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 .
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over D’Agostino (US 20250307290 A1 hereinafter, D’Agostino ‘290) in view of Wyss et al. (US 20220400091 hereinafter, Wyss ‘091).
Regarding claim 15; D’Agostino ‘290 discloses a computer system (Fig. 10,Computer System 1001) comprising:
one or more computer processors (Fig. 10, Processing Unit 1002);
and a non-transitory computer readable storage medium storing instructions that when executed by one or more computer processors cause the one or more computer processors to perform steps (i.e. Provides a computer-readable medium comprising instructions stored therein, which when executed by a processor cause the processor to perform one or more operations. Paragraphs 0004-0016)
comprising:
storing, in a vector database, data obtained from a plurality of data sources, each data source storing information associated with users of an organization (i.e. The processor configured to retrieve vectors from a vector database, where the vectors include previous communication content between a source device and a service provider device, identify an item of interest that has not been discussed in the previous communication content based on execution of a large language model (LLM) on the vectors. Paragraph 0014)
and repeatedly performing: receiving a natural language request from a user via a user interface (Fig. 9B, 902B i.e. Referring to Fig. 9B, in 902B, the method may include receiving interaction content from a communication session between a source device and a service provider device of a service provider. The software application 432 may include a user interface/page that is displayed on the service provider device 420 which includes content, account details, transaction history, and the like, about a user of the source device 410. For example, when the source device 410 initially places a call with the contact center, the API 431 may receive information about the call from the host platform 430, the service provider device 420, the source device 410, or the like. Paragraphs 0086 & 0199)
generating a prompt for input to a machine learning based language model comprising the natural language request and metadata describing each of the plurality of data sources (i.e. For example, the LLM 820 may identify an item of interest and notify the software application 830 to generate an item offer for a new product, such as a new credit card, a new checking account, a new loan offer, or the like, and output content on a user interface 842 of the source device 840. Here the software application 830 may generate a clickable link which when clicked automatically registers the source device 840 with a service corresponding to the item of interest and output the clickable link on the user interface 842 of the source device 840 during the active communication session. Paragraph 0180)
and requesting a machine learning based language model to generate one or more queries for extracting data relevant to the natural language request from the plurality of data sources (i.e. Users initiate conversations by sending queries or messages through the chat interface provided by the platform. The chatbot generates relevant and accurate responses to the user's queries based on the identified contextual attributes. Paragraph 0104)
providing the prompt to the machine learning based language model for execution (i.e. Users initiate conversations by sending queries or messages through the chat interface provided by the platform. Users may inquire about financial topics such as banking services, investment options, loan products, insurance policies, or general financial advice. Paragraph 0104)
receiving a response generated by the machine learning based language model based on the prompt, the response comprising one or more queries for extracting data relevant to the natural language request from the plurality of data sources (i.e. The processor simultaneously outputs the first response to the source device and the second to the service provider device, ensuring both parties receive prompt and relevant responses during the ongoing communication session. Paragraph 0102);
executing the one or more queries against the vector database, to extract information relevant to the natural language request from the plurality of data sources (i.e. The chatbot utilizes LLM technology to extract contextual attributes from the message content. This includes analyzing the linguistic patterns, sentiment, key terms, and intent behind the user's queries to understand their financial needs, preferences, and the conversation context. The chatbot generates relevant and accurate responses to the user's queries based on the identified contextual attributes. Paragraph 0104)
using the information extracted from the vector database for generating a reply to the natural language request using the machine learning based language model (Fig. 9A, Step 908A i.e. In 908A, the method may include determining a response based on execution of at least one large language models (LLMs) on the second interaction content, the at least one contextual attribute associated with the source device, and the first interaction content with the service provider. Paragraph 0198)
and sending the reply to the user via the user interface (Fig. 9A, Step 910A i.e. In 910A, the method may include outputting the response to at least one of the source device and the service provider device during the communication session. Paragraph 0198).
Examiner reasonably believes that D’Agostino ‘290 discloses the non-transitory computer readable storage medium as expressed above. However, Examiner cites Wyss ‘091 to cure any deficiencies of D’Agostino ‘290.
Wyss ‘091 discloses non-transitory computer readable storage medium storing instructions that when executed by one or more computer processors cause the one or more computer processors to perform steps (i.e. The disclosed embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. Paragraph 0034)
D’Agostino ‘290 and Wyss ‘091 are combinable because they are from same field of endeavor of speech systems (Wyss ‘091 at “Field of Invention”).
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the speech system as taught by D’Agostino ‘290 by adding a non-transitory computer readable storage medium as taught by Wyss ‘091. The motivation for doing so would have been advantageous to support many of the more complicated or sophisticated user interactions between chat bots to human agents. Therefore, it would have been obvious to combine D’Agostino ‘290 with Wyss ‘091 to obtain the invention as specified.
Regarding claim 16; D’Agostino ‘290 discloses wherein the prompt is a first prompt and the response is a first response, wherein generating the reply to the natural language request comprises: generating a second prompt for input to a machine learning based language model comprising the natural language request and information extracted from the plurality of data sources, and requesting the machine learning based language model to generate a reply to the natural language request; receiving a second response obtained by executing the machine learning based language model using the second prompt; and generating a reply to the natural language request based on the second response generated by the machine learning based language model based on the second prompt (i.e. The processor configured to store first interaction content with a service provider, receive second interaction content from a communication session between a source device and a service provider device of the service provider, identify at least one contextual attribute associated with the source device, determine a response based on execution of at least one large language models (LLMs) on the second interaction content, the at least one contextual attribute associated with the source device, and the first interaction content with the service provider, and output the response to at least one of the source device and the service provider device during the communication session. Paragraph 0002)
Regarding claim 17; D’Agostino ‘290 discloses wherein the instructions further cause the one or more computer processors to perform steps comprising: configuring, by an online system, a user interface for performing conversations associated with an organization, wherein each conversation is performed by the online system with a user of the organization (i.e. When a policyholder submits a claim through various communication channels such as phone calls, emails, or online forms, the system employs LLMs to extract contextual attributes from the conversation, including details about the claim, policy coverage, urgency, and any relevant supporting documentation provided by the policyholder. Paragraph 0128)
Regarding claim 18; D’Agostino ‘290 discloses wherein the prompt specifies one or more precedence rules for handling conflicts if multiple data sources have an answer available for certain question (i.e. In 908F, the method may include identifying an item of interest discussed during the communication session and a sentiment toward the item of interest based on execution of the at least one LLMs on the second interaction content. In 910F, the method may include receiving device data from the source device, and the determining the response comprises determining the response based on execution of the device data, wherein the device data comprises at least one of a geographical location of the source device, an Internet Protocol (IP) address of the source device, and a type of network connection of the source device. Paragraph 0203)
Regarding claim 19; D’Agostino ‘290 discloses wherein the prompt specifies, for a data source, a question template for queries for the data source (i.e. For example, the LLM framework 440 may generate an instruction, a question, a query, a product offer, or the like, which can be displayed on the user interface 422 of the service provider device 420. As another example, the LLM framework 440 may generate a product offer, verification question, or the like, which can be displayed on the user interface 412 of the source device 410. Paragraph 0091);
and an answer template describing how to answer a question based on the data source (i.e. During users' active sessions within the system, they can engage with the chatbot to obtain instant support, receive answers to their questions, or complete insurance-related tasks seamlessly. For example, a user may chat with the bot to update their contact information, request a quote for a new policy, or inquire about discounts or special offers. Paragraph 0196)
Regarding claim 20; D’Agostino ‘290 discloses wherein a data source comprises one or more of: a user profile of the user, event information associated with the user, documents storing policies of the organization, and conversation data of conversation channels used by the organization (i.e. The system utilizes the users' financial profiles to offer proactive financial suggestions and reminders tailored to individual needs. Paragraph 0105)
Regarding claim 1; Claim 1 contains substantially the same subject matter as claim 15. Therefore, claim 1 is rejected on the same grounds as claim 15.
Regarding claim 2; Claim 2 contains substantially the same subject matter as claim 16. Therefore, claim 2 is rejected on the same grounds as claim 16.
Regarding claim 3; Claim 3 contains substantially the same subject matter as claim 17. Therefore, claim 3 is rejected on the same grounds as claim 17.
Regarding claim 4; Claim 4 contains substantially the same subject matter as claim 18. Therefore, claim 4 is rejected on the same grounds as claim 18.
Regarding claim 5; Claim 5 contains substantially the same subject matter as claim 19. Therefore, claim 5 is rejected on the same grounds as claim 19.
Regarding claim 6; Claim 6 contains substantially the same subject matter as claim 20. Therefore, claim 6 is rejected on the same grounds as claim 20.
Regarding claim 7; The computer-implemented method of claim 1, wherein a data source stores conversation data of conversation channels used by the organization.
Regarding claim 8; Claim 8 contains substantially the same subject matter as claim 15. Therefore, claim 8 is rejected on the same grounds as claim 15.
Regarding claim 9; Claim 9 contains substantially the same subject matter as claim 165. Therefore, claim 9 is rejected on the same grounds as claim 16.
Regarding claim 10; Claim 10 contains substantially the same subject matter as claim 17. Therefore, claim 10 is rejected on the same grounds as claim 17.
Regarding claim 11; Claim 11 contains substantially the same subject matter as claim 18. Therefore, claim 11 is rejected on the same grounds as claim 18.
Regarding claim 12; Claim 12 contains substantially the same subject matter as claim 19. Therefore, claim 12 is rejected on the same grounds as claim 19.
Regarding claim 13; Claim 13 contains substantially the same subject matter as claim 20. Therefore, claim 13 is rejected on the same grounds as claim 20.
Regarding claim 14; D’Agostino ‘290 discloses wherein a data source stores conversation data of conversation channels used by the organization (i.e. Interaction content may be a conversation content, previous conversation content, historical conversation content, communication session and any other data related to a session or interaction between one party and another party. The vector of the conversation may be labelled with identifiers (e.g., metadata tags, account data, etc.) which identify the contextual attributes of the communication session embedded in the vector. Furthermore, the system may store the vector with the labels in a database, such as a vector database. Paragraphs 0040-0041)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARCUS T. RILEY, ESQ. whose telephone number is (571)270-1581. The examiner can normally be reached 9-5 M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hai Phan can be reached at 571-272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
MARCUS T. RILEY, ESQ.
Primary Examiner
Art Unit 2654
/MARCUS T RILEY/Primary Examiner, Art Unit 2654
Prosecution Insights