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 .
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 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1-7 are directed to a system, which is an apparatus. Claims 8-17 are directed to a method, which is a process. Therefore, claims 1-17 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Taking claim 1 as representative, claim 1 sets forth the following limitations reciting the abstract idea of using user feedback to refine a product solution for the user:
receive an input prompt from a user;
storing a product catalog comprising a plurality of product offerings;
interpret the input prompt and identify a user problem to solve;
dynamically interact with the user to refine the user problem to solve through the input prompt;
analyze the refined user problem to solve;
select a product solution from the product catalog;
iteratively refine the product solution based on one or more criteria provided by the user through the input prompt;
present the product solution to the user and guide the user through one or more actionable steps to solve the user problem.
The recited limitations above set forth the process for using user feedback to refine a product solution for the user. These limitations amount to certain methods of organizing human activity, including commercial or legal interactions (e.g. advertising, marketing or sales activities or behaviors, etc.). The claims recite steps for interacting with a user and iterating a process of receiving inputs of criteria from the user and refining a product solution (see specification: [0003] disclosing the field of invention for recommending a product using dynamic questioning and interactive dialogue), which is a sales and marketing activity.
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106.04(a)(2)).
Step 2A (Prong 2):
Examiner acknowledges that representative claim 1 recites additional elements, such as:
a user interface;
a server in communication with the user interface;
a database;
an AI engine implemented on the server;
a sentiment analysis processing module configured to;
a refinement engine configured to;
a recommendation engine configured to:
Taken individually and as a whole, representative claim 1 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite a user interface, a server, and a database, these elements are recited with a very high level of generality. The interface is disclosed in paragraph [0025] of the specification, which discloses the interface serves as a primary point of interaction between a user and the system, and is configured to receive an input prompt from the user, implemented as a web-based platform, mobile application, desktop software, or the like. The server is disclosed in specification paragraph [0027] disclosing that it may be implemented as a cloud-based platform or as an on-premises solution, with no further details. Specification paragraph [0028] discloses that the database is structured to allow for efficient querying and retrieval of data, without any further description. As such, it is evident that these elements are generic computing elements, that merely serve to provide a general link to a computing environment, such that the abstract idea may be implemented on a computing device. The AI engine is disclosed in specification paragraphs [0030-0032] disclosing that the AI engine is composed of various models. However, the various models merely describe the steps of the abstract idea. There is no discussion to any of the underlying technology of AI or machine learning, and the only description in the specification of these elements merely disclose providing an output of information regarding the abstract idea. As such, it is clear that the AI engine is any generic AI technology that is merely applied to the abstract idea to automate the dialog of an agent.
In view of the above, under Step 2A (Prong 2), representative claim 1 does not integrate the recited exception into a practical application (see: MPEP 2106.04(d)).
Step 2B:
Returning to representative claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in claim 1 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even when considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Regarding Claim 8 (method): Claim 8 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 8 is rejected under at least similar rationale as provided above regarding claim 1.
Dependent claims 2-7 and 9-17 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the algorithm for using user feedback to refine a product solution for the user, and do not recite any further additional elements. Thus, each of claims 2-7 and 9-17 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-7 and 9-17 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 2-7 and 9-17 rely on at least similar elements as recited in claim 1. Further additional elements are also acknowledged (e.g., a client application(claim 2); a machine-learning model (claim 3)); however, the additional elements of claims 2-7 and 9-17 are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 2-7 and 9-17 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 2-7 and 9-17 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 1. Thus, dependent claims 2-7 and 9-17 do not add “significantly more” to the abstract idea.
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 1-4, 6-15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable by Wang (US 20230245651 A1) in view of Kakodkar (US 20240428003 A1), and in further view of Ahmad-Taylor (US 20250061491 A1).
Regarding Claim 1: Wang discloses a system comprising:
a user interface configured to receive an input prompt from a user; (Wang: [0250] – “The interface generator 215 in the AI system is a component responsible for creating and managing user interfaces (Uls) that facilitate seamless interaction between the AI system and its users. The interface generator aims to provide a user-friendly, accessible, and engaging interface, enabling users to effectively communicate with the AI system”; Wang: [0476] – “The process of providing personalized recommendations or suggestions based on user preferences and behavior starts with the Al system receiving input from the user, such as a search query or selection 2201. This input serves as a starting point for the Al system to generate relevant options for the user”).
a server in communication with the user interface; (Wang: [0041] – “The intelligent system 100 also includes data storage 106, cloud-based server 107, application programming interfaces (APIs) 108, and network 109. The data storage 106 stores and manages the vast amounts of data generated by the AI system 101. The cloud-based server 107 manages the processing and storage of data and provides computing power to enable the AI application to function. The network 109 connects the various components and allows for communication between them”).
an AI engine implemented on the server; (Wang: [0476] – “The process of providing personalized recommendations or suggestions based on user preferences and behavior starts with the Al system receiving input from the user, such as a search query or selection 2201. This input serves as a starting point for the Al system to generate relevant options for the user”).
a sentiment analysis processing module configured to interpret the input prompt and identify a user problem to solve; (Wang: [0477] – “Once the Al system has received user input, it retrieves user data such as preferences and behavior history 2202. This data is then filtered and analyzed by the Al system to identify relevant options that can be recommended to the user 2203. Based on this analysis, the Al system generates personalized recommendations or suggestions that are tailored to the user’s preferences and behavior 2204. These recommendations can take the form of product suggestions, content recommendations, or any other relevant options based on the user’s input and data”).
a refinement engine configured to dynamically interact with the user via the user interface to refine the user problem to solve through the input prompt; (Wang: [0478] – “The Al system then presents these personalized recommendations or suggestions to the user 2205, who can provide feedback on the options presented 2206. This feedback is valuable in improving the recommendations or suggestions for future interactions with the user. The Al system uses feedback from the user to update the recommendations or suggestions 2207. This iterative process of feedback and data analysis allows the Al system to continually learn and adapt to the user’s preferences and behavior”; Wang: [0197] – “The AI system employs the dialogue management module 209 to control the conversation’s progression by understanding and identifying user’s intent and objective of the conversation as well as choosing suitable responses and prompts according to the user’s prior inputs and the prevailing context. This approach ensures a seamless conversational flow and enables the AI application to comprehend the user’s needs and preferences more personally”).
a recommendation engine configured to:
analyze the refined user problem to solve; (Wang: [0477] – “Once the Al system has received user input, it retrieves user data such as preferences and behavior history 2202. This data is then filtered and analyzed by the Al system to identify relevant options that can be recommended to the user 2203. Based on this analysis, the Al system generates personalized recommendations or suggestions that are tailored to the user’s preferences and behavior 2204. These recommendations can take the form of product suggestions, content recommendations, or any other relevant options based on the user’s input and data”).
select a product solution; (Wang: [0477] – “the Al system generates personalized recommendations or suggestions that are tailored to the user’s preferences and behavior”).
iteratively refine the product solution based on one or more criteria provided by the user through the input prompt; (Wang: [0478] – “The Al system then presents these personalized recommendations or suggestions to the user 2205, who can provide feedback on the options presented 2206. This feedback is valuable in improving the recommendations or suggestions for future interactions with the user. The Al system uses feedback from the user to update the recommendations or suggestions”; Wang: [0479] – “The process ends when the user has received and responded to the personalized recommendations or suggestions with satisfactory”).
a communication module configured to present the product solution to the user via the user interface. (Wang: [0478] – “The Al system then presents these personalized recommendations or suggestions to the user”).
Wang does not explicitly teach a method comprising:
a database storing a product catalog comprising a plurality of product offerings;
guide the user through one or more actionable steps to solve the user problem.
Notably, however, Wang does disclose improving the ability to provide recommendations and solutions for users (Wang: [0079]).
To that accord, Kakodkar does teach guide the user through one or more actionable steps to solve the user problem. (Kakodkar: [0064] – “generative artificial intelligence (e.g., LLM or other techniques) may be utilized to transform the conversation (e.g., conversational turns where a customer asks a question and an agent responds, or a summary thereof) into instructions, e.g., step-by-step instructions. The LLM or other model may be trained using user-permitted data from prior customer support interactions and groundtruth content items that addressed the customer request in such interactions. Training of the model can be performed using a suitable technique such as prompt tuning or fine tuning. In some implementations, the training may be performed using reinforcement learning with human feedback (RLHF) technique. The generated content update may include text, image, audio, video, or a combination. For example, generative AI may be utilized to generate an instruction sequence as text, as a flow diagram illustrating each step in the instruction sequence, as audio with step-by-step guidance, or as a video (e.g., including screenshots from the software application) with audio/text instructions”).
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 invention of Wang disclosing the system for a conversational AI to refine user problems and determine product solutions refined with user feedback with the guiding the user with instructions as taught by Kakodkar. One of ordinary skill in the art would have been motivated to do so in order to help users understand how to use a product and resolve problems (Kakodkar: [0001]).
Wang in view of Kakodkar does not explicitly teach a database storing a product catalog comprising a plurality of product offerings; Notably, however, Wang does disclose providing product recommendations for the user (Wang: [0477]), and a knowledge base of items that can be updated to improve recommendation accuracy (Wang: [0427]).
To that accord, Ahmad-Taylor does teach a database storing a product catalog comprising a plurality of product offerings; (Ahmad-Taylor: [0029] – “stores the aggregated catalog data from all vendors in an inventory database. When a user submits a query through the chat interface regarding a product, the chatbot system accesses the inventory database to identify vendors with the product and determine availability across catalogs”).
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 invention of Wang in view of Kakodkar disclosing the system for a conversational AI to refine user problems and determine product solutions refined with user feedback with the database storing a product catalog of product offerings as taught by Ahmad-Taylor. One of ordinary skill in the art would have been motivated to do so in order to search multiple vendor catalogs for the product query (Ahmad-Taylor: [0031]).
Regarding Claim 2: Wang in view of Kakodkar and Ahmad-Taylor discloses the limitations of claim 1 above.
Wang further discloses wherein the user interface is accessible via a client application running on a user device. (Wang: [0037] – “the AI system is designed to handle a large number of users and diverse conversational topics, making it suitable for various industries and applications. In some embodiments, the invention can be utilized in customer support settings to improve response times and customer satisfaction”).
Regarding Claim 3: Wang in view of Kakodkar and Ahmad-Taylor discloses the limitations of claim 1 above.
Wang further discloses wherein the sentiment analysis processing module further includes a machine learning model trained to understand domain-specific terminology relevant to the product offerings in the product catalog. (Wang: [0135] – “the NLP engine 202 is designed to receive and process user input in natural language by performing various NLP tasks, which may include parsing, part-of-speech tagging, sentence breaking, stemming, word segmentation, terminology extraction, grammar induction, lexical semantics, machine translation, named entity recognition (NER), NLG, NLU, and relationship extraction, among others”; Wang: [0195] – “The knowledge graph engine 210 serves as the backbone of the knowledge graph, providing the tools and functionalities to perform various tasks, such as: (1) Data Ingestion: The knowledge graph engine acquires and processes data from multiple sources, including structured databases, unstructured text, web pages, and APIs, to extract relevant entities and relationships. Data integration techniques, such as entity resolution and schema matching, are used to combine and harmonize information from diverse sources. (2) Knowledge Representation: The knowledge graph engine organizes and stores extracted entities and relationships in a graph-based data model. This representation enables efficient storage and retrieval of complex, interrelated information, while preserving its semantic structure. (3) Semantic Enrichment: The knowledge graph engine can use NLP, ML, and reasoning techniques to enrich the knowledge graph with additional semantic information, such as entity types, categories, and hierarchies”).
Regarding Claim 4: Wang in view of Kakodkar and Ahmad-Taylor discloses the limitations of claim 1 above.
Wang further discloses wherein the database further comprises a context module that informs the recommendation engine on handling initial queries based on predefined contexts. (Wang: [0208] – “Firstly, it receives the user’s intent from the intent classification component and considers the current conversation context. Secondly, it retrieves or generates a set of possible response candidates based on the intent and context. Thirdly, it ranks or scores the response candidates based on their relevance, coherence, and appropriateness to the user’s intent and conversation context”).
Regarding Claim 6: Wang in view of Kakodkar and Ahmad-Taylor discloses the limitations of claim 1 above.
Wang in view of Kakodkar does not explicitly teach wherein the communication module further comprises a post-solution recommendation module configured to suggest additional complementary product offerings after the user has selected a product solution. Notably, however, Wang does disclose the AI providing solutions for the user (Wang: [0079]).
To that accord, Ahamd-Taylor does teach wherein the communication module further comprises a post-solution recommendation module configured to suggest additional complementary product offerings after the user has selected a product solution. (Ahmad-Taylor: [0132] – “the dynamic advertisement system 314 recommends additional products to the user 320 based on analysis of the user query and a user profile 310 stored in a data store of the interactive platform 352. The user profile 310 may be updated by various components of the interactive platform 352 including the chatbot system 300 storing user profile information 346 about conversations the chatbot 340 has with the user 320 and the dynamic advertisement system 314 storing user profile information 346 about previous product details 312 the dynamic advertisement system 314 has provided for the user 320. For instance, after responding to the user's initial product query, the dynamic advertisement system 314 can provide recommendations for complementary or related products that may also suit the need and interests of the user”).
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 invention of Wang in view of Kakodkar disclosing the system for a conversational AI to refine user problems and determine product solutions refined with user feedback with the post-solution recommendation for complementary products as taught by Ahamd-Taylor. One of ordinary skill in the art would have been motivated to do so in order to upsell and expand sales beyond the original user query (Ahmad-Taylor: [0137]).
Regarding Claim 7: Wang in view of Kakodkar and Ahmad-Taylor discloses the limitations of claim 1 above.
Wang does not explicitly teach wherein the one or more actionable steps are at least one of making a purchase, scheduling a consultation, and accessing additional resources, or any combination. Notably, however, Wang does disclose generating personalized recommendations for a user (Wang: [0477]).
To that accord, Kakodkar does tech teach wherein the one or more actionable steps are at least one of making a purchase, scheduling a consultation, and accessing additional resources, or any combination. Examiner notes that Applicant recites at least one of in the claim. (Kakodkar: [0064] – “generative AI may be utilized to generate an instruction sequence as text, as a flow diagram illustrating each step in the instruction sequence, as audio with step-by-step guidance, or as a video (e.g., including screenshots from the software application) with audio/text instructions”). In summary, Kakodkar discloses accessing additional resources of the instruction sequence.
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 invention of Wang disclosing the system for a conversational AI to refine user problems and determine product solutions refined with user feedback with the actionable steps of accessing additional resources as taught by Kakodkar. One of ordinary skill in the art would have been motivated to do so in order to help users understand how to use a product and resolve problems (Kakodkar: [0001]).
Regarding Claim 8: Claim 8 recites substantially similar limitations as claim 1. Therefore, claim 8 is rejected under the same rationale as claim 1 above.
Regarding Claim 9: Wang in view of Kakodkar and Ahmad-Taylor discloses the limitations of claim 8 above.
Wang in view of Kakodkar does not explicitly teach providing post-solution recommendations based on the selected product solution. Notably, however, Wang does disclose the AI providing solutions for the user (Wang: [0079]).
To that accord, Ahamd-Taylor does teach providing post-solution recommendations based on the selected product solution. (Ahmad-Taylor: [0132] – “the dynamic advertisement system 314 recommends additional products to the user 320 based on analysis of the user query and a user profile 310 stored in a data store of the interactive platform 352. The user profile 310 may be updated by various components of the interactive platform 352 including the chatbot system 300 storing user profile information 346 about conversations the chatbot 340 has with the user 320 and the dynamic advertisement system 314 storing user profile information 346 about previous product details 312 the dynamic advertisement system 314 has provided for the user 320. For instance, after responding to the user's initial product query, the dynamic advertisement system 314 can provide recommendations for complementary or related products that may also suit the need and interests of the user”).
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 invention of Wang in view of Kakodkar disclosing the system for a conversational AI to refine user problems and determine product solutions refined with user feedback with the post-solution recommendation based on the selected product as taught by Ahamd-Taylor. One of ordinary skill in the art would have been motivated to do so in order to upsell and expand sales beyond the original user query (Ahmad-Taylor: [0137]).
Regarding Claim 10: Wang in view of Kakodkar and Ahmad-Taylor discloses the limitations of claim 8 above.
Wang further discloses the step of storing a history of user interactions in the database, wherein the history is used by the AI engine to refine future recommendations based on past user behavior. (Wang: [0348] – “the AI system considers the user’s preferences, interaction history, and previous interaction stored in the OKB to tailor the predicted contextual information to the user’s needs and interests”).
Regarding Claim 11: Wang in view of Kakodkar and Ahmad-Taylor discloses the limitations of claim 8 above.
Wang further discloses wherein the refinement engine adjusts its questioning based on real-time analysis of the user’s responses. (Wang: [0409] – “the AI system evaluates the user’s input and available contextual information to determine if additional information is needed to understand the most likely intent and objective accurately”; Wang: [0353] – “Real-time user input: The AI system can use real-time user input and interaction to understand the user’s immediate needs and intents, allowing it to adapt and provide relevant contextual information accordingly”).
Regarding Claim 12: Wang in view of Kakodkar and Ahmad-Taylor discloses the limitations of claim 8 above.
Wang further discloses analyzing user feedback after the implementation of the product solution to update the product catalog and improve future recommendations. (Wang: [0482] – “If the responses satisfy the user, the user feedback and generated information are then updated in the OKB 2311. The new information is then used by the Al system to analyze the feedback and determine the appropriate action to take”; Wang: [0428] – “By continuously comparing and updating its knowledge base, the AI system can improve its accuracy and provide more contextually relevant and personalized responses to the user”).
Regarding Claim 13: Wang in view of Kakodkar and Ahmad-Taylor discloses the limitations of claim 8 above.
Wang further discloses generating alternative product solutions based on one or more constraints provided by the user during the refinement process. (Wang: [0478] – “This feedback is valuable in improving the recommendations or suggestions for future interactions with the user. The Al system uses feedback from the user to update the recommendations or suggestions”).
Regarding Claim 14: Wang in view of Kakodkar and Ahmad-Taylor discloses the limitations of claim 8 above.
Wang in view of Kakodkar does not explicitly disclose wherein the communication module integrates with third-party platforms to automatically execute actionable steps. Notably, however, Wang does disclose scarping information from various sources on the internet to gather relevant data (Wang: [0219]).
To that accord, Ahmad-Taylor does disclose wherein the communication module integrates with third-party platforms to automatically execute actionable steps. (Ahmad-Taylor: [0160] – “Processing the payment and order fulfillment through ecommerce APIs integrated into the interactive platform”). In summary, the APIs of the platform would interact with the merchants to process the payments.
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 invention of Wang in view of Kakodkar disclosing the system for a conversational AI to refine user problems and determine product solutions refined with user feedback with the integrating with third-party platforms to execute actionable steps as taught by Ahamd-Taylor. One of ordinary skill in the art would have been motivated to do so in order to streamlined commerce experience for users without having to switch between different cites or applications (Ahmad-Taylor: [0162]).
Regarding Claim 15: Wang in view of Kakodkar and Ahmad-Taylor discloses the limitations of claim 8 above.
Wang does not explicitly teach presenting the user with an interactive visual representation of the product solution. Notably, however, Wand does disclose presenting the recommendations to the user (Wang: [0478]).
To that accord, Kakodkar does teach presenting the user with an interactive visual representation of the product solution. (Kakodkar: [0064] – “The generated content update may include text, image, audio, video, or a combination. For example, generative AI may be utilized to generate an instruction sequence as text, as a flow diagram illustrating each step in the instruction sequence, as audio with step-by-step guidance, or as a video (e.g., including screenshots from the software application) with audio/text instructions”).
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 invention of Wang disclosing the system for a conversational AI to refine user problems and determine product solutions refined with user feedback with the presenting an interactive visual representation of the product solution as taught by Kakodkar. One of ordinary skill in the art would have been motivated to do so in order to help users understand how to use a product and resolve problems (Kakodkar: [0001]).
Regarding Claim 17: Wang in view of Kakodkar and Ahmad-Taylor discloses the limitations of claim 8 above.
Wang does not explicitly teach recommending additional resources to the user based on the selected product solution, to aid in the implementation and usage of the solution. Notably, however, Wand does disclose presenting the recommendations to the user (Wang: [0478]).
To that accord, Kokadkar does teach recommending additional resources to the user based on the selected product solution, to aid in the implementation and usage of the solution. (Kokadkar: [0064] – “recommending additional resources to the user based on the selected product solution, to aid in the implementation and usage of the solution”).
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 invention of Wang disclosing the system for a conversational AI to refine user problems and determine product solutions refined with user feedback with the recommending additional resources to the user to aid in the implementation and usage of the solution as taught by Kakodkar. One of ordinary skill in the art would have been motivated to do so in order to help users understand how to use a product and resolve problems (Kakodkar: [0001]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable by the combination of Wang (US 20230245651 A1), Kakodkar (US 20240428003 A1), and Ahmad-Taylor (US 20250061491 A1), in view of Unnikrishnan (US 20240257199 A1).
Regarding Claim 5: The combination of Wang, Kakodkar, and Ahmad-Taylor discloses the limitations of claim 1 above.
The combination does not explicitly teach wherein the product catalog includes data related to compatibility and combinability of product offerings to facilitate the creation of synergistic product solutions. Notably, however, Ahmad-Taylor does disclose maintaining a catalog of items and recommending complementary items (Ahmad-Taylor: [0132]).
To that accord, Unnikrishnan does teach wherein the product catalog includes data related to compatibility and combinability of product offerings to facilitate the creation of synergistic product solutions. (Ahmad-Taylor: [0133] – “product catalog data 1010, additional unstructured data 1015, structured data 1020, knowledge graph component 1025, embedding component 1030, generative model 1035, product listing 1040, and compatible products bundle”; Unnikrishnan: [0120] – “a user (e.g., a merchant) adds a new product and associated catalog data on a merchant platform. In some cases, the catalog data is provided by the manufacturer of the product. The merchant may additionally add known compatibility data from, for example, a product database”).
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 invention of the combination of Wang, Kakodkar, and Ahmad-Taylor disclosing the system for a conversational AI to refine user problems and determine product solutions refined with user feedback with the product catalog data including compatibility and combinability of product offerings as taught by Unnikrishnan. One of ordinary skill in the art would have been motivated to do so in order to take advantage of additional sales for compatible products (Unnikrishnan: [0002]).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable by the combination of Wang (US 20230245651 A1), Kakodkar (US 20240428003 A1), and Ahmad-Taylor (US 20250061491 A1), in view of Kellogg (US 20240311879 A1).
Regarding Claim 16: The combination of Wang, Kakodkar, and Ahmad-Taylor discloses the limitations of claim 1 above.
The combination does not explicitly teach wherein the recommendation engine incorporates user-defined weighting factors for different criteria. Notably, however, Wang does disclose using user input to identify relevant options that can be recommended to the user (Wang: [0477]).
To that accord, Kellogg does teach wherein the recommendation engine incorporates user-defined weighting factors for different criteria. (Kellogg: [0042] – “the user inputs of adding and subtracting features may be used to determine an initial weight for the weighted factors”).
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 invention of the combination of Wang, Kakodkar, and Ahmad-Taylor disclosing the system for a conversational AI to refine user problems and determine product solutions refined with user feedback with the incorporating of user-defined weighting factors as taught by Kellogg. One of ordinary skill in the art would have been motivated to do so in order to update results for a user (Kellogg: [0029]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bueche (US 11,776,542 B1) discloses col. 6, ln. 37-51 – “A voice browsing conversation is typically represented or modeled as a sequence of turns in a context, and responses provided by a user in each of the turns may be used to refine an identified or predicted goal of the user, which may be represented as a set of one or more constraints. For example, where a user is interested in listening to music, a system operating a conversational agent may learn that the customer likes '90s hip hop but dislikes country, that the user prefers music with upbeat or energetic moods to laid back or depressed moods, or that the user is a fan of Adele but not a fan of Shania Twain. A system operating a conversational agent, or another system in communication with that system, may maintain a set of search results (e.g., candidate search results) that are consistent with such constraints”
Herling (US 20220057707 A1) discloses [0030] – “chatbot component 111 of the present invention provides the capability of assisting customers to have a personalized, conversational expert help them distinguish which solution(s) are right for them within vendor's products/services page. This is to improve the digital customer experience and decrease customer churn, resulting in more product sales”).
PTO-892 Reference U discloses dialogue strategies of conversational artificial intelligence predicting user intent using LSTM-based neural network models. Training samples are used to teach the model and provide more accurate results.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY J KANG whose telephone number is (571)272-8069. The examiner can normally be reached Monday - Friday: 7:30 - 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, Maria-Teresa Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 3/16/2026