DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are present. As a result, claims 1-20 are pending.
Drawings
The drawings received on 28 April 2025 are accepted by the Examiner
This Office Action is Non-Final.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e.,
(1) process,
(2) machine,
(3) manufacture or product, or
(4) composition of matter.
If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception, i.e.,
(1) law of nature,
(2) natural phenomenon, and
(3) abstract idea.
and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include:
(i) a method of organizing human activities, (ii) an idea of itself, or (iii) a mathematical relationship or formula.
Under the current 2019 PEG USPTO guidance, a two-step analysis is utilized to determine subject matter eligibility under 101.
Step 1:
In the instant case, with respect to claims 1-20:
Claim category:
(1) Process/method: 1-13, and
(2) Machine/system: 14-16
(3) Manufacture/product/article (CRM):17-20
(4) composition of matter: none.
Analysis:
1) Process: Claims 1-13 are directed to a method for providing, by the one more computers, data for an information card to an entity, comprising series of steps. The claimed method is therefore directed to a statutory category, i.e. a process (a series of steps).
(2) Machine/system: claims 13-16 are directed to a system comprising one or more computers providing, by the one more computers, data for an information card to an entity.
(3) claims 17-20 are directed to an article, one or more non-transitory computer readable storage media, for providing, by the one more computers, data for an information card to an entity.
The claimed system is therefore directed to a statutory category, i.e. an article.
Step 2A, Prong 1 (Claims 1, 13 and 17)
Regarding claims 1, 13 and 17 the following limitation is directed to an abstract idea of: "…user interaction with one or more elements provided by the information card" are all abstract ideas, which can be mentally performed. That is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper.
This limitation under its broadest reasonable interpretation is recited as at a high level of generality, for example as a general way the human mind can interact with one or more elements provided by the information card ". The human mind can identify elements provided by the information card, for example if something is reviewed and observed the human mind is capable of identifying one or more elements provided by the information card.
Step 2A Prong 2 (Claims 1, 13 and 17)
Furthermore, Claim 1 recites the additional elements of one or more computers;.
These are high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), and MPEP 2106.04(a)(2)(III)(C)(3) that recites using a computer as a tool to perform a mental process, which does not provide integration into a practical application.
The step of "accessing a chatbot configured to provide responses generated using one or more artificial intelligence and/or machine learning (AI/ML) models (Note that, absent any further technical detail, the limitation “one or more artificial intelligence and/or machine learning (AI/ML) models” does not required any particular sequence or complexity of operation and therefore the broadest reasonable interpretation of this limitation places it within the capabilities of the human mind); receiving, by one or more computers, a user prompt from the user entered through… providing, by one or more computers, a chatbot response to the user prompt for presentation” are additional elements and insignificantly more because it is merely storing and retrieving information in memory MPEP 2106.05(d) and does not provide integration into a practical application. This limitation under its broadest reasonable interpretation is recited as at a high level of generality, for example as a general way the human mind can determine at least one geographic location identifier for the at least one data transmission".
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Step 2B (Claims 1, 13 and 17 )
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements, “processor”, “non-transitory computer readable storage media” are recited at a high-level of generality and amount to no more than mere instructions to apply the abstract idea to computer environment (MPEP 2106.05(f)) and MPEP 2106.04(a)(2)(III)(C)(3).
Therefore, looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claims 2, 14 and 18 limitations “… 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; wherein the one or more elements provide access to the particular chatbot” are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' and displaying data (i.e. outputting data) as identified in MPEP 2106.05(g) and do not provide integration into a practical application.
Regarding claims 3, 15 and 19 the limitation “wherein each of the multiple card templates specifies a chatbot, from among the multiple chatbots, to be used to respond to user prompts; wherein the one or more computers are configured to select, in response to a user prompt associated with a particular information card, to use the chatbot that is specified in the card template used to generate the particular information card for generating a chatbot response” are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' and displaying data (i.e. outputting data) as identified in MPEP 2106.05(g) and do not provide integration into a practical application.
Regarding claims 4, 16 and 20 the limitation " wherein the one or more AI/ML models comprise a large language model (LLM) (Note that, absent any further technical detail, the limitation “one or more artificial intelligence and/or machine learning (AI/ML) models” does not required any particular sequence or complexity of operation and therefore the broadest reasonable interpretation of this limitation places it within the capabilities of the human mind).
Regarding claim 5, the limitation “wherein the LLM has a context window; and wherein the method 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” are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' and displaying data (i.e. outputting data) as identified in MPEP 2106.05(g) and do not provide integration into a practical application (Note that, absent any further technical detail, the limitation the LLM has a context window does not required any particular sequence or complexity of operation and therefore the broadest reasonable interpretation of this limitation places it within the capabilities of the human mind).
Regarding claim 6, the limitation “wherein the information card is displayed at a user device in response to interaction of the user with a user interface” are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' and displaying data (i.e. outputting data) as identified in MPEP 2106.05(g) and do not provide integration into a practical application.
Regarding claim 7, the limitation “providing the content of the user interface to the one or more AI/ML models so the content of the information card is included in a context window of the one or more AI/ML models when the one or more AI/ML models” are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' and displaying data (i.e. outputting data) as identified in MPEP 2106.05(g) and do not provide integration into a practical application (Note that, absent any further technical detail, the limitation the LLM has a context window does not required any particular sequence or complexity of operation and therefore the broadest reasonable interpretation of this limitation places it within the capabilities of the human mind).
Regarding claim 8, the limitation wherein the one or more elements comprise a text entry field configured to receive a user prompt to the chatbot “are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' and displaying data (i.e. outputting data) as identified in MPEP 2106.05(g) and do not provide integration into a practical application.
Regarding claim 9, the limitation “wherein the one or more elements comprise an interactive element configured to respond to user interaction to cause a chatbot interface to be provided in the information card or adjacent to the information card” are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' and displaying data (i.e. outputting data) as identified in MPEP 2106.05(g) and do not provide integration into a practical application.
Regarding claim 10, the limitations wherein the one or more elements are configured to cause, upon user interaction, presentation of a chatbot interface; and
wherein the one or more computers are configured to provide, for display in the
chatbot interface, one or more suggested prompts that are based on the content of the
information card” are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' and displaying data (i.e. outputting data) as identified in MPEP 2106.05(g) and do not provide integration into a practical application.
Regarding claim 11, the limitations “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” are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' and displaying data (i.e. outputting data) as identified in MPEP 2106.05(g) and do not provide integration into a practical application.
Regarding claim 12, the limitations wherein the one or more of the suggested prompts are displayed before the user enters a prompt in the chatbot interface, and wherein at least one of the suggested prompts is generated to include a reference to the entity based on the information card corresponding to the entity” are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' and displaying data (i.e. outputting data) as identified in MPEP 2106.05(g) and do not provide integration into a practical application.
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 1-20 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).
Regarding claims 1, 13 and 17 Temkin disclose a method performed by one or more computers, the method comprising: providing, 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),
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.)) and (ii) one or more elements for accessing a chatbot configured to provide responses generated using one or more artificial intelligence and/or machine learning (AI/ML) models (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 from the user entered
through a chatbot interface that is provided on the information card or is presented in
response to the user interaction with the one or more elements (see Temkin paragraph [0008], receiving, by the one or more computers, user input data indicating user input to enable voice response interaction for the information card or the data set; generating, by the one or more computers, a voice response application based on the information card); and
providing, by the one or more computers, a … response to the user prompt
for presentation in the chatbot interface, wherein the …response is generated
based on 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).
Taheri expressly discloses providing…chatbot response to user (see Taheri paragraph [0031], one or more of text content from a chat window, contextual content from the user device 110, payment card data from the data store 128, and the like may be input to the LLM 124 when generating responses to be output by the chatbot 114 displayed on the user device 110).
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 one or more elements provide access to the particular chatbot (see Temkin paragraph [0055], 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 3, 15 and 19 Temkin discloses,…wherein each card template corresponds to an entity type, wherein the one or more computers are configured to use each card template to generate information cards for each of multiple entities of the corresponding entity type (see Temkin paragraph [0055],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), and
wherein each of the multiple card templates specifies a chatbot, from
among the multiple chatbots, to be used to respond to user prompts;
wherein the one or more computers are configured to select, in response to a
user prompt associated with a particular information card, to use the chatbot that is
specified in the card template used to generate the particular information card for
generating a … response.
Taheri expressly discloses wherein the one or more computers are configured to provide chatbot responses from each of multiple chatbots, wherein at least some of the multiple chatbots are configured to provide responses based on different data sets; wherein the method includes storing multiple card templates (see Taheri paragraph [0031], one or more of text content from a chat window, contextual content from the user device 110, payment card data from the data store 128, and the like may be input to the LLM 124 when generating responses to be output by the chatbot 114 displayed on the user device 110; see Taheri paragraph [0031], the system described herein may use a vector database that stores vectorized responses that the chatbot can use to respond to queries from a user. For example, the vector database may store a plurality of responses that can be output by the chatbot in vector form).
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 4, 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]).
Regarding claims 5 Temkin discloses wherein the LLM has a context window; and wherein the method 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 claims 6 Temkin discloses, wherein the information card is displayed at a user device in response to interaction of the user with a user interface, and wherein the … response is generated based on content of the information card (see Temkin paragraph [0057],the process of generating the chatbot involves the server 130 or another server accessing information card data 145 indicating templates or definitions for information cards that specify data elements that have been defined as most important).
Taheri expressly discloses providing…chatbot response to user (see Taheri paragraph [0031], one or more of text content from a chat window, contextual content from the user device 110, payment card data from the data store 128, and the like may be input to the LLM 124 when generating responses to be output by the chatbot 114 displayed on the user device 110).
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 7 Temkin discloses, providing the content of the user interface to the one or more AI/ML models so the content of the information card is included in a context window of the one or more AI/ML models when the one or more AI/ML models are used to generate the … response (see Temkin paragraph [0007], an application, an artificial conversational entity, or chatbot, which can be integrated as a skill or module of a natural language interface to provide access to information in an enterprise database. The system can begin with an initial baseline agent module. The system is then customized to recognize and respond to phrases and keywords that users are expected to say. The set of phrases and keywords can be customized to take into account user interaction histories for users in a particular organization, e.g., indicating queries submitted, data and documents accessed, etc. The set of phrases and keywords can also be customized using the information in customized information cards).
Taheri expressly discloses providing…chatbot response to user (see Taheri paragraph [0031], one or more of text content from a chat window, contextual content from the user device 110, payment card data from the data store 128, and the like may be input to the LLM 124 when generating responses to be output by the chatbot 114 displayed on the user device 110).
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 8 Temkin discloses, wherein the one or more elements comprise a text entry field configured to receive a user prompt to the chatbot (see Temkin paragraph [0007], providing user interface data for a user interface having one or more controls to receive user input indicating (i) text of one or more requests for the voice response application to answer and (ii) text of one or more responses for the voice response application to provide. The voice response application is generated to respond to the one or more requests using the one or more responses, with values corresponding to the data objects used to complete the one or more responses).
Regarding claim 9 Temkin discloses wherein the one or more elements comprise an interactive element configured to respond to user interaction to cause a chatbot
interface to be provided in the information card or adjacent to the information card (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).
Regarding claim 10 Temkin discloses, wherein the one or more elements are configured to cause, upon user interaction, presentation of a chatbot interface; and
wherein the one or more computers are configured to provide, for display in the
chatbot interface, 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 claim 12 Temkin discloses, wherein the one or more of the suggested prompts are displayed before the user enters a prompt in the chatbot interface, and wherein at least one of the suggested prompts is generated to include a reference to the entity based on the information card corresponding to the entity (see Taheri paragraph [0006], the chatbot may suggest complementary products, and the user might discover new products they were unaware of, thus enhancing their AR shopping experience and increasing their satisfaction. In another example, when a nearby shop or product in proximity to the user's device has a discount that aligns with the user's charge card benefits, the chatbot alerts the user via the user's AR device, visually highlighting with virtual badges or markers to indicate that there are special deals being offered. If the user inquires, the chatbot describes the offers).
Remarks
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Taheri et al. (US 20250117630 A1) discloses one or more of train a large language model (LLM) to learn credit card data via execution of the LLM on content from one or more credit card documents, execute the LLM to generate a sequence of prompts which are output via a chatbot within a chat window of a software application, receive responses to the sequence of prompts from a user via the chat window of the software application.
Conclusion
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