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 .
Response to amendment
This office action is in response to an amendment filed on July 17, 2026 in response to PTO office action dated March 17, 2026. Claims 1, 2, 5, 9, 10, 12, 14, 17, 18 have been amended. Claims 3-4, 6-8, 12, 15 and 19 have been cancelled. New claims 21-26 have been added. As a result, claims 1-2, 5, 9-11, 13-14, 16-18, 20-26 are pending. The amendment has been entered and considered.
Applicant’s amendment to the abstract with respect to the rejection of claims under 35 U.S.C. § 101 has been fully considered. As a result, the rejection has been withdrawn.
Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 103(a) have been fully considered bur are moot in view of the new grounds of rejection.
This action is FINAL.
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 of this title, 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 1-2, 5, 9-11, 13-14, 16-18 and 20-26 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Temkin et al. (US 20200251111 A1) in view of in view of Taheri et al. (US 20250117836 A1) further in view of Abhyankar et al. (US 20200250217 A1).
Regarding claims 1, 13 and 17 Temkin disclose a method performed by one or more computers, the method comprising: accessing, by the one or more computers, data for an information card corresponding to an entity (see Temkin paragraph [0095], the computer system can identify the data objects included in or used by those items, as well as the data source(s) that provide the items, and the computer system can proceed to make the identified set of data objects available through an interactive agent. For example, a user could select an information card for his organization, rather than selecting a data set, and an option can be provided to make the data in the information card available through an artificial intelligence service or other platform; see Temkin paragraph [0005], the system has access to templates or specifications for information cards that are provided to summarize key attributes and metrics of entities (e.g., people, locations, companies, etc.). These information cards may be typically displayed on a screen when contextual factors refer to the corresponding entities),
displaying, by the one or more computers, the information card, wherein the information card includes (i) content including …one or more attributes or measures for the entity (see Temkin paragraph [0005] the system has access to templates or specifications for information cards that are provided to summarize key attributes and metrics of entities (e.g., people, locations, companies, etc.); see Temkin paragraphs [0020]-[0021], providing user interface data for a user interface that displays the information card, the user interface being interactive to enable a user to select portions of the displayed information card. In response to selection by a user of a portion of the displayed information card…the information card corresponds to an entity type, the data set has a plurality of data objects for the entity type, and the information card is configured to present only a proper subset of the data objects for the entity type) and (ii) a chatbot interface comprising one or more interactive user interface elements including a text entry field configured to receive user prompts to a chatbot (see Temkin paragraph [0055], to generate an interactive agent module, e.g., chatbot 135 or other application, service or module, from a data set. The system then uses the chatbot 135 with the rest of the system to respond to a user's query);
after user interaction with the one or more elements provided by the information card (see Temkin paragraph [0008], accessing, by the one or more computers, data identifying an information card configured to present data objects of a data set), receiving, by the one or more computers, a user prompt entered by the user in the text entry field of the chatbot interface in the information card (see; Taheri paragraph [0052], the LLM described herein may be trained based on custom-defined prompts to draw out specific attributes associated with a user's goal. These same prompts may be output during a live execution of the LLM. For example, a user may input a goal description and other attributes); and
in response to receiving user input of the user prompt in the text entry field of the chatbot interface in the information card, obtaining, by the one or more computers, … response to the user prompt, wherein the …response is generated using the one or more AI/ML models processing the user prompt and at least a portion of the content of the information card (see Temkin paragraph [0011], providing a user interface comprising controls to design and/or edit the information card; receiving, through the user interface, data indicating user input selecting data objects to present in the information card; and generating and/or updating specification data for the information card to include the selected data objects in the information card. The voice response application is generated based on the updated specification data for the information card to provide information for the selected data objects; see Temkin paragraph [0060] After the chatbot 135 is generated, the server 130 can publish the chatbot 135 to the NLP server 120. The chatbot 135 is then used to interpret and respond to questions from a user. For example, when a user speaks a question, the user device 110 captures the audio with a microphone and sends audio data over a network 115 to the server 117).
Abhyankar expressly discloses displaying, by the one or more computers, the information card content including values of one or more attributes or measures for the entity (see Abhyankar paragraph [0054], the server system 110 send the response 152 to the request 150. The server system 110 also sends card data 154 for information cards that are relevant to the context of the client device 105c, as determined above by finding objects in the semantic graph that are related to the request 150, the response 152, or other context information for the client device 105c (e.g., a user identifier for the user, a history of recent accesses by the user or the device 105c, accesses of others nearby or in the same department, messages received or sent by the user, etc.). The card data 154 may include information such as the values to be presented in information cards, layout and formatting for information cards, an indication of rules or triggers for presenting the information cards, and so on).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Abhyankar into the method of Temkin to have displaying, by the one or more computers, the information card content including values of one or more attributes or measures for the entity. Here, combining Abhyankar with Temkin, which are both related to data processing, improves Temkin by providing a system that presents information card including values for different categories of entities or individual entities, (see Abhyankar paragraph [0004).
Taheri expressly discloses in response to receiving user input of the user prompt in the text entry field of the chatbot interface in the information card, obtaining, by the one or more computers, a chatbot response to the user prompt, wherein the chatbot response is generated using the one or more AI/ML models processing the user prompt and at least a portion of the content of the information card (Taheri paragraph [0053], The LLM can then use the description/attributes to generate a custom image that enables the user to visualize the goal. The prompts may be generated via prompt engineering that can be performed through the model training process, such as the model training process described above in the examples of FIGS. 3A-3C); and
displaying, by the one or more computers the chatbot response to the user prompt n the chatbot interface, in the information card (Taheri paragraph [0093], the displaying may include outputting the chat message via an in-app chatbot within a mobile application installed on the user's device. In some embodiments, the method may further include detecting a partner location within a predetermined distance of the geographic location of the user device and displaying a recommendation associated with the partner location within the chat window on the user device)
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Taheri into the method of Temkin to have providing…chatbot response to user. Here, combining Taheri with Temkin, which are both related to query processing, improves Temkin by providing a system that informs users the benefits that are available to them through the different payment cards in their wallet and which merchants provide benefits that are specific to one card over another card, (see Taheri paragraph [0001]).
Regarding claims 2, 14 and 18 Temkin discloses wherein the information card is based on a card template corresponding to an entity type, and the card template is associated with a particular chatbot from among multiple chatbots (see Temkin paragraph [0009], the information card is an information card template for generating an information card for any of multiple entities of a particular entity type, and the information card template specifies a set of data object types that are relevant to the particular entity type); and
wherein the access to the particular chatbot (see Temkin paragraph [0055], a server 130 provides functionality to generate an interactive agent module, e.g., chatbot 135 or other application, service or module, from a data set. The system then uses the chatbot 135 with the rest of the system to respond to a user's query; see Temkin paragraph [0077], an information card for a specific entity or an information card template for a particular class of entity can define a specific set of attributes, metrics, topics, or other information that are relevant when the user 202 submits a request about the specific entity or an entity of the particular class. These sets of data, and the information cards and templates, can be defined by each organization to focus information retrieval and delivery to the areas the organization defines as most important).
Regarding claims 5 Temkin discloses wherein the one or more AI/ML models comprise a large language model (LLM); wherein the LLM has a context window; and wherein the generation of the chatbot response includes providing the content of the information card to the LLM so the content of the information card is included in a context window of the LLM when the LLM is used to generate the chatbot response (see Taheri paragraph [0070], The LLM takes the product identifier and analyzes it to come up with a relevant prompt that might be useful or insightful for the user. Following the generation of the prompt, the processor instructs the chatbot within the chat window 511 on the user's device to display the generated prompt to the user; see Taheri paragraph [0024], the GPT model may be a generative artificial intelligence (GenAI) model, such as a multimodal large language model (LLM). The GPT model will be referred to herein as an LLM for further reference. The LLM can understand connections between products and payment card benefits written within payment card documents).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Taheri into the method of Temkin to have providing…chatbot response to user one or more AI/ML models comprise a large language model (LLM). Here, combining Taheri with Temkin, which are both related to query processing, improves Temkin by providing a system that informs users the benefits that are available to them through the different payment cards in their wallet and which merchants provide benefits that are specific to one card over another card, (see Taheri paragraph [0001]).
Regarding claim 9 Temkin discloses wherein the information card comprises an interactive element configured to respond to user interaction to cause the chatbot
interface to be provided in the information card and (see Temkin paragraph [0016], generating the voice response application includes storing, in association with the voice response application, configuration data identifying (i) data objects from the information card that the voice response application is configured to use in responding to voice requests, (ii) keywords corresponding to the respective data objects, and (iii) data repository information for obtaining values corresponding to the data objects).
Tehari expressly discloses wherein the chatbot interface is displayed in the information card in response to user interaction with the interactive (Taheri paragraph [0093], the displaying may include outputting the chat message via an in-app chatbot within a mobile application installed on the user's device. In some embodiments, the method may further include detecting a partner location within a predetermined distance of the geographic location of the user device and displaying a recommendation associated with the partner location within the chat window on the user device)
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Taheri into the method of Temkin to have providing…chatbot response to user. Here, combining Taheri with Temkin, which are both related to query processing, improves Temkin by providing a system that informs users the benefits that are available to them through the different payment cards in their wallet and which merchants provide benefits that are specific to one card over another card, (see Taheri paragraph [0001]).
Regarding claim 10 Temkin discloses comprising in response to user interaction with a portion of the chatbot interface, causing display, in the information card one or more suggested prompts that are based on the content of the information card (see Temkin paragraph [0055], an automated response system 100 for analytics. The example shows how a server 130 provides functionality to generate an interactive agent module, e.g., chatbot 135 or other application, service or module, from a data set. The system then uses the chatbot 135 with the rest of the system to respond to a user's query).
Regarding claim 11 Temkin discloses, comprising generating the suggested prompts based on data that identifies the entity, such that one or more of the suggested prompts includes a reference to the entity (see Temkin paragraph [0247], the system may use machine learning to automatically generate cards or to suggest content for cards. In some cases, the system can predictively suggest cards to be generated and content for the cards (e.g., subsets of attributes and metrics that are most commonly used). For example, the system can access usage data indicating, for example, rates of co-occurrence of different terms in documents of an organization, query histories from users of the organization, counts of interactions with different elements of documents, time spent viewing or interacting with different documents).
Regarding claims 16 and 20 Taheri expressly discloses wherein the one or more AI/ML models comprise a large language model (LLM) (see Taheri paragraph [0024], the GPT model may be a generative artificial intelligence (GenAI) model, such as a multimodal large language model (LLM). The GPT model will be referred to herein as an LLM for further reference. The LLM can understand connections between products and payment card benefits written within payment card documents).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Taheri into the method of Temkin to have providing…chatbot response to user one or more AI/ML models comprise a large language model (LLM). Here, combining Taheri with Temkin, which are both related to query processing, improves Temkin by providing a system that informs users the benefits that are available to them through the different payment cards in their wallet and which merchants provide benefits that are specific to one card over another card, (see Taheri paragraph [0001]).
Regrading claim 21 Temkin disclose, in response to user interaction with a portion of the chatbot interface, expanding the chatbot interface in the information card to replace or cover at least some of the information card content (see Temkin paragraph [0257], The content of the overlay panel for an indicator can vary based on the data type of an attribute value as well as metadata from the data set from which the attribute is derived. In addition to the adjustments shown on the panel 2804, the contents of the different regions 2802 can altered, for example, the indicator specified can be deleted, replaced with a different indicator, can be expanded or restricted to cover a different time range, and so on).
Regarding claim 24, Temkin discloses, wherein the chatbot is configured to access a limited subset of data from a database to answer prompts entered through the chatbot interface of the information card, wherein the limited subset comprises a particular set of data objects or particular portions of the database; and
wherein obtaining the chatbot response to the user prompt comprises causing the chatbot response to be generated by the one or more AI/ML models processing data from the database beyond the content of the information card but within the limited subset of data from the database that is specified for the chatbot to access to answer prompts received through the chatbot interface of the information card (see Temkin paragraphs [0021]-[0022], the information card corresponds to an entity type, the data set has a plurality of data objects for the entity type, and the information card is configured to present only a proper subset of the data objects for the entity type. The voice response application is configured to provide responses using only the proper subset of the data objects that the information card is configured to present. [0022] In some implementations, the information card corresponds to an entity type, the data set has a plurality of data objects for the entity type, and the information card is configured to present only a proper subset of the data objects for the entity type. The voice response application is configured to provide responses using any of the plurality of data objects for the entity type; see Temkin paragraphs [0055], an automated response system 100 for analytics. The example shows how a server 130 provides functionality to generate an interactive agent module, e.g., chatbot 135 or other application, service or module, from a data set. The system then uses the chatbot 135 with the rest of the system to respond to a user's query).
Regarding claim 25, Temkin discloses wherein the limited subset comprises a predetermined set of attributes and/or measures specified for an entity type of the entity corresponding to the information card (see Temkin paragraph [0025], the data objects comprise at least one of: one or more attributes included in the data set; or one or more metrics representing results of calculations using data in the data set).
Regarding claim 26, Temkin discloses in response to a user interaction with the chatbot interface in the information card, removing or overlaying additional content of the chatbot interface in a region of the information card where the values of the one or more attributes or measures were displayed (see Tempkin paragraph [0204], the information card 2204 may be provided through the operating system (OS) functionality of the client device 2210 outside of the application 2212, e.g., as an OS notification. In general, the information card 2204 may be displayed in, alongside, or on (e.g., as an overlay to) the application or interface that has the term(s) that triggered the presentation of the information card 2204, such as the current interface of the client device 2210, whether the current application or interface is one for messaging, search, calendar management, and so on, or even a lock screen or home screen showing general system notifications).
Claim 22 is rejected under AIA 35 U.S.C. 103 as being unpatentable over Temkin et al. (US 20200251111 A1) in view of in view of Taheri et al. (US 20250117836 A1) further in view of Tiwari et al. (US 20220237567 A1).
Regarding claims 22, Tiwari expressly discloses wherein the information card is displayed based on an occurrence of a keyword corresponding to the entity in a web page or document; wherein the method comprises: obtaining content from the web page or document comprising the keyword; and including the content from the web page or document within a context window of the one or more AI/ML models, such that the chatbot response to the user prompt is generated based on the one or more AI/ML models processing the content of the information card, the content of the web page or document, and the user prompt (see Tiwari paragraph [0039], in certain embodiments, the chatbot system 222 is configured to receive images (e.g., digital photographs, scanned images) of documents (e.g., identification, diplomas, utility bills, tax documents, pay check stubs) of the user via the chat interface 226, and to extract applicant information to apply for the opportunity from these documents. Additionally or alternatively, in certain embodiments, the chatbot system 222 is configured to extract applicant information to apply for the opportunity from one or more social media accounts of the user).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Tiwari into the method of Temkin to have one or more AI/ML models processing the content of the information card, the content of the web page or document, and the user prompt. Here, combining Tiwari with Temkin, which are both related to query processing, improves Temkin by providing improved chatbot system that enhances the user experience by enabling the application process to proceed via natural language messages and prompts, reducing the amount of applicant information that is provided directly by the user, reducing the number of mistakes in the application process, and maximizing the benefit to the user by enabling the application to multiple or different opportunities with minimal additional effort, (see Tiwari paragraph [0001]).
Claim 23 is rejected under AIA 35 U.S.C. 103 as being unpatentable over Temkin et al. (US 20200251111 A1) in view of in view of Taheri et al. (US 20250117836 A1) further in view of Ma et al. (CN 118819702 A).
Regarding claim 23 Ma expressly discloses displaying, in the information card, one or more controls configured to switch between (i) filling the majority of the information card with predetermined information card content and (ii) filling the majority of the information card with the chatbot interface (see Ma page 2, the system is more flexibly adapted to different application scenes by allowing card messages to be linked with different templates, and different service requirements can use different card templates so as to display different styles of card contents in different scenes. on the one hand, the mechanism of template linking enables the system to easily update and switch the card template without modifying the issued card message, which promotes the innovation of the system, and can introduce new card template along with the passage of time, providing more abundant display effect, The invention further improves the diversity of the card content displayed by the terminal).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Tiwari into the method of Temkin to have one or more AI/ML models processing the content of the information card, the content of the web page or document, and the user prompt. Here, combining Tiwari with Temkin, which are both related to query processing, improves Temkin by providing improved system that improves the diversity of the card content displayed by the terminal, (see Ma paragraph page 2).
Remarks
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Shankar et al. (US 12531819 B2) discloses Apparatus and methods for an artificial intelligence/machine learning (“AI/ML”) chatbot personalized to a user are provided. The apparatus and methods may include a personalized AI/ML chatbot on a server receiving one or more financial records about a user. The personalized chatbot may analyze the one or more records. The personalized chatbot may determine one or more financial services to present to the user based on the financial records.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DINKU W GEBRESENBET whose telephone number is (571)270-1636. The examiner can normally be reached between 8:00AM-5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached on 571- 270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DINKU W GEBRESENBET/Primary Examiner, Art Unit 2164
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