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 .
Notice to Applicant
This is the first Non-Final Office Action in response to Application Serial Number: 19/242,234, filed on June 18, 2025. Claims 1-20 are pending in this application and have been rejected below.
Priority
The Examiner has noted the Applicant is claiming priority from Provisional Application No. 63/661,556 filed June 18, 2024.
Information Disclosure Statement
The information disclosure statement (IDS) filed on June 18, 2025 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
Claim Objections
Claim 19 is objected to because of the following informalities: grammatical error. Claim limitation “a document uploader configured to parse, vectorize, and store enterprise data associated with an enterprise into a vector database.” should end with a semi-colon instead of a period. Appropriate correction is required.
Claim 19 is objected to because of the following informalities: grammatical error. Claim limitation “determine an identity of the user, a role of the user within the enterprise and access rights associated with the user” is missing punctuation. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Here, even though “means for” has not been explicitly recited, claim limitations “a chat interface configured to receive a query”, “chat service configured to: determine one or more user attributes and contextual information; retrieve relevant content from the vector database; and generate a prompt”, “the chat service is further configured to provide the response”, “a topic analysis engine configured to: receive a plurality of queries; analyze the received queries; generate topic clusters; submit the topic clusters to the generative AI system; and provide summarized topic analysis results”, “a feedback interface configured to: receive an initial feedback selection; determine whether additional feedback justification is required; and store the feedback selection”, “chat service is further configured to: store, in a historical database: the query, the response, session metadata”; “a document uploader configured to parse, vectorize, and store enterprise data” and “the chat service is further configured to: store, in a historical database; and provide the response to the chat interface for display” have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses/they use a generic placeholder “interface configured to” coupled with functional language “chat” and “feedback”, “service configured to” coupled with functional language “chat” and “engine configured to” coupled with functional language “topic analysis” respectively without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claims 1, 7, 8, 10, 19 and 20 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: Fig. 8, Specification [0015]; [00125]-[00127].
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-10, 19 and 20 are directed towards a system and claims 11-18 are directed towards a method, both of which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
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 claims recite formulating a user specific response to a query based on a generated prompt, user attributes and contextual information.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, receive a query from a user; determine one or more user attributes and contextual information associated with the query; retrieve relevant content based on semantic similarity between the query and the vectorized enterprise data; generate a prompt combining the query and the relevant content; and formulate a response to the query based on the prompt, the user attributes, and the contextual information, constitutes methods based on managing personal behavior, as well as, methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of a chat interface, user application, chat service, vector database and a generative artificial intelligence system does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea. Claim 11 recites certain method of organizing human activity and mental processes for similar reasons as claim 1.
Claim 19 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, receive a query from a user; parse and vectorize enterprise data associated with an enterprise, determine an identity of the user, a role of the user within the enterprise and access rights associated with the user, retrieve relevant content based on semantic similarity between the query and the vectorized enterprise data; generate a prompt combining the query and the relevant content; and formulate a response to the query based on the prompt, the user attributes, and the contextual information wherein the response is customized to align with one or more of: the role of the user within the enterprise and with the access rights associated with the user, constitutes methods based on managing personal behavior, as well as, methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of a chat interface, user application, chat service, vector database and a generative artificial intelligence system does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites a vector database configured to store vectorized enterprise data associated with an enterprise; retrieve relevant content from the vector database based on semantic similarity between the query and the vectorized enterprise data; and the chat service is further configured to provide the response to the chat interface for display to the user, which are limitations considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Additionally, claim 1 recites a chat interface, user application, chat service, vector database and a generative artificial intelligence system and a chat service communicatively coupled to the chat interface and the vector database at a high-level of generality such that they amounts to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claim 1 recites a generative artificial intelligence system configured to formulate a response to the query based on the prompt, the user attributes, and the contextual information. The general use of an artificial intelligence technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the generative artificial intelligence system disclosed in the claim is not technological in nature and merely confines the abstract idea to a particular technological environment or field of use; see MPEP 2106.05(h). Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The method recited in claim 11 also amount to no more than mere instructions to apply the exception using generic computer components; see MPEP 2106.05(f) and are not technological in nature and merely confines the abstract idea to a particular technological environment or field of use; see MPEP 2106.05(h). Thus, the additional elements recited in claim 11 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Claim 19 recites a document uploader configured to store enterprise data associated with an enterprise into a vector database; access rights associated with the user retrieve relevant content from the vector database based on semantic similarity between the query and the vectorized enterprise data; and the chat service is further configured to: store, in a historical database: the query, the prompt, the response, session metadata comprising one or more of: user identity, timestamp, and session identifier, the relevant content used to generate the response, and model metadata associated with the generative AI system; and provide the response to the chat interface for display to the user on the user application, which are limitations considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Additionally, claim 1 recites a chat interface, user application, chat service, vector database and a generative artificial intelligence system and a chat service communicatively coupled to the chat interface and the vector database at a high-level of generality such that they amounts to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claim 1 recites a generative artificial intelligence system configured to formulate a response to the query based on the prompt, the user attributes, and the contextual information wherein the response is customized by the generative artificial intelligence system to align with one or more of: the role of the user within the enterprise and with the access rights associated with the user. The general use of an artificial intelligence technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the generative artificial intelligence system disclosed in the claim is not technological in nature and merely confines the abstract idea to a particular technological environment or field of use; see MPEP 2106.05(h). Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 19 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including a chat interface, user application, chat service and vector database amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). The generative artificial intelligence system recited in the claim are disclosed at a high-level of generality (see at least Specification [00102]; [00132]) and does not amount to significantly more than the abstract idea. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 7, 8, 10, 16, 18 and 20 recite providing and storing limitations respectively, which are considered insignificant extra-solution activities of collecting and delivering data; see MPEP 2106.05(g). Claims 4, 6, 7, 10, 13, 17 and 18 recites generative artificial intelligence system limitations, which are not technological in nature and merely confines the abstract idea to a particular technological environment or field of use; see MPEP 2106.05(h). Claims 7, 8, 10 and 20 a topic analysis engine, topic analysis interface, feedback interface and chat service, respectively at a high-level of generality such that they amounts to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claims 2-5, 7-10, 12-15, 18 and 20 recite steps that further narrow the abstract idea. Therefore claims 2-10, 12-18 and 20 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6 and 10-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Siebel et al., U.S. Publication No. 2024/0202221 [hereinafter Siebel].
Referring to Claim 1, Siebel teaches:
A store employee assistance system comprising:
a chat interface configured to receive a query from a user via a user application (Siebel, [0047]), “interactive query portion 256 enables users to input additional related queries (e.g., “follow-up” questions) through an interactive input portion 257. In the example of the FIG. 2B, the interactive query portion 256 comprises a chat interface”; (Siebel, [0029]), “the query comprises a natural language query received through a graphical user interface”; (Siebel, [0031]);
a vector database configured to store vectorized enterprise data associated with an enterprise (Siebel, [0037]), “the enterprise generative artificial intelligence system 102 can generate embeddings based on the embedding models of the embedding models datastore 124 and the content of the data records. In some implementations, the embeddings may be represented by one or more vectors that can be stored in the vector datastore 126. In some implementations, the retrieval models 122 can use the embeddings to retrieve relevant data records and perform similarity or relevance evaluations or other aspects of retrieval operations”; (Siebel, [0084]-[0085]), “The crawling module 414 can function to the intelligent enterprise crawling and mapping system can crawl and index a corpus of data records (e.g., data records of one or more enterprise systems) using contextual information (e.g., contextual metadata) along with data record embeddings to provide access control (e.g., role-based access), improved data record identification and retrieval, and map relationships between data records… the embeddings may be represented by one or more vectors that can be stored in the vector datastore 440”; (Siebel, [0156]);
a chat service communicatively coupled to the chat interface and the vector database, the chat service configured to (Siebel, [0058]; [0148]; [0086]):
determine one or more user attributes and contextual information associated with the query (Siebel, [0035]), “enterprise generative artificial intelligence system can also perform similar functionality based on the context of users and/or systems submitting the query. For example, a director and engineer may submit the same query (e.g., “what projects are past due?”), and the enterprise generative artificial intelligence system 102 can use contextual information (e.g., user role, permissions, domain associated with the user, and the like) to provide a response that is based on context both substantively (e.g., provide information on overdue projects for the particular requester) and/or with respect presentation of the response”; (Siebel, [0036]), “the enterprise generative artificial intelligence system 102 can crawl, index, and/or map a corpus of data records (e.g., data records of one or more enterprise systems or environments) using contextual information (e.g., contextual metadata) along with data record embeddings to provide access control (e.g., role-based access)”; (Siebel, [0081]; [0084]);
retrieve relevant content from the vector database based on semantic similarity between the query and the vectorized enterprise data (Siebel, [0037]), “the enterprise generative artificial intelligence system 102 can generate embeddings based on the embedding models of the embedding models datastore 124 and the content of the data records. In some implementations, the embeddings may be represented by one or more vectors that can be stored in the vector datastore 126. In some implementations, the retrieval models 122 can use the embeddings to retrieve relevant data records and perform similarity or relevance evaluations or other aspects of retrieval operations”; (Siebel, [0034]), “the enterprise access control layer 115 can filter information that is restricted by the retrieval model 122… the enterprise access control layer 115 may filter data sources, data records, and/or other elements of an enterprise information environment such that query responses (or supporting traceability references) do not include information the user is not permitted to access”; (Siebel, [0081]; [ [0086]; [0087]);
generate a prompt combining the query and the relevant content (Siebel, [0079]), “the enterprise comprehension module 412 processes inputs to determine one or more results (i.e., output, response, or answer), determine rationales for results, and determine whether the enterprise comprehension module 412 needs more information to determine results. Enterprise comprehension module 412 may output information (e.g., results, new prompts, or additional queries) in a natural language format. In some implementations, features of one or more models of the enterprise comprehension module 412 can define conditions or functions that determine if more information is needed to satisfy the initial input or if there is enough information to satisfy the initial input”; (Siebel, [0129]), “an enterprise generative artificial intelligence system can receive an initial input 802 from a user or another system. For example, an orchestrator module 803 may receive the input 802. The enterprise generative artificial intelligence system can provide that input to a retrieval module 804 which can then reach out and “retrieve” information from various enterprise data sources (e.g., data stores, databases, artificial intelligence applications, and/or the like). The enterprise generative artificial intelligence system can use that information to generate an initial prompt for the enterprise comprehension module 806. The enterprise comprehension module 806 can process that initial prompt and determine whether it has enough information to satisfy criteria based on the initial input (e.g., answer a question). If it has enough information to satisfy the initial input, the enterprise comprehension module can then provide the result to a recipient, such as the user or system that provided the initial input”; (Siebel, [0132]);
a generative artificial intelligence system configured to formulate a response to the query based on the prompt, the user attributes, and the contextual information; and wherein, the chat service is further configured to provide the response to the chat interface for display to the user (Siebel, Fig. 2B, [0042]), “…FIG. 2B, the enterprise generative artificial intelligence response graphical user interface 250 includes an enterprise search query input portion 252, a generative enterprise search result portion 254, and an interactive query portion 256”; (Siebel, [0034]), “the enterprise generative artificial intelligence system 102 can use the enterprise access control layer 115 to implement additional enterprise controls… the enterprise access control layer 115 can filter information that is restricted by the retrieval model 122, prior to processing by the large language models 120, or presenting the answer or other output. More specifically, the enterprise access control layer 115 may filter data sources, data records, and/or other elements of an enterprise information environment such that query responses (or supporting traceability references) do not include information the user is not permitted to access”; (Siebel, [0076]; [0167]; [0187]).
Referring to Claim 2, Siebel teaches the store employee assistance system of claim 1. Siebel further teaches:
wherein the one or more user attributes includes at least one of: identity of the user, a role of the user within the enterprise and access rights associated with the user (Siebel, [0035]), “he enterprise generative artificial intelligence system 102 can use contextual information (e.g., user role, permissions, domain associated with the user, and the like)”; (Siebel, [0051]).
Referring to Claim 3, Siebel teaches the store employee assistance system of claim 1. Siebel further teaches:
wherein the contextual information comprises at least one of a user's location, time stamp, context of the question posed, or enterprise-specific data (Siebel, [0024]), “An enterprise comprehension module is used to understand the language, intent, and context of a user natural language query to discern relevant information from the enterprise information environment and generate deterministic responses”; (Siebel, [0039]), “This framework uses machine learning techniques (e.g., generative artificial intelligence algorithms and models) to navigate enterprise information and applications, comprehend organization specific context queues (e.g., acronyms, nicknames, jargon, and the like), and locate information most relevant to a request (e.g., query)”.
Referring to Claim 4, Siebel teaches the store employee assistance system of claim 2. Siebel further teaches:
wherein the response is customized by the generative artificial intelligence system to align with one or more of: the role of the user within the enterprise and with the access rights associated with the user (Siebel, [0034]), “The enterprise generative artificial intelligence system 102 protects information so that a user is permitted to access based on permissions, profiles, and controls. In one example, the enterprise access control layer 115 can filter information that is restricted by the retrieval model 122, prior to processing by the large language models 120, or presenting the answer or other output. More specifically, the enterprise access control layer 115 may filter data sources, data records, and/or other elements of an enterprise information environment such that query responses (or supporting traceability references) do not include information the user is not permitted to access”; (Siebel, [0177]), “The validation data and the access controls may be used together to select the deterministic response from the set of potential responses. For example, the validation data may be used to determine the data (e.g., any of documents, document segments, and insights of the respective portions of the one or more enterprise data sets) from the plurality of data domains on which a potential response and the access controls may be used to determine whether access to a potential response based on that data is permitted or should be restricted”.
Referring to Claim 5, Siebel teaches the store employee assistance system of claim 1. Siebel further teaches:
wherein the enterprise is a retail enterprise, and the user is an employee of the retail enterprise (Siebel, [0053]), “The enterprise generative artificial intelligence system 302 can facilitate the design, development, provisioning, and operation of a platform for industrial-scale applications in various industries, such as… retail”; (Siebel, [0066]), “Example data objects can include accounts, products, employees, suppliers, opportunities, contracts, locations, digital portals, geolocation manufacturers, supervisory control and data acquisition (SCADA) information, open manufacturing system (OMS) information, inventories, supply chains, bills of materials, transportation services, maintenance logs, and service logs”
Referring to Claim 6, Siebel teaches the store employee assistance system of claim 1. Siebel further teaches:
wherein the generative Al system includes one or more large language models (LLMs) or multimodal models (Siebel, [0025]), “Example aspects include systems and methods to implement machine learning models such as multimodal models, large language models (LLMs)”; (Siebel, [0158]; [0195]; [0027]; [0070]).
Referring to Claim 10, Siebel teaches the store employee assistance system of claim 1. Siebel further teaches:
wherein the chat service is further configured to:
store, in a historical database: the query, the response, session metadata comprising one or more of: user identity, timestamp, and session identifier, the relevant content used to generate the response, and model metadata associated with the generative Al system; wherein the stored data is used for at least one of: performance monitoring, analytics, topic clustering, or iterative improvement of prompt engineering (Siebel, [0131]), “if the enterprise comprehension module 806 determines that it needs additional information to satisfy the initial input, it can generate context-specific data (or, simply, “context”) that will inform future iterations of the process and help the system more efficiently and accurately satisfy the initial input. The context is based on the rationale used by the enterprise comprehension module 806 when it is processing queries (or other inputs). For example, the enterprise comprehension module 806 may receive segments of information retrieved by the retrieval module 804. The segments may be passages of data record(s), for example, and the segments may be associated with embeddings from an embeddings datastore 808 that facilitates processing by the enterprise comprehension module 806. A query and rational generator 812 of the enterprise comprehension module 806 can process the information and generate a rationale for why it produced the result that it did. That rationale can be stored by the enterprise generative artificial intelligence system in an historical rational datastore 810 and provide the foundation for the context subsequent iterations”; (Siebel, [0101]; [0134]).
Referring to Claim 11. Siebel teaches:
A method for providing assistance to users, the method comprising:
Claim 11 disclose substantially the same subject matter as claim 1, and is rejected using the same rationale as previously set forth.
Claim 12 disclose substantially the same subject matter as claim 2, and is rejected using the same rationale as previously set forth.
Claim 13 disclose substantially the same subject matter as claim 4, and is rejected using the same rationale as previously set forth.
Claim 14 disclose substantially the same subject matter as claim 3, and is rejected using the same rationale as previously set forth.
Claim 15 disclose substantially the same subject matter as claim 5, and is rejected using the same rationale as previously set forth.
Referring to Claim 16, Siebel the method of claim 11. Siebel further teaches:
further comprising parsing, vectorizing, and storing the relevant data into the vector database (Siebel, [0037]), “the enterprise generative artificial intelligence system 102 can generate embeddings based on the embedding models of the embedding models datastore 124 and the content of the data records. In some implementations, the embeddings may be represented by one or more vectors that can be stored in the vector datastore 126. In some implementations, the retrieval models 122 can use the embeddings to retrieve relevant data records and perform similarity or relevance evaluations or other aspects of retrieval operations”; (Siebel, [0084]-[0085]).
Claim 17 disclose substantially the same subject matter as claim 6, and is rejected using the same rationale as previously set forth.
Claim 18 disclose substantially the same subject matter as claim 10, and is rejected using the same rationale as previously set forth.
Referring to Claim 19, Siebel teaches:
A store employee assistance system comprising:
a chat interface configured to receive a query from a user via a user application (Siebel, [0047]), “interactive query portion 256 enables users to input additional related queries (e.g., “follow-up” questions) through an interactive input portion 257. In the example of the FIG. 2B, the interactive query portion 256 comprises a chat interface”; (Siebel, [0029]), “the query comprises a natural language query received through a graphical user interface”; (Siebel, [0031]);
a document uploader configured to parse, vectorize, and store enterprise data associated with an enterprise into a vector database (Siebel, [0031]), “The retrieval models 122 can interact with the large language models 120 and the domain models 108 to retrieve data records (e.g., documents, images, application outputs, artificial intelligence insights, objects, and the like) across different domains using data models 112 specific to each domain”; (Siebel, [0037]), “the enterprise generative artificial intelligence system 102 can generate embeddings based on the embedding models of the embedding models datastore 124 and the content of the data records. In some implementations, the embeddings may be represented by one or more vectors that can be stored in the vector datastore 126. In some implementations, the retrieval models 122 can use the embeddings to retrieve relevant data records and perform similarity or relevance evaluations or other aspects of retrieval operations”; (Siebel, [0084]-[0085]);
a chat service communicatively coupled to the chat interface and the vector database, the chat service configured to (Siebel, [0058]; [0148]; [0086]):
determine an identity of the user, a role of the user within the enterprise and access rights associated with the user (Siebel, [0035]), “enterprise generative artificial intelligence system can also perform similar functionality based on the context of users and/or systems submitting the query. For example, a director and engineer may submit the same query (e.g., “what projects are past due?”), and the enterprise generative artificial intelligence system 102 can use contextual information (e.g., user role, permissions, domain associated with the user, and the like) to provide a response that is based on context both substantively (e.g., provide information on overdue projects for the particular requester) and/or with respect presentation of the response”; (Siebel, [0036]), “the enterprise generative artificial intelligence system 102 can crawl, index, and/or map a corpus of data records (e.g., data records of one or more enterprise systems or environments) using contextual information (e.g., contextual metadata) along with data record embeddings to provide access control (e.g., role-based access)”; (Siebel, [0081]; [0084]);
retrieve relevant content from the vector database based on semantic similarity between the query and the vectorized enterprise data (Siebel, [0037]), “the enterprise generative artificial intelligence system 102 can generate embeddings based on the embedding models of the embedding models datastore 124 and the content of the data records. In some implementations, the embeddings may be represented by one or more vectors that can be stored in the vector datastore 126. In some implementations, the retrieval models 122 can use the embeddings to retrieve relevant data records and perform similarity or relevance evaluations or other aspects of retrieval operations”; (Siebel, [0034]), “the enterprise access control layer 115 can filter information that is restricted by the retrieval model 122… the enterprise access control layer 115 may filter data sources, data records, and/or other elements of an enterprise information environment such that query responses (or supporting traceability references) do not include information the user is not permitted to access”; (Siebel, [0081]; [ [0086]; [0087]);
generate a prompt combining the query and the relevant content (Siebel, [0079]), “the enterprise comprehension module 412 processes inputs to determine one or more results (i.e., output, response, or answer), determine rationales for results, and determine whether the enterprise comprehension module 412 needs more information to determine results. Enterprise comprehension module 412 may output information (e.g., results, new prompts, or additional queries) in a natural language format. In some implementations, features of one or more models of the enterprise comprehension module 412 can define conditions or functions that determine if more information is needed to satisfy the initial input or if there is enough information to satisfy the initial input”; (Siebel, [0129]), “an enterprise generative artificial intelligence system can receive an initial input 802 from a user or another system. For example, an orchestrator module 803 may receive the input 802. The enterprise generative artificial intelligence system can provide that input to a retrieval module 804 which can then reach out and “retrieve” information from various enterprise data sources (e.g., data stores, databases, artificial intelligence applications, and/or the like). The enterprise generative artificial intelligence system can use that information to generate an initial prompt for the enterprise comprehension module 806. The enterprise comprehension module 806 can process that initial prompt and determine whether it has enough information to satisfy criteria based on the initial input (e.g., answer a question). If it has enough information to satisfy the initial input, the enterprise comprehension module can then provide the result to a recipient, such as the user or system that provided the initial input”; (Siebel, [0132]);
a generative artificial intelligence system configured to formulate a response to the query based on the prompt, the user attributes, and the contextual information wherein the response is customized by the generative artificial intelligence system to align with one or more of: the role of the user within the enterprise and with the access rights associated with the user (Siebel, Fig. 2B, [0042]), “…FIG. 2B, the enterprise generative artificial intelligence response graphical user interface 250 includes an enterprise search query input portion 252, a generative enterprise search result portion 254, and an interactive query portion 256”; (Siebel, [0034]), “the enterprise generative artificial intelligence system 102 can use the enterprise access control layer 115 to implement additional enterprise controls… the enterprise access control layer 115 can filter information that is restricted by the retrieval model 122, prior to processing by the large language models 120, or presenting the answer or other output. More specifically, the enterprise access control layer 115 may filter data sources, data records, and/or other elements of an enterprise information environment such that query responses (or supporting traceability references) do not include information the user is not permitted to access”; (Siebel, [0076]; [0167]; [0187]); and
wherein, the chat service is further configured to:
store, in a historical database: the query, the prompt, the response, session metadata comprising one or more of: user identity, timestamp, and session identifier, the relevant content used to generate the response, and model metadata associated with the generative AI system (Siebel, [0131]), “if the enterprise comprehension module 806 determines that it needs additional information to satisfy the initial input, it can generate context-specific data (or, simply, “context”) that will inform future iterations of the process and help the system more efficiently and accurately satisfy the initial input. The context is based on the rationale used by the enterprise comprehension module 806 when it is processing queries (or other inputs). For example, the enterprise comprehension module 806 may receive segments of information retrieved by the retrieval module 804. The segments may be passages of data record(s), for example, and the segments may be associated with embeddings from an embeddings datastore 808 that facilitates processing by the enterprise comprehension module 806. A query and rational generator 812 of the enterprise comprehension module 806 can process the information and generate a rationale for why it produced the result that it did. That rationale can be stored by the enterprise generative artificial intelligence system in an historical rational datastore 810 and provide the foundation for the context subsequent iterations”; (Siebel, [0101]; [0134]); and
provide the response to the chat interface for display to the user on the user application (Siebel, Fig. 2B, [0042]), “…FIG. 2B, the enterprise generative artificial intelligence response graphical user interface 250 includes an enterprise search query input portion 252, a generative enterprise search result portion 254, and an interactive query portion 256”; (Siebel, [0034]; [0076]; [0167]; [0187]).
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 7 is rejected under 35 U.S.C. 103 as being unpatentable over Siebel et al., U.S. Publication No. 2024/0202221 [hereinafter Siebel], and further in view of Neervannan et al. U.S. Publication No. 2025/0061141 [hereinafter Neervannan].
Referring to Claim 7, Siebel teaches the store employee assistance system of claim 1. Siebel teaches in responding to a request, the enterprise comprehension module 412 may infer that a category of data is needed and request a specific retrieval model to retrieve data of the inferred category (see par. 0081), but Siebel does not explicitly teach:
further comprising a topic analysis engine configured to:
receive a plurality of queries over a period of time;
analyze the received queries to identify trending topics across the plurality of queries;
generate topic clusters based on semantic similarity between the plurality of queries;
submit the topic clusters to the generative AI system for summarization; and
provide summarized topic analysis results to the user via a topic analysis interface.
However Neervannan teaches:
further comprising a topic analysis engine configured to:
receive a plurality of queries over a period of time; analyze the received queries to identify trending topics across the plurality of queries; generate topic clusters based on semantic similarity between the plurality of queries (Neervannan, [0210]-[0211]), “models may determine that most analysts are currently focusing their discussions on a particular topic, such as commercial real estate. Thus, the models identify the topic of commercial real estate as relevant across the industry. This information and topic may then be used to analyze trends as they relate to commercial real estate in one or more industries… this allows for real-time or near real-time identification of momentum and/or relevance of topics, which may be used to automatically surface trending topics providing insight to users without the users specifically requesting those insights or needing to know what topics they are interested a priori”; (Neervannan, [0066]), “provide views of trends and summaries over time, as well as how they change over time. They may be time-limited or time-specific”; and
submit the topic clusters to the generative AI system for summarization; and provide summarized topic analysis results to the user via a topic analysis interface (Neervannan, [0212]-[0213]), “topics and their associated snippets are provided as input to the summarization engine, which generates summaries based on the topics and snippets. The topics allow for the summarization engine to perform aspect-based summarization organized around the topics… the market intelligence platform may provide a daily summary (or a summary on another time frame) that is generated based on a user's preferences, recommendations, and/or predicted actions. As the user interacts with the system, the system is configured to learn about the types of information the user is interested in, and when the user is interested in that information”; (Neervannan, Fig. 22, [0266]), “FIG. 22 depicts an example of a user interface providing topic summaries according to the subject matter disclosed herein”; (Neervannan, [0271]), “Summary pane 2706 shows AI-generated summaries 2708”; (Neervannan, [0253]-[0254]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the categories in Siebel to include the queries limitations as taught by Neervannan. The motivation for doing this would have been to improve the method of combining user search with capabilities of natural language processing, generative AI, and predictive analysis that enable enterprise users to ask open-ended, multi-level, context specific questions in Siebel (see par. 0004) to efficiently include the results of providing an overall summary of the responses to a prompt. (see Neervannan par. 0284).
Claims 8, 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Siebel et al., U.S. Publication No. 2024/0202221 [hereinafter Siebel], and further in view of Rosu et al. U.S. Publication No. 2022/0027768 [hereinafter Rosu].
Referring to Claim 8, Siebel teaches the store employee assistance system of claim 1. Siebel further teaches:
further comprising a feedback interface configured to:
receive an initial feedback selection from the user regarding the response (Siebel, [0046]), “The response feedback portion 270 enables users to provide feedback regarding the response (e.g., positive or negative feedback)”; (Siebel, [0095]), “The model optimization module 428 can function to obtain user feedback (e.g., regarding the final result/answer)”; (Siebel, [0123]); and
store the feedback selection, any provided detailed feedback content, and associated contextual data in a historical database for analysis and system improvement (Siebel, [0134]), “he user may also from feedback 816 which can be stored in a feedback datastore 818. The enterprise generative artificial intelligence system can, in some embodiments, use the feedback to improve the accuracy and/or reliability of the system”.
Siebel teaches a feedback graphical icon can enable users to provide feedback regarding the response (e.g., positive or negative feedback). Enterprise generative artificial intelligence systems can, for example, use the received feedback to improve enterprise generative artificial intelligence systems (see par. 0123), but Siebel does not explicitly teach:
determine whether additional feedback justification is required; and
when it is determined that the additional feedback justification is required, prompt the user to provide detailed feedback content explaining why the response was unsatisfactory.
However Rosu teaches:
determine whether additional feedback justification is required; and when it is determined that the additional feedback justification is required, prompt the user to provide detailed feedback content explaining why the response was unsatisfactory (Rosu, [0051]), “If the user is not satisfied with the answer and wants to continue the interaction, the system may collect implicit negative feedback (308)… ‘the answer is bad’ or ‘the answer is not helpful’ are examples of negative feedback. In an exemplary embodiment, in the case of negative feedback, multi-choice questions may be utilized to qualify the scope of error, such as ‘content is incorrect’ or ‘content not found’”; (Rosu, [0052]), “the enrichment may be through the chatbot platform or through ground truth provided by a subject matter expert (SME). Similarly, the enrichment may assess presence of a knowledge gap, if any, and resolve such gaps through the domain knowledge enrichment in the form of feedback interaction via the chatbot (162). It is understood in the art that various methods may have been utilized to generate responses to the chatbot, and as such, some matches between a question, e.g. input, and a corresponding generated answer may be partial, e.g. partially good or partially bad. Although they may be considered overall as a good answer, there may be knowledge gaps present that could benefit from additional information. Similarly, the system may consider an answer to be good, when the feedback is negative, and as such requires or could benefit from identification information or knowledge to clarify the discrepancy”; (Rosu, [0086]), “policy may be used to determine when to trigger interaction for an explanation request at step (318) in order to control impact on user satisfaction. An example policy may be in the form of only implement on negative feedback”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the feedback in Siebel to include the feedback justification limitations as taught by Rosu. The motivation for doing this would have been to improve the method of using the feedback to improve the accuracy and/or reliability of the system in Siebel (see par. 0134) to efficiently include the results of improve performance of the automated virtual dialog agent (see Rosu par. 0001).
Referring to Claim 9, Siebel in view of Rosu teaches the store employee assistance system of claim 8. Siebel teaches a feedback graphical icon can enable users to provide feedback regarding the response (e.g., positive or negative feedback). Enterprise generative artificial intelligence systems can, for example, use the received feedback to improve enterprise generative artificial intelligence systems (see par. 0123), but Siebel does not explicitly teach:
wherein the determination of whether the additional feedback justification is required is based on at least one of: the type of initial feedback selection, a classification of the question topic, the user attributes, or predefined policy criteria.
However Rosu teaches:
wherein the determination of whether the additional feedback justification is required is based on at least one of: the type of initial feedback selection, a classification of the question topic, the user attributes, or predefined policy criteria (Rosu, [0051]), “If the user is not satisfied with the answer and wants to continue the interaction, the system may collect implicit negative feedback (308)… ‘the answer is bad’ or ‘the answer is not helpful’ are examples of negative feedback. In an exemplary embodiment, in the case of negative feedback, multi-choice questions may be utilized to qualify the scope of error, such as ‘content is incorrect’ or ‘content not found’”; (Rosu, [0067]), “negative feedback, selection may take plan on different criteria. Artifacts of an explanation management system provide for the following types of knowledge: rules to associate sets of patterns into types of explanation, presentation templates to generate output for the user providing explanation, and system or management policy to be applied when more than one explanation types matches an input”; (Rosu, [0086]), “policy may be used to determine when to trigger interaction for an explanation request at step (318) in order to control impact on user satisfaction. An example policy may be in the form of only implement on negative feedback”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the feedback in Siebel to include the feedback justification limitations as taught by Rosu. The motivation for doing this would have been to improve the method of using the feedback to improve the accuracy and/or reliability of the system in Siebel (see par. 0134) to efficiently include the results of improve performance of the automated virtual dialog agent (see Rosu par. 0001).
Referring to Claim 20, Siebel teaches the store employee assistance system of claim 19. Siebel further teaches:
further comprising a feedback interface configured to:
receive an initial feedback selection from the user regarding the response (Siebel, [0046]), “The response feedback portion 270 enables users to provide feedback regarding the response (e.g., positive or negative feedback)”; (Siebel, [0095]), “The model optimization module 428 can function to obtain user feedback (e.g., regarding the final result/answer)”; (Siebel, [0123]); and
store the feedback selection, any provided detailed feedback content, and associated contextual data in a historical database for analysis and system improvement (Siebel, [0134]), “he user may also from feedback 816 which can be stored in a feedback datastore 818. The enterprise generative artificial intelligence system can, in some embodiments, use the feedback to improve the accuracy and/or reliability of the system”.
Siebel teaches a feedback graphical icon can enable users to provide feedback regarding the response (e.g., positive or negative feedback). Enterprise generative artificial intelligence systems can, for example, use the received feedback to improve enterprise generative artificial intelligence systems (see par. 0123), but Siebel does not explicitly teach:
determine whether additional feedback justification is required, wherein the determination of whether the additional feedback justification is required is based on at least one of: the type of initial feedback selection, a classification of the question topic, the user attributes, or predefined policy criteria; and
when it is determined that the additional feedback justification is required, prompt the user to provide detailed feedback content explaining why the response was unsatisfactory.
However Rosu teaches:
determine whether additional feedback justification is required, wherein the determination of whether the additional feedback justification is required is based on at least one of: the type of initial feedback selection, a classification of the question topic, the user attributes, or predefined policy criteria; and when it is determined that the additional feedback justification is required, prompt the user to provide detailed feedback content explaining why the response was unsatisfactory (Rosu, [0051]), “If the user is not satisfied with the answer and wants to continue the interaction, the system may collect implicit negative feedback (308)… ‘the answer is bad’ or ‘the answer is not helpful’ are examples of negative feedback. In an exemplary embodiment, in the case of negative feedback, multi-choice questions may be utilized to qualify the scope of error, such as ‘content is incorrect’ or ‘content not found’”; (Rosu, [0052]), “the enrichment may be through the chatbot platform or through ground truth provided by a subject matter expert (SME). Similarly, the enrichment may assess presence of a knowledge gap, if any, and resolve such gaps through the domain knowledge enrichment in the form of feedback interaction via the chatbot (162). It is understood in the art that various methods may have been utilized to generate responses to the chatbot, and as such, some matches between a question, e.g. input, and a corresponding generated answer may be partial, e.g. partially good or partially bad. Although they may be considered overall as a good answer, there may be knowledge gaps present that could benefit from additional information. Similarly, the system may consider an answer to be good, when the feedback is negative, and as such requires or could benefit from identification information or knowledge to clarify the discrepancy”; (Rosu, [0067]), “negative feedback, selection may take plan on different criteria. Artifacts of an explanation management system provide for the following types of knowledge: rules to associate sets of patterns into types of explanation, presentation templates to generate output for the user providing explanation, and system or management policy to be applied when more than one explanation types matches an input”; (Rosu, [0086]), “policy may be used to determine when to trigger interaction for an explanation request at step (318) in order to control impact on user satisfaction. An example policy may be in the form of only implement on negative feedback”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the feedback in Siebel to include the feedback justification limitations as taught by Rosu. The motivation for doing this would have been to improve the method of using the feedback to improve the accuracy and/or reliability of the system in Siebel (see par. 0134) to efficiently include the results of improve performance of the automated virtual dialog agent (see Rosu par. 0001).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Feng et al. (US 20260127383 A1) - There are provided systems and methods for a search and answer generation engine for data summarization from multiple data sources. An online transaction processor or other service provider may provide computing services and platforms to entities, which may include live agent and self-service assistance features for answering users' questions. To provide more comprehensive searching and automated answer generation, the service provider may utilize an answer engine that may search multiple data sources in different data formats. Keywords may be extracted from a natural language question using an embedding LLM, and API calls to search features of each data source may be executed to retrieve relevant content. A summarization LLM may then concisely summarize the different content in different formats so that an answer may be provided. The user may then refine their question with further questions or requests, which may adjust the keywords and/or summarization.
Doulton et al. (CA 2641853 C) - A mass-scale, user-independent, device-independent, voice messaging system that converts unstructured voice messages into text for display on a screen is disclosed. The system comprises (i) computer implemented sub-systems and also (ii) a network connection to human operators providing transcription and quality control; the system being adapted to optimise the effectiveness of the human operators by further comprising 3 core sub-systems, namely (i) a pre-processing front end that determines an appropriate conversion strategy; (ii) one or more conversion resources; and (iii) a quality control sub-system.
Al-Maaitah et al. (Electronic Commerce in the ERA of Generative Artificial Intelligence) - This paper provides a detailed landscape analysis of the generative AI space, investigating use cases across research, product creation, marketing, and advertising. It also identifies key industry trends, including the a-state of New Players, meteoric rise of Text Generate Platforms, and urgent demand for user-friendly Generative AI tools. A range of applications have been created by tech firms in generative AI, which is an area of machine learning that creates and produces material using a generative model. Advances in artificial intelligence (AI), amount of internet data, and widespread acceptance of cloud computing are all accelerating the development of generative AI technologies. Significant development and access have been made available to the general population for text generation models.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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