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 .
Priority
Applicant claims priority to provisional U.S. Patent Application No. 63/448117, filed 2/24/2023.
Information Disclosure Statement
The IDS submitted on 9/12/2024 was previously considered.
Status of Claims
Applicant’s amended claims, filed 1/16/2026, have been entered. Claims 1, 8, and 15 have been amended. Claims 1-20 are currently pending in this application and have been examined.
Interview
Examiner invites the representative of this application to contact the Examiner to schedule an interview to expedite prosecution of this application.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Under Step 1 of the Alice/Mayo test the claims are directed to statutory categories. Specifically, the method, as claimed in claims 1-7, are directed to a process, the system, as claimed in claims 8-14, are directed to a machine, and the non-transitory computer readable medium, as claimed in claims 15-20, are directed to an article of manufacture (see MPEP 2106.03).
Under Step 2A (prong 1), claim 1, taken as representative, recites at least the following limitations (emphasis added) that recite an abstract idea:
ingesting a first set of brand content data;
organizing the ingested first set of brand content data into a plurality of embeds and indexes, wherein an embed comprises an embedding of a vector within an embedding space, and wherein the plurality of embeds are retrievable at runtime based on similarity between their vector representations, and storing the plurality of embeds and indexes in a knowledge base;
wherein a location of the embed confers semantic meaning of the content represented by that vector, and wherein an index comprises a data structure that provides a mapping between the brand content data and its location in the knowledge base and a link to metadata associated with the content data;
generating a first user profile that encodes associations between the first user and the plurality of organized embeds and indexes, based at least in part on the plurality of organized embeds and indexes, and a user history of a first user associated with the first user profile;
updating the first user profile based at least in part on one or more interactions between the first user and the first user profile, and one or more models trained on records indicative of one or more processes of human users; and
personalizing one or more responses of a agent interacting with the first user based at least in part on retrieved ones of the plurality of embeds and indexes associated with the first user profile.
These limitations recite certain methods of organizing human activity, such as performing commercial interactions (see MPEP 2106.04(a)(2)(II)). Certain methods of organizing human activity are defined by MPEP 2106.04 as including “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” In this case, the abstract ideas recited in representative claim 1 are certain methods of organizing human activity because generating and updating a user profile and personalizing responses based on the profile is considered managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II).
Thus, claim 1 recites an abstract idea.
Independent claims 8 and 15 recite the same abstract idea as recited in independent claim 1. As such, the analysis under Step 2A, Prong 1 is the same for independent claims 8 and 15 as described above for independent claim 1.
Under Step 2A (prong 2), if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception (see MPEP 2106.04). As stated in the MPEP, when “an additional element merely recites the words ‘apply it (or an equivalent) with the judicial exception, or merely uses a computer as a tool to perform an abstract idea,” the judicial exception has not been integrated into a practical application. In this case, representative claim 1 includes additional elements such as (additional elements are bolded):
ingesting, by a processor, a first set of brand content data;
organizing, by the processor, the ingested first set of brand content data into a plurality of embeds and indexes, wherein an embed comprises an embedding of a vector within an embedding space, and wherein the plurality of embeds are retrievable at runtime based on similarity between their vector representations, and storing the plurality of embeds and indexes in a knowledge base;
wherein a location of the embed confers semantic meaning of the content represented by that vector, and wherein an index comprises a data structure that provides a mapping between the brand content data and its location in the knowledge base and a link to metadata associated with the content data;
generating, by the processor, a first user profile that encodes associations between the first user and the plurality of organized embeds and indexes, based at least in part on the plurality of organized embeds and indexes, and a user history of a first user associated with the first user profile;
updating, by the processor, the first user profile based at least in part on one or more interactions between the first user and the first user profile, and one or more models trained on records indicative of one or more processes of human users; and
personalizing, by the processor, one or more responses of a conversational agent interacting with the first user based at least in part on retrieved ones of the plurality of embeds and indexes associated with the first user profile.
Independent claims 8 and 15 recite the additional elements of “a computer having a processor and a memory,” “one or more code sets stored in the memory and executed by the processor,” and “a non-transitory computer-readable medium storing computer-program instructions, that when executed by one or more processors, cause the one or more processors to effectuate operations” in addition to the additional elements already addressed in the rejection for independent claim 1.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. These additional elements merely amount to the general application of the abstract idea to a technical environment (“by a processor”, “conversational agent”, “a computer having a processor and a memory,” “one or more code sets stored in the memory and executed by the processor,” and “a non-transitory computer-readable medium storing computer-program instructions, that when executed by one or more processors, cause the one or more processors to effectuate operations”) and insignificant pre-and-post solution activity (receiving/ingesting information, storing information). The specification makes clear the general-purpose nature of the technological environment. This is because the additional elements of claims 1, 8, and 15 are recited 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 the abstract idea) (see Fig. 5; paragraphs [0039] “A conversational agent, as understood herein, is any dialogue system that conducts natural language processing (NLP) and responds automatically using human language” and ¶¶00140-00152). The specification indicates that while exemplary general-purpose systems may be specific for descriptive purposes, any elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim. The description demonstrates that these additional elements are merely generic devices such as a generic computer. Further, the additional elements do no more than generally link the use of a judicial exception to a particular environment or field of use (such as the Internet or computing networks).
Therefore, considered both individually and as an ordered pair, the additional elements do no more than generally link the use of the abstract idea to a particular technological environment or field of use. That is, given the generality with which the additional elements are recited, the limitations do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim. Additionally, the claims as currently recited, do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not transform or reduction of a particular article to a different state or thing; and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technology environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea into a practical application, and is therefore “directed to” the abstract idea.
In addition to the above, the recited(receiving/ingesting and storing steps (even assuming arguendo they do not form part of the abstract idea, which the Examiner does not acquiesce), are at best little more than extra-solution activity (e.g., data gathering, presentation of data) that contributes nominally or insignificantly to the execution of the claimed system (see MPEP 2106.05(g)).
In view of the above, under Step 2A (prong 2), claims 1, 8, and 15 do not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to claims 1, 8, and 15, taken individually or as a whole the additional elements of claims 1, 8, and 15 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). 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 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.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claims 1, 8, and 15 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data over a network,
storing or retrieving information from memory,
presenting offers
Additionally, the Specification recites a conversational agent is a well-understood, routine, and conventional activities previously known to the industry (see ¶0039 [““A conversational agent, as understood herein, is any dialogue system that conducts natural language processing (NLP) and responds automatically using human language”]).
Even considered as an ordered combination (as a whole), the additional elements of claims 1, 8, and 15 do not add anything further than when they are considered individually.
In view of the above, representative claims 1, 8, and 15 do not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Regarding claims 4-7, 11-14, and 18-20
Dependent claim(s) 4-7, 11-14, and 18-20, 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 claim(s) 4-7, 11-14, and 18-20 merely further define the abstract limitations of claim(s) 1, 8, and 15 or provide further embellishments of the limitations recited in independent claim claim(s) 1, 8, and 15.
Claims 4-7, 11-14, and 18-20 set forth:
wherein the one or more models trained on records indicative of one or more processes of human users comprises: one or more models trained on records indicative of biological cognitive and neuroscience processes of human users that provide information about at least one of a user's thought processes, behavior patterns, motivations, or biases.
wherein the first user profile is updated in real time.
further comprising: generating one or more customized content recommendations for the first user based at least in part on the first user profile; and providing the one or more customized content recommendations to the first user by the conversational agent.
further comprising: analyzing, by the processor, inputs from a plurality of users responsive to interactions with respective conversational agents; extracting, by processor, one or more insights associated with interactions with the plurality of users; and providing, by the processor, one or more data-driven recommendations regarding at least one of improvements to responses of respective conversational agents, improvements to one or more services provided, or system performance.
Such recitations merely embellish the abstract idea of generating and updating a user profile and personalizing responses based on the profile. The claims do not set forth any further additional limitations, and therefore such abstract embellishments are applied to the additional limitations recited in claim(s) 1, 8, and 15, which do no more than generally link the use of the abstract idea to a particular technological environment, do not integrate the abstract idea into a practical application, and do not provide an inventive concept. Accordingly, the claims do not confer eligibility on the claimed invention and is ineligible for similar reasons to claim(s) 1, 8, and 15.
Thus, dependent claims 4-7, 11-14, and 18-20 are ineligible.
Regarding claims 2, 3, 9, 10, 16, and 17
Dependent claim(s) 2, 3, 9, 10, 16, and 17 sets forth:
wherein ingesting the first set of brand content data comprises: processing the first set of brand content data using one or more machine learning algorithms; and identifying one or more insights regarding the first set of brand content data.
wherein the one or more insights comprise at least one of tone, language, audience engagement, intent, mood, receptiveness, skill, expertise, or understanding.
Such recitations merely embellish the abstract idea of generating and updating a user profile and personalizing responses based on the profile. While the claim(s) do set forth the additional elements of “machine learning algorithms”, these recitations are similar to the additional limitations in claims 1, 8, and 15, as they do no more than generally link the use of the abstract idea to a particular technological environment. That is these additional elements merely amount to the general application of the abstract idea to a technical environment. The specification makes clear the general-purpose nature of the technological environment. Paragraphs [0034], [0039], [0042], [0097], [00103], [00115]-[00121], [00140]-[00152] indicate that while exemplary general-purpose systems may be specific for descriptive purposes, any elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim. Therefore, these additional elements do not integrate the abstract idea into a practical application because they merely amount to using a computer to apply the abstract idea and no more than a general link of the use of the abstract idea to a particular technological environment or field of use and thus do not act to integrate the abstract idea into a practical application of the abstract idea. Further, the “machine learning algorithms” is recited at a high level and amounts to merely applying the abstract idea.
Additionally, the additional elements do not amount to significantly more because they merely amount to using a computer to apply the abstract idea and amount to no more than a general link of the use of the abstract idea to a particular technological environment.
Thus, dependent claims 2, 3, 9, 10, 16, and 17 are also ineligible.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sejpal et al. (US 2022/0270594 A1 [previously recited]) in view of Kopru et al. (US 2023/0101174 A1).
Regarding claim 1, Sejpal et al., hereinafter Sejpal, discloses a method for providing adaptive and interactive AI-driven profiles (abstract), comprising:
ingesting, by a processor, a first set of brand content data (¶0022 [converting the utterance to text and determining a customer intent based on the text and a user history. The operations include determining a user model of the customer based on the text and the customer intent and updating a conversation state associated with the conversation based on the customer intent and the user model. The user model may include one or more of: an urgency level of the customer, a comfort level associated with the customer in conversing with the artificial intelligence engine, a cost sensitivity of the customer, or any combination thereof.] in view of ¶¶0016-0018 [based on brand] and ¶¶0025-0026 [The AI engine(s) 110 may keep a running track of an order context 120 associated with each particular order. The order context 120 may include order data associated with previously placed orders by the customer 142, trending items in a region in which the customer 142 is located, specials/promotions (e.g., buy one get one free (BOGO), limited time specials, regional specials, and the like) that the restaurant 132 is currently promoting (e.g., on social media, television, and other advertising media), and other context-related information. The order context 120 may include user preferences, such as gluten allergy, vegan, vegetarian, or the like. The user may specify the preferences or the AI engines 110 may determine the preferences based on the customer's order history…conversations between human employees and customers may be stored as conversation data 136. The conversation data 136 is used to train a software agent 116 to take orders from customers in a manner similar to a human employee, such that the customers may be unaware that they are interacting with the software agent 116 rather than a human employee]; Examiner notes paragraph [0042] of the instant Specification recites “a brand may refer to the identity of a company, person, persona, product, service, or concept, which makes it distinguishable from others”);
organizing, by the processor, the ingested first set of brand content data into a plurality of indexes, a vector within a space, and wherein the plurality are retrievable at runtime based on a similarity between their vector representations, and storing the plurality of indexes in a knowledge base (Figs. 1-4; ¶0096 and ¶¶0037-0038 in view of ¶¶0016-0018 [The conversation data is collected and used to train the AI engine to enable the AI engine to interact with customers in a human-like conversation… the software agent learns dialog policies at runtime by identifying a model for the customer and adjusting actions (including responses to the customer) based on the model. For example, upselling may be based on brand and/or product specific upselling rules that are set by each specific brand, store, or franchise. The software agent may make decisions based in part on one or more community models in which the software agent learns user behavior associated with different groups to which individual customers may belong. For example, the groups may include groups defined by region (e.g., people living in a particular geographic area tend to order X and Y but not Z). The groups may include groups defined by gender (e.g., males tend to order X and women tend to order Y). The groups may include groups defined by age (e.g., 12 years old and under tend to order A, 13- to 19-year-old tend to order B or C, 20- to 30-year-old tend to order D, and so on). The software agent may make personalized model-based recommendation based on learning user preferences based on a customer's previous order history], ¶0020, ¶¶0025-0026 [The AI engine(s) 110 may keep a running track of an order context 120 associated with each particular order. The order context 120 may include order data associated with previously placed orders by the customer 142, trending items in a region in which the customer 142 is located, specials/promotions (e.g., buy one get one free (BOGO), limited time specials, regional specials, and the like) that the restaurant 132 is currently promoting (e.g., on social media, television, and other advertising media), and other context-related information. The order context 120 may include user preferences, such as gluten allergy, vegan, vegetarian, or the like. The user may specify the preferences or the AI engines 110 may determine the preferences based on the customer's order history…employee entries may be used as labels when training the AI engine(s) 110 and various machine learning (ML) models in the NLP pipeline 112. The AI engine(s) 110 may keep a running track of an order context 120 associated with each particular order. The order context 120 may include order data associated with previously placed orders by the customer 142, trending items in a region in which the customer 142 is located, specials/promotions (e.g., buy one get one free (BOGO), limited time specials, regional specials, and the like) that the restaurant 132 is currently promoting (e.g., on social media, television, and other advertising media), and other context-related information. The order context 120 may include user preferences, such as gluten allergy, vegan, vegetarian, or the like. The user may specify the preferences or the AI engines 110 may determine the preferences based on the customer's order history. For example, if the customer 142 orders gluten-free products more than once, then the AI engines 110 may determine that the customer 142 is gluten intolerant and add gluten intolerance to the customer's preference file. As another example, if the customer 142 orders vegan or vegetarian items (or customizes menu items to be vegan or vegetarian) then the AI engines 110 may determine that the customer 142 is vegan or vegetarian and add vegan or vegetarian to the customer's preference file] and ¶¶0039-0045);
wherein a location confers semantic meaning of the content represented by that vector (¶¶0037-0038 [the encoder 210 may use word2vec, a two-layer neural net, to process the text 207 to create the utterance vector 212. The input to the NLP pipeline 112 is a text corpus and the output is a set of vectors, e.g., feature vectors that represent words in that corpus. The encoder 210 thus converts the text 207 into a numerical form that deep neural networks can understand. The encoder 210 looks for transitional probabilities between states, e.g., the likelihood that two states will co-occur. The NLP pipeline 112 groups vectors of similar words together in vector space to identify similarities mathematically. The vectors are distributed numerical representations of features, such as menu items. Given enough data, usage, and contexts during training, the encoder 210 is able to make highly accurate predictions about a word's meaning based on past appearances. The predictions can be used to establish the word's association with other words (e.g., “man” is to “boy” what “woman” is to “girl”), or cluster utterances and classify them by topic. The clusters may form the basis of search, sentiment analysis, and recommendations. The output of the encoder 210 is a vocabulary in which each item has a vector attached to it, which can be fed into a deep-learning net or simply queried to detect relationships between words] and ¶0048 [a domain specific ontology 250 may be added as semantic representation of items in the knowledge base (e.g., the conversation data 136). The ontology 250 allows the encoder 210 to identify specific entities with which to select the correct modification to operate on the cart 126. The ontology 250 may be used to facilitate the onboarding of new items or whole semantic fields, alleviate the need for annotated data for each label (e.g., the entries of the employee into the EA-POS 102), and improve the performance of the NLP pipeline 112]) and, wherein an index comprises a data structure that provides a mapping between the brand content data and its location in the knowledge base and a link to metadata associated with the content data (Figs. 1-2; ¶¶0037-0038 [order context 120, including the interaction history 222, the cart state 224, and the conversation state 226, are provided to the encoder 210 in the form of structured data 209. The structured data 209 includes defined data types that enable the structured data 209 to be easily searched. Unstructured data is raw text, such as “two pizzas with sausage and pepperoni”. Structured data may use a structured language, such as JavaScript Object Notation (JSON), Structured Query Language (SQL), or the like to represent the data. For example, “two pizzas with sausage and pepperoni” may be represented using structured data as: {“Quantity”: 2, “Item”: “Pizza”, “Modifiers”: [“Pepperoni”, “Sausage”]}. In structured data 209, each data item has an identifier or some fixed structured meaning and is not subject to natural language meaning or interpretation. The order context 120 captures where the customer 142 and the software agent 116 are in the conversation 111 (e.g., what has already been said), what items are in the cart 126, and the like]);
generating, by the processor, a first user profile based at least in part on the plurality of organized embeds and indexes, and a user history of a first user associated with the first user profile (Figs. 1-3 and 6; ¶¶0016-0018 and ¶¶0025-0026 [The AI engine(s) 110 may keep a running track of an order context 120 associated with each particular order. The order context 120 may include order data associated with previously placed orders by the customer 142, trending items in a region in which the customer 142 is located, specials/promotions (e.g., buy one get one free (BOGO), limited time specials, regional specials, and the like) that the restaurant 132 is currently promoting (e.g., on social media, television, and other advertising media), and other context-related information. The order context 120 may include user preferences, such as gluten allergy, vegan, vegetarian, or the like. The user may specify the preferences or the AI engines 110 may determine the preferences based on the customer's order history. For example, if the customer 142 orders gluten-free products more than once, then the AI engines 110 may determine that the customer 142 is gluten intolerant and add gluten intolerance to the customer's preference file. As another example, if the customer 142 orders vegan or vegetarian items (or customizes menu items to be vegan or vegetarian) then the AI engines 110 may determine that the customer 142 is vegan or vegetarian and add vegan or vegetarian to the customer's preference file. The cart 126 may include other information as how the order is to be fulfilled (e.g., pickup or delivery), customer address for delivery, customer contact information (e.g., email, phone number, etc.), and other customer information….conversations between human employees and customers may be stored as conversation data 136. The conversation data 136 is used to train a software agent 116 to take orders from customers in a manner similar to a human employee, such that the customers may be unaware that they are interacting with the software agent 116 rather than a human employee], ¶0026, ; Examiner notes paragraph [0042] of the instant Specification recites “a brand may refer to the identity of a company, person, persona, product, service, or concept, which makes it distinguishable from others”);
updating, by the processor, the first user profile based at least in part on one or more interactions between the first user and the first user profile, and one or more models trained on records indicative of one or more processes of human users (Figs. 1-2, 6; ¶¶0025-0026); and
personalizing, by the processor, one or more responses of a conversational agent interacting with the first user based at least in part on retrieved ones of the plurality of indexes associated with the first user profile (Figs. 1-2, 6; ¶0018, ¶¶0025-0026, ¶¶0038-0051).
While Sejpal discloses organizing, by the processor, the ingested first set of brand content data into a plurality of indexes, a vector within a space, and wherein the plurality are retrievable at runtime based on a similarity between their vector representations, and storing the plurality of indexes in a knowledge base (Figs. 1-4; ¶0096 and ¶¶0037-0038 in view of ¶¶0016-0018, ¶0020, ¶¶0025-0026, and ¶¶0039-0045), wherein a location confers semantic meaning (¶¶0037-0038), and generating, by the processor, a first user profile based at least in part on the plurality of organized embeds and indexes, and a user history of a first user associated with the first user profile (Figs. 1-3 and 6; ¶¶0016-0018 and ¶¶0025-0026), Sejpal does not explicitly disclose organizing, by the processor, the ingested first set of brand content data into a plurality of embeds, wherein an embed comprises an embedding of a vector within an embedding space, and wherein the plurality of embeds are retrievable and storing the plurality of embeds in a knowledge base, wherein a location of the embed confers semantic meaning, and generating a first user profile that encodes associations between the first user and the plurality of organized embeds and indexes. However, in the field of generating embedded vector data, Kopru et al., hereinafter Kopru, teaches organizing data into a plurality of embeds and indexes, wherein an embed comprises an embedding of a vector within an embedding space and wherein the plurality of embeds are retrievable and storing the plurality of embeds in a knowledge base, wherein a location of the embed confers semantic meaning, and generating a first user profile that encodes associations between the first user and the plurality of organized embeds and indexes (Figs. 1-3, 5, 7; ¶¶0008-0009, ¶¶0021-0026, ¶¶0029-0030, ¶0033, ¶¶0035-0044,¶¶0049-0054). The step of Kopru is applicable to the method of Sejpal as they share characteristics and capabilities, namely, they are directed to analyzing and processing data. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the organized and stored information as taught by Sejpal with the embeds and vector indexes as taught by Kopru. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Sejpal in order to automatically generate similarity index data using a machine learning model and using the similarity index data to determine similarities among items, products, users, and queries by generating embedded vector data using a model that indicates vector representations of similarities (¶0004 and ¶0008).
Regarding claim 2, Sejpal in view of Kopru teaches the method as in claim 1, Sejpal further discloses wherein ingesting the first set of brand content data comprises:
processing the first set of brand content data using one or more machine learning algorithms (Fig. 4; ¶0010 [FIG. 4 is a block diagram that includes a machine learning algorithm to modify a text-based representation of one or more utterance(s), according to some embodiments.] and ¶¶0064-0105); and
identifying one or more insights regarding the first set of brand content data (abstract, ¶0005 [a server may receive an utterance from a customer. The utterance may be included in a conversation between the artificial intelligence engine and the customer. The server may convert the utterance to text and determine a customer intent based on the text and a user history. The server may determine a user model of the customer based on the text and the customer intent. The server may update a conversation state associated with the conversation based on the customer intent and the user model. The server may determine a user state based on the user model and the conversation state. The server may select, using a reinforcement learning based module, a particular action from a set of actions, the particular action including a response and provide the response to the customer] in view of ¶0021 and ¶0043).
Regarding claim 3, Sejpal in view of Kopru teaches the method as in claim 2, Sejpal further discloses wherein the one or more insights comprise at least one of tone, language, audience engagement, intent, mood, receptiveness, skill, expertise, or understanding (abstract, ¶0005 [a server may receive an utterance from a customer. The utterance may be included in a conversation between the artificial intelligence engine and the customer. The server may convert the utterance to text and determine a customer intent based on the text and a user history. The server may determine a user model of the customer based on the text and the customer intent. The server may update a conversation state associated with the conversation based on the customer intent and the user model. The server may determine a user state based on the user model and the conversation state. The server may select, using a reinforcement learning based module, a particular action from a set of actions, the particular action including a response and provide the response to the customer] in view of ¶0021 and ¶0043).
Regarding claim 4, Sejpal in view of Kopru teaches the method as in claim 1, Sejpal further discloses wherein the one or more models trained on records indicative of one or more processes of human users comprises: one or more models trained on records indicative of biological cognitive and neuroscience processes of human users that provide information about at least one of a user's thought processes, behavior patterns, motivations, or biases (¶0020 [determine a type of customer based on the words the customer uses, the speed of the delivery of the words, the inflection and pitch of the words, one or more emotions conveyed by the words, other information, or any combination thereof. The type of customer is used to determine a user model. The systems and techniques use the user model and a conversation state to adapt, in real-time, the dialog flow and recommendations. By gaining a deeper understanding of the customer, the systems and techniques are able to improve the entire user experience when placing an order. The systems and techniques focus on three areas: (1) when to recommend (e.g., based on urgency detection and the like), (2) what to recommend (e.g., based on cost-sensitivity and the like), and (3) verbosity level that balances naturalness (e.g., chattiness) with step-by-step guidance. The dialog is adapted using reinforcement learning in which a point is added (reward) or subtracted (punishment) based on whether a recommendation is provided or not provided, whether the provided recommendation is accepted or rejected, an urgency level (e.g., reward if customer praises fast order taking, punish if customer complains about slow process of placing an order), and cost sensitivity (does customer express gratitude or displeasure for individual items, total order cost, or both). The systems and techniques may weight dialog naturalness (e.g., chatty) and step-by-step instructions (e.g., guided). A task completion reward may be associated with whether a recommendation was accepted or rejected, a number of turns in the conversation (one complete turn is the customer speaking followed by a response from the software agent), an urgency level of the customer (in a hurry or relaxed), and a proficiency of the customer in terms of interacting with an AI]).
Regarding claim 5, Sejpal in view of Kopru teaches the method as in claim 1, Sejpal further discloses wherein the first user profile is updated in real time (¶0111 [ an AI engine may converse with a customer to receive an order and use reinforcement learning to adapt to the customer in real time. Adapting to the customer may include determining whether the customer is in a hurry by (1) determining whether the customer's choice of words indicates urgency, (2) determining whether the customer is cost sensitive based on the customer's choice of words that indicate that the customer has a limited budget or is otherwise price conscious, and (3) determining the customer's comfort level conversing with an AI and adjusting the verbosity of the output such that customers that are comfortable conversing with an AI may receive relatively non-verbose responses from the AI engine while customers that are inexperienced conversing with an AI may receive relatively more verbose responses that include more step-by-step instructions on placing an order with an AI. In this way, the AI is able to adjust to the particular characteristics of a customer in real time, thereby providing a pleasing experience to the customer by adapting to the customer rather than having the customer to adapt to the AI.] in view of ¶¶0016-0018 and ¶¶0025-0026).
Regarding claim 6, Sejpal in view of Kopru teaches the method as in claim 1, Sejpal further discloses comprising:
generating one or more customized content recommendations for the first user based at least in part on the first user profile (Figs. 6-8; ¶¶0110-0111 [ an AI engine may converse with a customer to receive an order and use reinforcement learning to adapt to the customer in real time. Adapting to the customer may include determining whether the customer is in a hurry by (1) determining whether the customer's choice of words indicates urgency, (2) determining whether the customer is cost sensitive based on the customer's choice of words that indicate that the customer has a limited budget or is otherwise price conscious, and (3) determining the customer's comfort level conversing with an AI and adjusting the verbosity of the output such that customers that are comfortable conversing with an AI may receive relatively non-verbose responses from the AI engine while customers that are inexperienced conversing with an AI may receive relatively more verbose responses that include more step-by-step instructions on placing an order with an AI. In this way, the AI is able to adjust to the particular characteristics of a customer in real time, thereby providing a pleasing experience to the customer by adapting to the customer rather than having the customer to adapt to the AI.] in view of ¶¶0016-0018 and ¶¶0025-0026); and
providing the one or more customized content recommendations to the first user by the conversational agent (Figs. 6-8; ¶¶0110-0114 [ an AI engine may converse with a customer to receive an order and use reinforcement learning to adapt to the customer in real time. Adapting to the customer may include determining whether the customer is in a hurry by (1) determining whether the customer's choice of words indicates urgency, (2) determining whether the customer is cost sensitive based on the customer's choice of words that indicate that the customer has a limited budget or is otherwise price conscious, and (3) determining the customer's comfort level conversing with an AI and adjusting the verbosity of the output such that customers that are comfortable conversing with an AI may receive relatively non-verbose responses from the AI engine while customers that are inexperienced conversing with an AI may receive relatively more verbose responses that include more step-by-step instructions on placing an order with an AI. In this way, the AI is able to adjust to the particular characteristics of a customer in real time, thereby providing a pleasing experience to the customer by adapting to the customer rather than having the customer to adapt to the AI.] in view of ¶¶0016-0018 and ¶¶0025-0026).
Regarding claim 7, Sejpal in view of Kopru teaches the method as in claim 1, Sejpal further discloses further comprising:
analyzing, by the processor, inputs from a plurality of users responsive to interactions with respective conversational agents (¶0018 [RL builds on top of supervised models to improve dialog policies based on overall reward for a complete conversation. For example, rewards may be higher (positive) for success, lower (negative) for rejection, and neutral (zero) for everything else. Based on the reward, the software agent learns dialog policies at runtime by identifying a model for the customer and adjusting actions (including responses to the customer) based on the model. For example, upselling may be based on brand and/or product specific upselling rules that are set by each specific brand, store, or franchise. The software agent may make decisions based in part on one or more community models in which the software agent learns user behavior associated with different groups to which individual customers may belong. For example, the groups may include groups defined by region (e.g., people living in a particular geographic area tend to order X and Y but not Z). The groups may include groups defined by gender (e.g., males tend to order X and women tend to order Y). The groups may include groups defined by age (e.g., 12 years old and under tend to order A, 13- to 19-year-old tend to order B or C, 20- to 30-year-old tend to order D, and so on). The software agent may make personalized model-based recommendation based on learning user preferences based on a customer's previous order history]);
extracting, by processor, one or more insights associated with interactions with the plurality of users (¶0018 [RL builds on top of supervised models to improve dialog policies based on overall reward for a complete conversation. For example, rewards may be higher (positive) for success, lower (negative) for rejection, and neutral (zero) for everything else. Based on the reward, the software agent learns dialog policies at runtime by identifying a model for the customer and adjusting actions (including responses to the customer) based on the model. For example, upselling may be based on brand and/or product specific upselling rules that are set by each specific brand, store, or franchise. The software agent may make decisions based in part on one or more community models in which the software agent learns user behavior associated with different groups to which individual customers may belong. For example, the groups may include groups defined by region (e.g., people living in a particular geographic area tend to order X and Y but not Z). The groups may include groups defined by gender (e.g., males tend to order X and women tend to order Y). The groups may include groups defined by age (e.g., 12 years old and under tend to order A, 13- to 19-year-old tend to order B or C, 20- to 30-year-old tend to order D, and so on). The software agent may make personalized model-based recommendation based on learning user preferences based on a customer's previous order history])); and
providing, by the processor, one or more data-driven recommendations regarding at least one of improvements to responses of respective conversational agents, improvements to one or more services provided, or system performance (¶0018 [RL builds on top of supervised models to improve dialog policies based on overall reward for a complete conversation. For example, rewards may be higher (positive) for success, lower (negative) for rejection, and neutral (zero) for everything else. Based on the reward, the software agent learns dialog policies at runtime by identifying a model for the customer and adjusting actions (including responses to the customer) based on the model. For example, upselling may be based on brand and/or product specific upselling rules that are set by each specific brand, store, or franchise. The software agent may make decisions based in part on one or more community models in which the software agent learns user behavior associated with different groups to which individual customers may belong. For example, the groups may include groups defined by region (e.g., people living in a particular geographic area tend to order X and Y but not Z). The groups may include groups defined by gender (e.g., males tend to order X and women tend to order Y). The groups may include groups defined by age (e.g., 12 years old and under tend to order A, 13- to 19-year-old tend to order B or C, 20- to 30-year-old tend to order D, and so on). The software agent may make personalized model-based recommendation based on learning user preferences based on a customer's previous order history])).
Regarding claim 8, the claim discloses substantially the same limitations, as claim 1, except claim 1 is directed to a process while claim 8 is directed to a machine. The added element of “a computer having a processor and a memory” and “one or more code sets stored in the memory and executed by the processor” is also taught by Sejpal (Fig. 9; ¶0022, ¶0105, ¶¶0126-0133). Therefore, claim 8 is rejected for the same rational over the prior art.
Regarding claims 9-14, the claims disclose substantially the same limitations, as claims 2-7, except claims 2-7 are directed to a process while claims 9-14 are directed to a machine. All limitations as recited have been analyzed and rejected with respect to claims 2-7, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claims 9-14 are rejected for the same rational over the prior art cited in claims 2-7.
Regarding claim 15, the claim discloses substantially the same limitations, as claim 1, except claim 1 is directed to a process while claim 15 is directed to an article of manufacture. The added element of “a non-transitory computer-readable medium storing computer-program instructions that, when executed by one or more processors, cause the one or more processors to effectuate operations” is also taught by Sejpal (Fig. 9; ¶0022, ¶0105, ¶¶0126-0133). Therefore, claim 15 is rejected for the same rational over the prior art.
Regarding claims 16-20, the claims disclose substantially the same limitations, as claims 2-4, 6, and 7, except claims 2-4, 6, and 7 are directed to a process while claims 16-20 are directed to a machine. All limitations as recited have been analyzed and rejected with respect to claims 2-4, 6, and 7, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claims 16-20 are rejected for the same rational over the prior art cited in claims 2-4, 6, and 7.
Response to Arguments
Applicant’s arguments, on page 9 of the Remarks filed 1/16/2026, with respect to the previous claim objections have been fully considered and are persuasive in view of the claim amendments. Accordingly, the previous claim objections have been withdrawn.
Applicant’s arguments, on pages 9-12 of the Remarks filed 1/16/2026, with respect to the previous 35 USC §101 rejections have been fully considered but are not persuasive.
Specifically, Applicant argues the claims are not directed to an abstract idea, but instead integrate any alleged abstraction into a practical application that improves the operation of computing systems themselves. Examiner respectfully disagrees. While Applicant argues on pages 10-12 of the Remarks filed 1/16/2026 that the claims are directed to improvements to conversational AI systems, Examiner respectfully disagrees. As currently claimed, the claims do not recite improvements to conversational AI systems. While the specification may disclose sufficient details such that one of ordinary skill in the art would recognize providing an improvement to conventional artificial intelligence systems, the claim itself must reflect the improvement in technology (see MPEP 2106.04(a)(I)). Applicant’s arguments rely on language solely recited in preamble recitations in claim(s) 1, 8, and 15 and the abstract ideas. When reading the preamble in the context of the entire claim, the recitation “providing adaptive and interactive AI-driven profiles” is not limiting because the body of the claim describes a complete invention and the language recited solely in the preamble does not provide any distinct definition of any of the claimed invention’s limitations. Thus, the preamble of the claim(s) is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02. The independent claims themselves only recite the additional limitations of “a processor,” “a computer having a processor and a memory,” “one or more code sets stored in the memory and executed by the processor,” “a non-transitory computer-readable medium storing computer-program instructions, that when executed by one or more processors, cause the one or more processors to effectuate operations,” and “a conversational agent.” As noted above in the full rejection of the claims, these additional elements merely amount to the general application of the abstract idea to a technical environment. The specification makes clear the general-purpose nature of the technological environment. Paragraphs [0034], [0039] (“A conversational agent, as understood herein, is any dialogue system that conducts natural language processing (NLP) and responds automatically using human language”), [0042], [0097], [00103], [00115]-[00121], [00140]-[00152] indicate that while exemplary general-purpose systems may be specific for descriptive purposes, any elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim. Therefore, these claimed additional elements do not integrate the abstract idea into a practical application because they merely amount to using a computer to apply the abstract idea and no more than a general link of the use of the abstract idea to a particular technological environment or field of use and thus do not act to integrate the abstract idea into a practical application of the abstract idea.
Further, Applicant argues improvements to limitations that, as currently claimed, are directed to the abstract idea. Examiner notes the Specification recites an “embed is a representation of a piece of text, image, audio, or other media or data, that captures the essence and meaning of the original content. In some embodiments, the embed corresponds to the embedding of a vector within an embedding space, and the location of that embed confers semantic meaning of the content represented by that vector. An index on the other hand, is a data structure that provides a mapping between content and its location in the knowledge base, as well as a link to any other metadata associated with the content such as its source, or access permissions. Together, embeds and indexes may afford embodiments of the system to quickly retrieve relevant information from the knowledge base and use it to inform a decision making process and deliver a personalized experience.” See paragraphs [0043]-[0044]. The broadest reasonable interpretation of embeds and indexes (including corresponding to a vector), as currently claimed and described within the specification does not require a computing device and is not limited to artificial intelligence; therefore as currently claimed, these limitation are directed to the abstract idea. Abstract ideas are not patent eligible, therefore this limitation cannot provide integration.
While Applicant argues on pages 10-12 of the Remarks filed 1/16/2026 that the claims are similar to the claims in Enfish, McRO, BASCOM, DDR, and Ex Parte Desjardins Examiner respectfully disagrees. While the specification provided descriptive support that the claimed invention achieves benefits over the conventional technology in Enfish, McRO, BASCOM, DDR, and Ex Parte Desjardins, unlike the instant claims, the claims in Enfish, McRO, BASCOM, DDR, and Ex Parte Desjardins also reflected the improvement in technology described in the Specification (see MPEP 2106.04(a)(I)). In the instant application, the additional elements described in the Specification that are argued within the Remarks as providing the technical improvement are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Examiner recommends amending the claims to include the additional elements described in the Specification as providing a technological improvement to conventional conversational artificial intelligence systems.
Accordingly, Examiner maintains the 35 USC §101 rejection of the claims.
Applicant’s arguments, on pages 13-16 of the Remarks filed 1/16/2026, with respect to the previous 35 USC §103 rejections have been fully considered but are mostly moot in view of the new 35 USC §103 rejections applied to applicant’s amended claims. While Applicant argues on pages 13-14 that Sejpal does not disclose organizing brand content into a semantic vector embeddings in an embedding space, nor does it disclose storing such embeddings in a knowledge base for runtime similarity-based retrieval, Examiner notes that in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Due to the amendments to the claims, new 103 rejections have been applied. However, Examiner notes Sejpal discloses storing generated vectors and grouped vectors of similar words together in vector space to identify similarities mathematically and cluster utterances and classify them by topic (¶¶0038-0039).
While Applicant argues on pages 14-16 of the Remarks a narrow definition of claim language based on the Specification, Examiner once again notes that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Accordingly, as noted above in the full rejection of the claims, new 103 rejections have been applied to the amended claims and the Examiner maintains the broadest reasonable interpretation of the claimed limitations are taught by Sejpal in view of Kopru for the reasons noted above in the full rejection of the claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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LINDSEY B. SMITH
Examiner
Art Unit 3688
/LINDSEY B SMITH/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
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