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 .
Information Disclosure Statement
The filed information disclosure statement (IDS) is being considered by the examiner.
Claim Rejections - 35 USC § 101
3. 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 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. 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.
Step 1: Is the claimed invention to a process, machine, manufacture or composition of matter?
The claimed invention, at independent claims 1, 12, 23, and 30, is directed to a device (machine), method (process), and computer readable medium (manufacture) for receiving a user prompt; obtaining user context information from one or more sources of physical context information and user background information; using the received user prompt and the obtained user context information to generate a contextualized prompt for submission to a large generative artificial intelligence model (LXM); and outputting the generated contextualized prompt to the LXM.
Step 2A, prong 1: Does the claim recite an abstract idea, law or nature, or natural phenomenon?
Under the 35 U.S.C. 101 new guidelines, the broadest reasonable interpretation of the claims, the claimed steps fall within the “Mental Processes” grouping of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
The step of receiving a user prompt; obtaining user context information from one or more sources of physical context information and user background information; using the received user prompt and the obtained user context information to generate a contextualized prompt for submission to a large generative artificial intelligence model (LXM), may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, a human can receive a prompt from a user, obtain context information and user background information, and use the context information and user background information to generate a contextualized prompt without using a machine. The step of outputting the generated contextualized prompt to the LXM is recited at a high level of generality, and thus is insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). The claim does not provide any details about how the large generative artificial intelligence model (LXM) operates. Therefore, the claimed steps fall within the mental process grouping of abstract ideas.
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements of “a processor” and “a large generative artificial intelligence model (LXM) are mere data gathering and manipulating recited at high level of generality, and thus are insignificant extra-solution activity. The processor is recited at a high level of generality, and it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The recitation of “a large generative artificial intelligence model (LXM)” also is at high level of generality. The mere nominal recitation of a generic network appliance does not take the claims limitations out of the mental processes grouping. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claims are directed to the judicial exception.
Step 2B: Does the claim recite additional elements that amount to significantly more than the abstract idea?
As to whether the claims as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim (Step 2B), as explained above in Step 2A, Prong 2, the use of “a large generative artificial intelligence model (LXM)” and “processor” are at high level of generality, and even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, and therefore do not provide an inventive concept. Accordingly, the claims are ineligible.
Dependent claims 2-11, 13-22, 24-29 further refer and describe the process of obtaining the user context information from the one or more sources of physical context information and the user background information (claims 2-5, 13-16, 24-27), which is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. Claims 6-11, 17-22, and 28-29 relate to selecting one of the plurality of relevance models, selecting the LXM, which is recited at high level of generality; outputting the generated contextualized prompt to the LXM, which is as well recited at high level of generality and does not show how the selection is performed, and relate to insignificant extra-solution activity; obtaining local context information and generating the contextualized prompt may be practically performed in the human mind using observation, evaluation, judgment, and opinion.
Accordingly, claims 1-30 are directed to an abstract idea, and are not patent eligible.
CLAIM INTERPRETATION UNDER 35 USC § 112(f)
4. 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.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means,” and are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: means for obtaining user context information; means for using the received user prompt and the obtained user context information; and means for outputting the generated contextualized prompt.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
5. 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 1-30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lo (US 12,061,970).
As per claim 1, Lo teaches a memory; and at least one processor coupled to the memory (Fig. 5) and configured to:
receive a user prompt (Fig. 1, col. 7, line 41-47, receiving a user query);
obtain user context information from one or more sources of physical context information and user background information (col. 10, line 10-37, obtaining contextual information such as location, time of the day, user, user department, user firm, user entitlements, user job function, etc.);
use the received user prompt and the obtained user context information to generate a contextualized prompt for submission to a large generative artificial intelligence model (LXM) col. 7, line 41-47, using the received user’s query and the contextual information to generate a contextualized prompt for submission to the model orchestration Large Language Model (LLM)); and
output the generated contextualized prompt to the LXM (Fig. 1 and col. 7, line 41-47, injecting the contextual query to the model orchestration Large Language Model (LLM) to obtain a response to the user's query. See also, col. 3, line 19-56, wherein contextual information received from the user’s profile is used to generate a contextual prompt. the contextual prompt is input into a model orchestration large language model to inject context into a model orchestration large language model runtime of the model orchestration large language model; input the natural language request of the data record query as a data record query prompt into the model orchestration large language model to output at least one instruction to at least one data record processing machine learning agent of a plurality of data record processing machine learning agents based at least in part on trained parameters of the model orchestration large language model and the at least one context attribute).
As per claim 2, Lo teaches wherein the at least one processor is further configured to obtain the user context information from the one or more sources of physical context information and the user background information by obtaining a summary user context from a user profile summary table in memory that correlates a user profile with categories of instantaneous context and historical context (col. 10, line 26-37, the contextual data may be stored in a user profile associated with the user, e.g., in the database 122 or other data store. Col. 8, line 26-46, wherein said, the database model may include, e.g., a navigational database, a hierarchical database, a network database, a graph database, an object database, a relational database, an object-relational database …).
As per claim 3, Lo teaches wherein the at least one processor is further configured to process the obtained user context information in a lightweight profile summary model that is trained to receive the obtained user context information and output the user context information in a language format (col. 3, line 19-56, wherein contextual information received from the user’s profile is used to generate a contextual prompt. the generate a contextual prompt is into a model orchestration large language model to inject context into a model orchestration large language model runtime of the model orchestration large language model; input the natural language request of the data record query as a data record query prompt into the model orchestration large language model to output at least one instruction to at least one data record processing machine learning agent of a plurality of data record processing machine learning agents based at least in part on trained parameters of the model orchestration large language model and the at least one context attribute).
As per claim 4, Lo teaches wherein the at least one processor is further configured to use the received user prompt and the obtained user context information to generate the contextualized prompt for submission to the LXM by appending to the user prompt the user context information that is relevant to the user prompt (col. 3, line 24-38, inputting user persona attributes into a model orchestration large language model to inject context into a model orchestration large language model runtime of the model orchestration large language model) .
As per claim 5, Lo teaches wherein the at least one processor is further configured to use the received user prompt and the obtained user context information to generate the contextualized prompt for submission to the LXM by: processing the received user prompt and the user context information in a relevance model that is trained to receive as inputs the user prompt and the user context information and generate relevance model output that includes elements of the user context information that are relevant to the user prompt; and combining the user prompt and the relevance model output in a contextualized prompt generator that is trained to output an LXM prompt that combines relevant user context information with information in the user prompt (col. 7, line 47-52, wherein said, to improve accuracy of the data as well as presentation in the GUI, a context engine 112 may inject context data into the model orchestration LLM 114 based on the identity of the user, the user's query, among other factors or any combination thereof. col. 15, line 21-55, ML models to identify the most relevant elements to the user's request by providing training queries to the models to produce predicted data elements, such as by scoring a relevance of each data element received by retrieval agents, determining errors of the predicted elements, employing loss functions).
As per claim 6, Lo teaches wherein the at least one processor is further configured to: select a correlation relevance model from among a plurality of relevance models based on a subject matter in the received user prompt; and process the received user prompt and the user context information in the relevance model by processing the received user prompt and the user context information in the selected correlation relevance model (col. 14, line 50-55 and col. 16, line 1-6, wherein correlation between a user's request and the classes and/or categories that are subject of the user's request is modeled).
As per claim 7, Lo teaches wherein the at least one processor is further configured to select one of the plurality of relevance models by processing the received user prompt by a language model that is trained to identify a category of subject matter in the received user prompt and select one of the plurality of relevance models corresponding to the identified category of subject matter (col. 13, line 62 – col.14, line 60, wherein the recognition agent may include
ML-based functionality to interpret a user's request and classifies the request into one or more classes and/or categories, determine whether the user request relates to a financial instrument, a type of a financial instrument, a sector associated with a financial instrument, a market associated with a financial instrument, a geography associated with a financial instrument, among other categories and/or classes, and thereof select the appropriate model to provide the right response to the user).
As per claim 8, Lo teaches wherein the at least one processor is further configured to: select the LXM from a plurality of available LXM models based on a physical context of a user in the user context information; and output the generated contextualized prompt to the LXM by outputting the generated contextualized prompt to the selected LXM (col. 6, line 6-15, col. 10, line 10-37, obtaining contextual information such as location, time of the day, user, user department, user firm, user entitlements, user job function; and col. 13, line 62 – col.14, line 60 for selecting the appropriate model to provide the right response to the user).
As per claim 9, Lo teaches wherein the at least one processor is further configured to: process the received user prompt by a language model that is trained to identify a category of subject matter in the received user prompt; use the identified category of subject matter to select the LXM from a plurality of available LXM models to which the generated contextualized prompt will be applied; and output the generated contextualized prompt to the LXM by outputting the generated contextualized prompt to the selected LXM (col. 13, line 62 – col.14, line 60, wherein the recognition agent may include ML-based functionality to interpret a user's request and classifies the request into one or more classes and/or categories, and determine among which category and/or classes the user’s request is relevant to, and thereof select the appropriate model to provide the right response to the user).
As per claim 10, Lo teaches wherein the contextualized prompt generator is a large language model trained to generate the LXM prompt for the selected LXM to include information phrased in a manner that will cause the LXM to generate a reply that is responsive to the received user prompt based on knowledge of how the selected LXM responds to prompt rhetoric (col. 6, line 6-21, machine learning (ML)-based software agents with one or more LLMs such that the LLM(s) provide orchestration of the ML-based software agents).
As per claim 11, Lo teaches wherein the at least one processor is further configured to: obtain local context information from a data source available on a local context database; and use the obtained local context information in conjunction with the received user prompt and the obtained user context information to generate the contextualized prompt for submission to the LXM (col. 10, line 38-47, the context engine 112 retrieves data ( over the internet, over local, over cloud) from multiple sources (API, flat files, databases) and inserts it into the model orchestration LLM 114 runtime context).
As per claims 12-22, method claims 12-22 and apparatus claims 1-11 are related as method and apparatus of using same, with each claimed element's function corresponding to the claimed method step. Accordingly claims 12-22 are similarly rejected under the same rationale as applied above with respect to apparatus claims 1-11.
As per claims 23-29, claims 23-29 recite means for performing the steps as claimed by claims 1-11. Therefore, claims 23-29 are rejected under the same rationale as applied above with respect to apparatus claims 1-11.
As per claim 30, Lo teaches a computer readable medium (col. 3, line 21). The remaining steps are rejected under the same rationale as applied to the method steps of rejected claim 1.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDELALI SERROU whose telephone number is (571)272-7638. The examiner can normally be reached M-F 9 Am - 5 PM.
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/ABDELALI SERROU/Primary Examiner, Art Unit 2659