DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/09/2026 has been entered.
Status of Claims
Claims 1, 3-11, and 13-20 submitted on 03/09/2026 are pending and have been examined. Claims 2 and 12 have been cancelled. Claims 1, 11, and 20 have been amended.
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 .
Priority
No foreign priority or domestic benefit was claimed by the applicant and the application has been examined with respect to its filing date of 12/12/2022.
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, 3-11, and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 1
Claims 1 and 3-10 are directed to a process, claims 11 and 13-19 are directed to an article of manufacture, and claim 20 is directed to a machine (see MPEP 2106.03).
Step 2A, Prong 1
Claim 1, taken as representative, recites at least the following limitations that recite an abstract idea:
a method comprising:
maintaining, information about a set of intents;
maintaining, information about an association between each intent from the set of intents and one or more corresponding groups of items of a plurality of groups of items;
receiving first unstructured data including text data and audio data from a user, the first unstructured data communicated, via the user;
applying a natural language process to the first unstructured data to generate a reply, the reply identifying a specific item and requesting from the user to provide one or more additional details about the first unstructured data and the specific item, wherein the natural language process is trained by:
obtaining a training example including natural language text data and a label, the label representing an intent corresponding to the natural language text data,
applying the natural language process to the natural language text data to generate an output,
comparing the output to the label to generate a score that is indicative of a difference between the output and the label, and
updating a set of parameters for the natural language process;
updating the chat interface with the reply generated, the reply including text and audio simulating at the chat interface an interaction of the user with another user;
receiving second unstructured data including text from the user through the chat interface in response to the reply, the second unstructured data comprising the one or more additional details about the first unstructured data;
generating, using the first unstructured data, the reply and the second unstructured data, conversational data received via the chat interface;
extracting one or more keywords from the conversational data by applying the natural language process to the conversational data;
determining, from the extracted one or more keywords, an intent of the user as one of the set of intents maintained;
identifying, based on information about the intent and the information about the association maintained, one or more groups of items of the plurality of groups of items;
selecting a group of items from the identified one or more groups of items based on characteristics of the user maintained;
generating an order including a plurality of items included in the selected group of items for the user; and
the order for presentation to the user, wherein transmitting the order with information about the order.
The above limitation, under its broadest reasonable interpretation, falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that it recites a commercial interaction, see ¶¶0001-0002. Claims 11 and 20 recite similar limitations as claim 1.
Thus, under Prong 1 of Step 2A, claims 1, 11, and 20 recite an abstract idea.
Step 2A, Prong 2
Claim 1 includes the following additional elements that are bolded:
a method comprising, at a computer system comprising a processor and a computer-readable storage medium:
maintaining, by the computer system, information about a set of intents;
maintaining, by the computer system, information about an association between each intent from the set of intents and one or more corresponding groups of items of a plurality of groups of items;
receiving first unstructured data including text data and audio data from a user of the computer system, the first unstructured data communicated, via a device associated with the user, through a chat interface provided by the computer system;
applying a natural language process of a chatbot of the computer system to the first unstructured data to generate a reply, the reply identifying a specific item and requesting from the user to provide one or more additional details about the first unstructured data and the specific item, wherein the natural language process is trained by:
obtaining a training example including natural language text data and a label, the label representing an intent corresponding to the natural language text data,
applying the natural language process to the natural language text data to generate an output,
comparing the output to the label to generate a score that is indicative of a difference between the output and the label, and
updating a set of parameters for the natural language process by backpropagating the score through layers of a neural network of the natural language process;
updating the chat interface with the reply generated by the chatbot, the reply including text and audio simulating at the chat interface an interaction of the user with another user of the computer system;
receiving second unstructured data including text from the user through the chat interface of the device in response to the reply, the second unstructured data comprising the one or more additional details about the first unstructured data;
generating, using the first unstructured data, the reply and the second unstructured data, conversational data received via the chat interface of the device;
extracting one or more keywords from the conversational data by applying the natural language process of the chatbot to the conversational data;
determining, from the extracted one or more keywords, an intent of the user as one of the set of intents maintained by the computer system;
identifying, based on information about the intent and the information about the association maintained by the computer system, one or more groups of items of the plurality of groups of items;
selecting a group of items from the identified one or more groups of items based on characteristics of the user maintained by the computer system;
generating an order including a plurality of items included in the selected group of items for the user; and
transmitting the order to the device for presentation to the user, wherein transmitting the order to the device causes the device to display a user interface with information about the order.
Claims 11 and 20 include the same additional elements as claim 1. In addition, claim 11 includes the following additional elements that are bolded: a computer program product comprising a non- transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to. In addition, claim 20 includes the following additional elements that are bolded: a computer system comprising: a processor; and a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the processor, cause the processor to.
The additional elements recited in claims 1, 11, and 20 merely invoke such elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment of chatbots and computer devices (see MPEP 2106.05(f) and MPEP 2106.05(h). These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration (see Fig. 1 and ¶¶0016-0022).
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the additional elements do not integrate the judicial exception into a practical application and, thus, claims 1, 11, and 20 are directed to an abstract idea.
Step 2B
As noted above, while the recitation of the additional elements in independent claims 1, 11, and 20 are acknowledged, claims 1, 11, and 20 merely invoke such additional elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment (see MPEP 2106.05(f) and MPEP 2106.05(h)).
Even when considered as an ordered combination, the additional elements of claim 1, 11, and 20 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1, 11, and 20 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1, 11, and 20 are ineligible.
Dependent claims 3-10 and 13-19 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 3-10 and 13-19 merely further define the abstract limitations of claims 1, 11, and 20 or provide further embellishments of the limitations recited in independent claims 1, 11, and 20. Claims 3-10 and 13-19 do not introduce any further additional elements.
Thus, dependent claims 3-10 and -19 are ineligible.
Subject Matter Free of Prior Art
Claims 1, 3-11, and 13-20 are determined to have overcome the prior art of rejection and are free of
prior art, however the claims 1, 3-11, and 13-20 remain rejected under 35 USC 101, as set forth above.
Taking amended claim 1 as a representative independent claim, the claims as amended are found to overcome the prior art rejection for the reasons set forth below.
Claim 1 now recites the additional claimed features of receiving first unstructured data including text data and audio data from a user of the computer system; requesting from the user to provide one or more additional details about the first unstructured data and the specific item; updating the chat interface with the reply generated by the chatbot, the reply including text and audio simulating at the chat interface an interaction of the user with another user of the computer system.
The closest prior art was found to be as follows:
Grandhi et al., hereinafter, Grandhi (US 2016/0055563 A1 [previously cited]) recites (Figs. 6 and 9; ¶¶0061-0065[In the specific example of FIG. 15, "dresses to wear for a Summer wedding reception that make me look taller and are under 100 dollars' can be received as a query in step 1502. In step 1504, process 1500 implements a semantic processing engine (e.g. NLP, artificial intelligence (AI), graph analysis, etc.)], ¶0070[The system implementing process 1200 can enable users to discover appropriate products using voice. A conversational based Interface for the user to interact example is now provided. In another scenario the user may be in a public place and cannot use voice for the sake of privacy. In this case, the system could act as a conversational chat agent that interacts with the user using keyboard and help find the products in an easy and effective manner. In another scenario the user may send a text message to the system to help recommend the right products. The system implementing process 1200 can then interact with the user back and forth to help user find the appropriate products]).
Carney et al., hereinafter, Carney (US 2021/0182283 A1 [previously cited]) recites (Figs. 1 and 2; ¶0041[The training module 244 is configured to train the query matching module 238 to improve the identification of matching queries 224 from the stored queries 220 based on feedback received regarding previously identified matching queries 224. For instance, when a matching query 224 is provided to a user for evaluation, the user may confirm or deny that the matching query 224 is sufficiently close to the query structure 214. In some cases, when the user denies that the matching query 224 is sufficiently close, that denial may be used as feedback to adjust the query matching module 238 to narrow or tighten the range in which a matching query 224 is identified from the stored queries 220 (e.g., a percentage threshold of matching query parameters is increased). Alternatively, or additionally, narrowing the range of the query matching module 238 may include adjusting the operation of specific matching rules of the query matching module 238 that were used when the module 238 identified the matching query 224 that has been denied by the user.] in view of ¶¶0018-0020[It should be understood that the natural language engine 108 may be configured and/or implemented using any known natural language engine and/or natural language processing application or similar software without departing from the description herein]).
Balasubramanian et al., hereinafter, Balasubramanian (US 2023/0316375 A1 [previously cited]) recites (Fig. 5; ¶0080[ If the comparison indicates the predicted measure of similarity differs from the label applied to the example (e.g., the predicted measure of similarity is less than a threshold for performing the specific interaction with the recipe when the label indicates the specific interaction with the recipe was performed or the predicted measure of similarity is above a threshold for performing the specific interaction with the item when the label indicates the specific interaction was not performed), the machine learning recommendation model updates 520 one or more parameters of the machine learning recommendation model using one or more supervised learning methods. For example, the online concierge system 102 backpropagates the one or more error terms from the difference between label applied to the example of the training data and the output of the machine learning recommendation model.]).
Mallette et al., hereinafter, Mallette (US 2021/0150385 A1 [previously cited]) recites (¶0052[In one embodiment, the defined intent dataset 248 may represent the intents 216 which the trained question-intent classifier 212 may be able to classify (e.g., recognize) in the example questions 226a of the historical chat data 222... The cognitive system 200 may store the defined intent dataset 248 in the conversation database 246 for use during a recommendation phase, as will be described with reference to FIG. 9.]).
NPL: "Only 17% Of Consumers Browse With Intent To Purchase On First E-Commerce Site Visit" teaches important facts and figures regarding shoppers' intents. “One important part of converting browsing into buying is on-site search optimization, according to Kennedy. Making it easy for a consumer to reach a product page on a retailer’s web site can significantly boost the conversion rate.” “The top “must-haves” for shoppers include: Easy-to-use product search functions; ample information about products and returns; and personalized recommendations. When those features are not available, 9% will abandon the site. And the abandonment rate rises significantly, to 42%, among shoppers who are online daily.” “The top “must-haves” for shoppers include: Easy-to-use product search functions; ample information about products and returns; and personalized recommendations.”
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the below noted features of Applicant’s invention. The features of claims 1, 11, and 20 in combination that overcome the prior art are:
Receiving first unstructured data including text data and audio data from a user of the computer system, the first unstructured data communicated, via a device associated with the user, through a chat interface provided by the computer system;
The reply identifying a specific item and requesting from the user to provide one or more additional details about the first unstructured data and the specific item,
Updating the chat interface with the reply generated by the chatbot, the reply including text and audio simulating at the chat interface an interaction of the user with another user of the computer system.
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art. Claims 3-10, and 13-19 depend from claims 1, 11, and 20 and therefore the dependent claims are also indicated as containing allowable subject matter.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that the claims 1, 3-11, and 13-20 are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Response to Arguments
Applicant’s arguments on pages 13-15 of the remarks filed 03/09/2026, with respect to the previous 35
USC § 101 rejections have been fully considered but are not persuasive.
Applicant argues on pages 13-14 of the remarks that the amended claims are not directed to a judicial exception without significantly more. Examiner respectfully disagrees. Receiving first unstructured data including text data and audio data from a user, the first unstructured data communicated, via the user, through a chat interface, applying a natural language process to the first unstructured data to generate a reply, the reply identifying a specific item and requesting from the user to provide one or more additional details about the first unstructured data and the specific item, and updating the chat interface with the reply generated, the reply including text and audio simulating at the chat interface an interaction of the user with another user are all part of the abstract idea. The limitations are directed to generating an order for products. These amount to sales activities and therefore are categorized under The Certain Methods of Organizing Human Activity grouping of Abstract ideas, see MPEP 2106(a)(2)(II). The mere execution of the abstract idea on generic and high-level components such as a “computer system”, a “device” associated with a user, and a “chatbot of the computer system” does not overcome the rejection or provide a practical application. These elements are described at a high level and as generic on ¶0069, ¶0092, and Figs. 1-2 of the instant specification.
Furthermore, merely applying the abstract idea on an interface such as a “chat interface” or a “device of a user of the computer system” to collect data such as “text data and audio data from the user through the chat interface” does not integrate the abstract idea into a practical application or provide a technical improvement.
Accordingly, Examiner maintains that the invention is directed to a judicial exception without
significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the 35 USC §101 rejections are maintained.
Conclusion
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/AHOORA LADONI/Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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