users, such as a vendor's customers”; and 
Para. [0050]: “..., employ various data processing and analysis techniques to generate recommended actions for companies and for their employees (such as customer service or sales representatives) when interacting with customers… The database is specifically designed and constructed to serve as a primary source of information regarding the operational status of a company as well as information regarding previous or planned interactions with customers or prospective customers. The customer-related records may include records of contacts, previous browsing and/or purchasing behavior, features accessed on an eCommerce web-site, loyalty program participation, social network behavior, etc.”; and
Para. [0051]: “…, utilize a record structure that associates each product or service on an eCommerce web-site with its own data record”; and
Para. [0052]: “The recommendation(s) may balance product recommendations (data not requested by the shopper) with product data or other information actively requested by the shopper”; and
Fig. 3, Para. [0098]: “The data storage layer 320 may include one or more data objects 322 each having one or more data object components 321, such as attributes and/or behaviors. For example, the data objects may correspond to tables of a relational database, and the data object components may correspond to columns or fields of such tables. Alternatively, or in addition, the data objects may correspond to data records having fields and associated services.”).

Although NATHENSON teaches receiving, by the processing device, a second request to retrieve additional data to include in the recommendation object (NATHENSON Para. [0017]: “Embodiments of the inventive system, and methods provide the ability to access and process real-time data, such as customer data (e.g., purchase history, browsing history, inquiry history, etc.), inventory data (current levels, in-shipment amounts, in-transit locations, etc.), … to provide an integrated shopping experience for end users, such as a vendor's customers. “; and
Fig. 4, Para. [0121]: “…, the sources of data that are processed in order to identify possible actions and generate recommendations may include product data (element 408) and supply chain data (element 410). This data may be obtained from the underlying database that contains the information reflecting the operational status of a business. Note that other sources of data may also be accessed and processed (e.g., sales/CRM data, HR data, loyalty group data, financial data, etc.) as part of generating a recommendation”; and
Para. [0141]: “…, inputs to the data analysis and decision processes may include customer data, product data, supply chain data, or financial operating data, among others. One aspect of the inventive system and methods is to not only leverage the techniques to generate recommendations, but to do so in a manner which ranks possible suggestions by taking into account the predicted outcomes and ordering them according to a rule or heuristic (such as by the likelihood of success in producing a desired outcome).”, 
the examiner notes that the additional input data from product data or others might be used to generate the recommendation to that customer to that of identifying additional data to include in the recommendation),
wherein the first request and the second request is derived from a process flow generated by a user of a graphical user interface (GUI) (NATHENSON Para. [0025]: “… process the accessed data, including implementing a decision process to generate the recommendation or the suggested action; [0026] generate a workflow or process for interacting with the customer to enable the organization's representative to present the recommendation or suggested action to the customer; and [0027] present the workflow or process to the organization's representative.”; and
Fig. 2, Para. [0077]: “The distributed computing service/platform (which may also be referred to as a multi-tenant business data processing platform) 208 may include multiple processing tiers, including a user interface tier 216, an application server tier 220, and a data storage tier 224. The user interface tier 216 may maintain multiple user interfaces 217, including graphical user interfaces and/or web-based interfaces.”;
Para. [0093]: “Accessing or receiving data representing a customer's current browsing activities, order status, previous browsing or purchase activities, loyalty group memberships, responsiveness to different means of contact or presentation of information, etc.”; and Para. [0095]: “…, generating a suggested workflow or customer-interaction process to enable an organization's representatives to more effectively interact with a customer based on known or derived information about the customer, the organization's inventory or sales, etc.”; and
Fig. 4, Para. [0115]: “FIG. 4 is a flow chart or flow diagram illustrating aspects of a process, method, operation, or function that may be used when implementing an embodiment of the invention. Note that the data referenced in the flowchart refers to one or more of product or product related data, customer or customer related data, or business operations data, such as that identified in the example table (and/or its equivalents or corresponding data).”; and
Fig. 4, Para. [0125]: “Once one or more recommended actions have been identified or generated, the process illustrated in FIG. 4 may determine if one or more of those actions may be implemented by an automated process (as suggested by step or stage 412). If an automated execution is possible (as indicated by the “Yes” branch of step or stage 412), then the process may initiate a workflow to execute that action (as suggested by step or stage 414). Such actions may include, but are not limited to, generating a message for delivery by text or email, contacting a service representative and requesting that certain information be provided to the customer, providing/shipping a sample of a new product to the customer, automatically processing an adjustment to a customer's loyalty or credit account, etc.”, 
the examiner notes that the reference discloses that the data records are sourced from customer/user interaction based on a workflow leading to initiating an action/request/query that includes a request for certain information based on the customer/user input);

However, NATHENSON does not explicitly teach the process flow being generated by: 
providing, in the GUI to a device, the GUI providing functionality to visually insert, edit, and connect nodes representative of a plurality of operations via one or more connectors without requiring a user to write code depicted visually as interconnected nodes for which connectivity of the nodes is modifiable, wherein one or more of the nodes is associated with a load operation of the recommendation data and the additional data; and receiving the process flow from the device resulting from interactions received by the GUI.
But DREDZE teaches the process flow being generated by: providing, in the GUI to a device, the GUI providing functionality to visually insert, edit, and connect nodes representative of a plurality of operations via one or more connectors without requiring a user to write code depicted visually as interconnected nodes for which connectivity of the nodes is modifiable, wherein one or more of the nodes is associated with a load operation of the recommendation data and the additional data (DREDZE Abstract: “A method searches a set of information using a computer. The method generates a set of search results based on a search query. Then, without further user input, the method generates a set of candidate facets, where each of the candidate facets can be used to select a subset of the search results. The method ranks the candidate facets in accordance with selectivity of the candidate facets and selects a plurality of facets from among the candidate facets for presentation to the user.”; and 
Fig. 5, Col. 3, lines (5-6): “FIG. 5 illustrates a functional process flow according to some embodiments”; and
Fig. 1, Col. 5, lines (8-12): “…, the candidate facets are ranked by the Facet Ranking Module 120, and a subset of the candidate facets is selected for presentation to the user (e.g., facets selected for conveyance to the requesting client 102 along with search results produced in response to the user's search query).”; and
Fig. 1, Col. 14, lines (24-34): “…, user selection of a presentation facet causes the client application (e.g., a browser application) to augment the search query with the presentation facet, but does not automatically send the resulting revised search query to the search augmentation system. This enables the user to further edit or further augment the search query before sending the search query to the search augmentation system 100 to obtain an new set of search results. In these embodiments, the client application 104 at the client includes instructions for responding to user selection of a facet by augmenting the search query with the presentation facet.”),
the examiner notes that the reference discloses in Fig. 5 a process flow with a user interface presented with selection from the multiple candidate facets list to produce a research query to that of the process flow being presented in the GUI with interconnected nodes, i.e. facets to govern the query structure flow, for which connectivity of the nodes is modifiable by the user.  Further, the examiner notes that the reference discloses that the system enables the user to further edit or further augment the search query before sending the search query to the search augmentation, which is that to execute a load operation of the query); and 
receiving the process flow from the device resulting from interactions received by the GUI (DREDZE Abstract: “A method searches a set of information using a computer. The method generates a set of search results based on a search query. Then, without further user input, the method generates a set of candidate facets, where each of the candidate facets can be used to select a subset of the search results. The method ranks the candidate facets in accordance with selectivity of the candidate facets and selects a plurality of facets from among the candidate facets for presentation to the user.”; and
Fig. 1, Col. 5, lines (8-12): “…, the candidate facets are ranked by the Facet Ranking Module 120, and a subset of the candidate facets is selected for presentation to the user (e.g., facets selected for conveyance to the requesting client 102 along with search results produced in response to the user's search query).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of NATHENSON (disclosing workflow/scripting system and method) to include the teachings of DREDZE (disclosing methods for facet suggestion for search query augmentation) and arrive at a method to provide a user interface for interactive flow control for query generation that is modifiable by the user.  One of ordinary skill in the art would have been motivated to make this combination because by providing a user control in query generation, thereby the user is able to generate revised search query that meets the user needs of generating revised query results, as recognized by (DREDZE, Col. 1-Col. 2). In addition, the references of NATHENSON and DREDZE teach features that are directed to analogous art and they are directed to the same field of endeavor of analytical recommendation processes.

Regarding claims 14 and 20, the aforementioned claims recite similar limitations to claim 1 and therefore rejected for similar reasons as mentioned above.

Regarding claim 2, the combination of NATHENSON and DREDZE teaches the limitations of claim 1.  Further, NATHENSON teaches mapping the additional data comprises generating structured query language (SQL) statements to retrieve the additional data of the second database table, and the recommendation object is hydrated with the additional data at runtime (NATHENSON Para. [0017]: “Embodiments of the inventive system, and methods provide the ability to access and process real-time data, such as customer data (e.g., purchase history, browsing history, inquiry history, etc.), …, to provide an integrated shopping experience for end users, such as a vendor's customers”; and
Fig. 2, Para. [0078]: “Each tenant data store 226 may contain tenant-specific data that is used as part of providing a range of tenant-specific business services or functions, including but not limited to ERP, CRM, eCommerce, Human Resources management, payroll, etc. Data stores may be implemented with any suitable data storage technology, including structured query language (SQL) based relational database management systems (RDBMS).”, 
the examiner notes that the reference discloses that data is processed in real-time data utilizing SQL to that of additional data is provided at run-time).  

Regarding claim 3, the combination of NATHENSON and DREDZE teaches the limitations of claim 2. Further, NATHENSON teaches wherein the additional data is stored as a structured data object, and the mapping of the additional data is agnostic to a data hierarchy of the structured data object (NATHENSON Para. [0011]: “Note that even if multiple data sources are effectively integrated, the overall database typically requires the use of active mapping processes, as integration does not necessarily create a single source of “truth” in the absence of further processing to ensure consistency across all data. Furthermore, integration does not necessarily produce a timely transfer of data. Finally, integration does not guarantee that all relevant sources of data are available for decision-making, as one of the fundamental principles of data science is the discovery of previously-unknown casual or suggestive relationships between disparate pieces of data.”; and
Para. [0057]: “…, the inventive system and methods may include one or more of the following data/information and functional capabilities: [0058] All of a vendor's/merchant's/platform-tenant's customer data contained in a structured data storage and access element (such as a database), and associated with (though not necessarily a part of) the customer record(s) and the sales orders.”).  

Regarding claim 5, the combination of NATHENSON and DREDZE teaches the limitations of claim 1.  Further, NATHENSON teaches comprising: generating for presentation, by the processing device, the GUI which visually represents retrieval of the recommendation data, retrieval of additional data for inclusion in the recommendation object, mapping of the additional data, and generation of the recommendation object as a process flow (NATHENSON Fig. 2, Para. [0077]: “The distributed computing service/platform (which may also be referred to as a multi-tenant business data processing platform) 208 may include multiple processing tiers, including a user interface tier 216, an application server tier 220, and a data storage tier 224. The user interface tier 216 may maintain multiple user interfaces 217, including graphical user interfaces and/or web-based interfaces.”; and
Fig. 4, Para. [0121]: “…, the sources of data that are processed in order to identify possible actions and generate recommendations may include product data (element 408) and supply chain data (element 410). This data may be obtained from the underlying database that contains the information reflecting the operational status of a business. Note that other sources of data may also be accessed and processed (e.g., sales/CRM data, HR data, loyalty group data, financial data, etc.) as part of generating a recommendation”; and
Para. [0141]: “…, inputs to the data analysis and decision processes may include customer data, product data, supply chain data, or financial operating data, among others.”).  

Regarding claim 6, the combination of NATHENSON and DREDZE teaches the limitations of claim 5.  Further, NATHENSON teaches wherein each of the retrieval of the recommendation data, the retrieval of additional data for inclusion in the recommendation object, the mapping of the additional data, and the generation of the recommendation object is visually as associated with a node within the process flow (NATHENSON Para. [0025]: “… process the accessed data, including implementing a decision process to generate the recommendation or the suggested action; [0026] generate a workflow or process for interacting with the customer to enable the organization's representative to present the recommendation or suggested action to the customer; and [0027] present the workflow or process to the organization's representative.”; and
Fig. 4, Para. [0115]: “FIG. 4 is a flow chart or flow diagram illustrating aspects of a process, method, operation, or function that may be used when implementing an embodiment of the invention. Note that the data referenced in the flowchart refers to one or more of product or product related data, customer or customer related data, or business operations data, such as that identified in the example table (and/or its equivalents or corresponding data).”).  

Regarding claim 7, the combination of NATHENSON and DREDZE teaches the limitations of claim 6.  Further, NATHENSON teaches wherein the mapping of the additional data is visually represented as a map node which links nodes representative of the retrieval of the additional data and the generation of the recommendation object (NATHENSON Fig. 4, Para. [0115]: “FIG. 4 is a flow chart or flow diagram illustrating aspects of a process, method, operation, or function that may be used when implementing an embodiment of the invention. Note that the data referenced in the flowchart refers to one or more of product or product related data, customer or customer related data, or business operations data, such as that identified in the example table (and/or its equivalents or corresponding data).”).  

Regarding claim 8, the combination of NATHENSON and DREDZE teaches the limitations of claim 6.  Further, NATHENSON teaches wherein the recommendation object is based on an up next recommendation (NATHENSON Para. [0108]: “…, the inventive system leverages a set of native records in a business data processing platform to create (in some cases using advanced analysis and rules-based management) a new set of records (the recommendations for action) that are distributed to various channels for implementing specific actions/workflows. Those new records appear in lists for human perception, and are run through an automated internal workflow process or definition to take the next step. Such native records may include (but are not required to include, or limited to only including): [0109] Customer and all associated records; [0110] Transaction Types; [0111] Item Types; [0112] Campaigns (both for acquiring data and for organizing activities); and [0113] Future records being defined as a part of order management.”; and
Para [0102]: “As a result of the data structure and platform architecture utilized in some embodiments of the inventive system and methods, data such as item description, inventory level, profit margin, vendor/supplier, etc., are sourced from the same database or data storage location(s) regardless of the origin of the information request. This means that any application or user seeking certain data will access the data from a singular location in the database. Consequently, inventory data for warehouses and stores are all in the same place, as are the possible sources for more items and the data on orders in the supply chain system. This enables more productive interactions with customers or prospective customers (e.g., a store's sales representative may conduct a search and find that an item of interest is in transit or will be available at a certain date, thus suggesting a follow up action with regards to an interested customer (e.g., “ … we have a 4 of this hat on order, and it's scheduled to arrive in our store next week. Would you like me to call you when they arrive?”).”).  

Regarding claims 15, the aforementioned claim recites similar limitations to claim 8 and therefore rejected for similar reasons as mentioned above.

Regarding claim 9, the combination of NATHENSON and DREDZE teaches the limitations of claim 6.  Further, NATHENSON teaches wherein the recommendation data comprises a customer relationship management (CRM) record (NATHENSON Para. [0019]: “…, the inventive methods may be implemented as part of an eCommerce platform that is used in conjunction with ERP and/or CRM data as part of a multi-tenant system for providing order management and order processing services for multiple tenant accounts. Typically, such a system or data processing platform may be implemented as a web-service or cloud-based architecture, such as in a Software-as-a-Service (SaaS) model or format.”).  

Regarding claims 16, the aforementioned claim recites similar limitations to claim 9 and therefore rejected for similar reasons as mentioned above.

Regarding claim 10, the combination of NATHENSON and DREDZE teaches the limitations of claim 6.  Further, NATHENSON teaches wherein the additional data comprises scheduling information (NATHENSON Para. [0102]: “As a result of the data structure and platform architecture utilized in some embodiments of the inventive system and methods, data such as item description, inventory level, profit margin, vendor/supplier, etc., are sourced from the same database or data storage location(s) regardless of the origin of the information request. This means that any application or user seeking certain data will access the data from a singular location in the database. Consequently, inventory data for warehouses and stores are all in the same place, as are the possible sources for more items and the data on orders in the supply chain system. This enables more productive interactions with customers or prospective customers (e.g., a store's sales representative may conduct a search and find that an item of interest is in transit or will be available at a certain date, thus suggesting a follow up action with regards to an interested customer (e.g., “... we have a 4 of this hat on order, and it's scheduled to arrive in our store next week. Would you like me to call you when they arrive?”)”).  

Regarding claims 17, the aforementioned claim recites similar limitations to claim 10 and therefore rejected for similar reasons as mentioned above.

Regarding claim 11, the combination of NATHENSON and DREDZE teaches the limitations of claim 6.  Further, NATHENSON wherein the additional data in the second database table is mapped to the recommendation object (NATHENSON Para. [0011]: “Note that even if multiple data sources are effectively integrated, the overall database typically requires the use of active mapping processes, as integration does not necessarily create a single source of “truth” in the absence of further processing to ensure consistency across all data. Furthermore, integration does not necessarily produce a timely transfer of data. Finally, integration does not guarantee that all relevant sources of data are available for decision-making, as one of the fundamental principles of data science is the discovery of previously-unknown casual or suggestive relationships between disparate pieces of data. Conventional, actively-managed integrations typically result in a situation where a machine-learning system does not have access to certain of the possible data, and as a result may be unable to discover all of the instructive inferences.”).  

Regarding claims 18, the aforementioned claim recites similar limitations to claim 11 and therefore rejected for similar reasons as mentioned above.

Regarding claim 12, the combination of NATHENSON and DREDZE teaches the limitations of claim 6.  Further, NATHENSON wherein the additional data in a second database table is mapped to the one or more fields via a schema (NATHENSON Para. [0101]: “Note that the inventive system or platform provides this and other benefits or advantages at least partially as a result of the underlying data schema and database structure.”).  

Regarding claims 19, the aforementioned claim recites similar limitations to claim 12 and therefore rejected for similar reasons as mentioned above.

Regarding claim 13, the combination of NATHENSON and DREDZE teaches the limitations of claim 6.  Further, NATHENSON wherein the additional data comprises a prompt to take an action (NATHENSON Para. [0142]: “In cases where the behavior is one that requires a human being to take action (e.g., a phone call is the recommended action, or an email is recommended but the content requires human input), the system may trigger an alert containing the relevant data and inform the person who needs to take the action of the situation.”).  

Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication (US 2017/0236131 A1) issued to NATHENSON (hereinafter as “NATHENSON”), in view of US Patent (US 8,433,705 B1) issued to Dredze et al. (hereinafter as “DREDZE”), and in view of US Patent Application Publication (US 2021/0064676 A1) issued to Rawal et al. (hereinafter as “RAWAL”).
Regarding claim 4, the combination of NATHENSON and DREDZE teaches the limitation of claim 1. However, the combination of NATHENSON and DREDZE does not explicitly teach wherein the additional data is mapped to the recommendation object without duplicating the additional data in the first database table.
But RAWAL teaches wherein the additional data is mapped to the recommendation object without duplicating the additional data in the first database table (RAWAL discloses in Fig. 4 mapping structure that depicts an example of a user interface output to accept user inputs to map a web content variable to a respective data element of a plurality of data elements included in the recommendation of FIG. 3, which shows no duplication of data as such).  
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of NATHENSON (disclosing workflow/scripting system and method) and DREDZE (disclosing methods for facet suggestion for search query augmentation), to include the teachings of RAWAL (disclosing methods  for mapping of content variables to data elements supported by an analytics system) and arrive at a method to map data to a particular object of interest.  One of ordinary skill in the art would have been motivated to make this combination because by associating different content variables, such as how the functionality of those variables is described in text of the content, and then mapping these data elements to an object of interest, as a result, the techniques described may improve efficiency of onboarding of huge content to arrive at the desired recommended outcome, as recognized by (RAWAL Abstract, Para. [0004]-[0006]). In addition, the references of NATHENSON, DREDZE and RAWAL teach features that are directed to analogous art and they are directed to the same field of endeavor of analytical recommendation processes.

Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Cook et al.; (US-20180096417-A1); “Methods and device for object discovery and object mapping through an application with a Graphical User Interface (GUI), wherein the device is operable to display the recommended object in the object recommendation channel of the GUI.”
Reyes et al.; (US- 11270215-B2); “Methods for intelligent recommendations, wherein a recommendation engine may generate a recommendation in response to user interactions and executed operations in a system. The recommendation may be determined according to a number of factors including, but not limited to, an object affinity and a user affinity. The recommendation may include one or more of a recommendation to use an object and a recommendation for taking one or more actions.”
Franz ; (US- 20190303446  -A1); “System and methods for creating and managing dynamic elements, wherein receiving a dynamic element insertion request from the client application; then identifying and forwarding one or more keys corresponding to one or more suggested dynamic elements to the client application.”

Any inquiry concerning this communication or earlier communications from the examiner should be directed to Zuheir A Mheir whose telephone number is (571)272-4151.  The examiner can normally be reached on Monday - Friday 9:00 - 5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital can be reached on (571)272-4215.  The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system.  Status information for published applications may be obtained from either Private PAIR or Public PAIR.  Status information for unpublished applications is available through Private PAIR only.  For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
4/5/2025

/ZUHEIR A MHEIR/Patent Examiner, Art Unit 2162   

/PIERRE M VITAL/Supervisory Patent Examiner, Art Unit 2162                                                                                                                                                                                                        




    
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
    


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