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 .
The application has been examined. Claims 1 – 20 are pending in this office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Based upon consideration of all of the relevant factors with respect to the claims as a whole, claims 1 – 20 are determined to be directed to an abstract idea and not significantly more than the abstract idea itself. The rationale for this determination is explained below:
The representative claim 1 (and other independent claims) recites a processing method of a generative model, the method comprising: obtaining a prompt; simplifying the prompt into a simplified prompt including an intent and details; searching for stored records of tasks performed prior to the obtaining the prompt, based on the intent of the simplified prompt; based on identifying a stored record of a task performed prior to the obtaining the prompt, the task corresponding to the intent of the simplified prompt, executing the generative model according to the simplified prompt using an intermediate computation result corresponding to the task and outputting an execution result of the generative model.
The claims as drafted recite a process that, under broadest reasonable interpretation, covers mental process but for the generic computer components. Before computers when a prompt was received, which could be a question or enquiry, the person to whom the question was asked, could simplify the question in their mind as to what the purpose of the question is along with other details. In their mind the person could figure out what information matched the question and its purpose, where this could be information that was previously deduced by the person because of a previous question, and the information matched would be outputted or presented. This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements of an “electronic device”, “processor”, “processing circuitry”, to perform steps. These computer components are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitation of obtaining a prompt is a recitation of an insignificant extra-solution, a pre-solution activity which is a step of gathering data in a claimed method. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978) See MPEP 2106.05 (g) examples of activities that the courts have found to be insignificant extra-solution activity, Mere Data Gathering: Consulting and updating an activity log, Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754. Furthermore the recitation of outputting a result, under broadest reasonable interpretation is not more than addition of insignificant extra-solution activity which does not amount to an inventive concept because adding a final step of transmitting data for review does not add a meaningful limitation to the method of filtering content. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity see MPEP 2106.05 (a) I (iii). The receiving and transmitting steps are directed to well-understood, routine, and conventional activities. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. 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); TLI MPEP 2106.05(d)(II). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is not patent eligible.
Claims 2 – 11, and 13 – 20 further narrow the abstract idea recited in the independent claims 1, and 12 and are therefore directed towards the same abstract idea. The dependent claims are directed towards further narrowing the abstract idea of receiving collection data and processing the received data to determine a profile ranking for a plurality of profiles.
Claims 2 – 11, and 13 – 20 do not recite any additional elements that have not already been analyzed above. Therefore, the claims do not direct the claims to recite a practical application.
Therefore, claims 1 – 20 are rejected under U.S.C. 101.
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1 – 20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Bhardwaj et al. (US 20230342559 A1) (‘Bhardwaj’ herein after) further in view of Kulkarni et al. (US 20240311652 A1) (‘Kulkarni’ herein after) further in view of Hosseinisianaki et al. (US 20200344194 A1) (‘Hosseinisianaki’ herein after).
With respect to claim 1, 10, 11, 12,
Bhardwaj discloses a processing method of a generative model, the method comprising: obtaining a prompt (figure 7 #702, paragraph 20 – 24, 64 teaches receiving an input text and prompts, Bhardwaj); simplifying the prompt into a prompt including an intent (figure 7 #702, paragraph 20 teaches the task of intent classification, the prompt may take a form as “the intent of the sentence is {intent}”, paragraph 21 – 24 teaches the contextual prompt which can be understood as the simplified prompt with intent and details, paragraph 64 – 67 teach the contextualized representation of the input received, Bhardwaj); searching for stored records of tasks performed prior to the obtaining the prompt, based on the intent of the simplified prompt (figure 7 #710, #712, paragraph 54, 59 teaches the previous and historical data, Bhardwaj); based on identifying a stored record of a task, the task corresponding to the intent of the simplified prompt, executing the generative model according to the simplified prompt using an intermediate computation result corresponding to the task and outputting an execution result of the generative model (figure 7 #710, #712, paragraphs 20 – 24 and 63 – 69, Bhardwaj).
Bhardwaj teaches searching stored records but does not teach tasks performed prior to the obtaining the prompt explicitly as claimed.
However, Kulkarni teaches tasks performed prior to the obtaining the prompt in paragraphs 43, 46 – 49, 67 – 71 and 78 teach using heuristics or machine-learned models to determine the semantic structure of a preliminary prompt input. Based on the semantic structure, a determined task, historical data, stored templates, stored effective prompts, and/or the contents of the text, template suggestions, autocompletion suggestions, and/or structure suggestions can be determined and provided to the user to aid in prompt generation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention because both references are directed to the same field of study prompt simplification. Furthermore, Kulkarni teaches in paragraphs 28 – 29 the benefits of combining its method with Bhardwaj as leveraging a specialized markup language and user interface to generate refined prompts without relying on user knowledge. The use of a specialized markup language can enable a markup language transform that generates a refined prompt that leverages known weighting techniques and terminology to generate a prompt that captures an intent of a user. Additionally, the integrated development environment can be utilized to receive inputs and provide indicators of identified tokens, errors, labels, etc. In some implementations, the systems and methods can include determining and providing selection of one or more prompt term suggestions. The interface elements paired with the specialized markup language can allow unversed users to generate refined prompts with detailed terms and particularized structure that can be utilized to retrieve generative outputs that encapsulate a user's intent.
Bhardwaj teaches simplifying the prompt but does not teach explicitly as claimed the details along with the intent from the input.
Hosseinisianaki teaches the details being extracted along with the intent from the input in paragraphs 51 – 55 teaching that the content encoder takes, as input, target content. The content encoder extracts content features from the content. Similarly, the context encoder extracts context features from context. The context can include metadata of the communication, such as can indicate a date, time, author, signature, text from other, related communications, such as another message in a chain of messages. The outputs of the content encoder and context encoder may be used as input to a feature fusion operation which generates a context-aware content representation that can be used by an intent classification operation to determine an intent in the content.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention because both references are directed to the same field of intent and purpose detection. Furthermore, Hosseinisianaki modifies the combination of Bhardwaj and Kulkarni because it incorporates the advantage of extracting a request for action or an action-item. Primarily there is the benefit of detecting intent at a finer grain along with leveraging context to improve intent detection. Previous work focused on identifying the sentences in the email that contain requests for action items. Detecting intents made in communications enables PIMs to help users recall promises they have made and help users complete them in a timely manner and can use one or more machine learning models that are trained to classify the intent.
With respect to claim 2, 13,
Bhardwaj as modified discloses the method of claim 1, wherein the simplifying the prompt comprises: extracting key tokens from a plurality of tokens included in the prompt and reducing a number of tokens by performing vector quantization on a set of tokens indicating the intent among the extracted key tokens (figure 7 #707, #708, paragraphs 21, 31, 66 – 67 teach the reduction of noise in the prompt teaching that the input adapted-prompt tokens, which are the prompt representations from the sentence encoder, are then “quantized” to reduce the noise, Bhardwaj).
With respect to claim 3, 14,
Bhardwaj as modified discloses the method of claim 1, wherein the simplifying the prompt comprises: converting the prompt into a vector (figure 7, paragraph 63 – 64, Bhardwaj); selecting a vector, from a code book for vector quantization, corresponding to a shortest prompt included in a same cluster as the converted vector and changing the prompt to the shortest prompt (figure 7, paragraphs 66 – 67 and 71 – 75 teach the codebook vectors along with vector quantizer, Bhardwaj).
With respect to claim 4, 15,
Bhardwaj as modified discloses the method of claim 1, wherein information related to the tasks and prompts corresponding to the tasks are stored on a task database (paragraph 59, Bhardwaj), and
wherein the searching for the stored records of tasks performed prior to the obtaining the prompt comprises: searching for prompts that include the same intent as the simplified prompt among the prompts stored in the task database and based on identifying at least one prompt that includes the same intent as the simplified prompt, comparing at least one detail of the at least one prompt with the details of the simplified prompt (paragraphs 34 – 36 and 67 – 70, Hosseinisianaki).
With respect to claim 5, 16,
Bhardwaj as modified discloses the method of claim 4, wherein the executing the generative model comprises: based on identifying a first prompt that includes different details than the simplified prompt from the at least one prompt, obtaining, from the task database, a hidden state matrix corresponding to the first prompt and changing the obtained hidden state matrix based on the details of the simplified prompt, and performing computation of the generative model using the changed hidden state matrix (paragraphs 34 – 36 and 67 – 70, Hosseinisianaki).
With respect to claim 6, 17,
Bhardwaj as modified discloses the method of claim 4, wherein the executing the generative model comprises: based on identifying a second prompt that includes same details as the simplified prompt from the at least one prompt, obtaining, from the task database (paragraph 58 – 60 and 79 – 83, Kulkarni), an Application Programming Interface (API) request corresponding to the second prompt and executing the API request without performing computation of the generative model (paragraph 122, 124 and 142, Kulkarni).
With respect to claim 7, 18,
Bhardwaj as modified discloses the method of claim 1, wherein information related to the tasks and prompts corresponding to the tasks are stored on a task database (figure 7, paragraph 59, 63 – 64, Bhardwaj) and wherein the searching for the stored records of tasks performed prior to the obtaining the prompt comprises: obtaining, from the task database, a code book in which a plurality of token sequences are clustered by intent; identifying a code index corresponding to the intent of the simplified prompt, from the code book and comparing at least one detail linked to the identified code index with the details of the simplified prompt (paragraphs 34 – 36 and 67 – 70, Hosseinisianaki).
With respect to claim 8, 19,
Bhardwaj as modified discloses the method of claim 7, wherein the executing the generative model comprises: based on the at least one detail linked to the identified code index being different from the details of the simplified prompt, obtaining, from the task database, a hidden state matrix corresponding to the identified code index and changing the obtained hidden state matrix based on the details of the simplified prompt, and performing computation of the generative model using the changed hidden state matrix (paragraphs 34 – 36, 67 – 69 and 73, Hosseinisianaki).
With respect to claim 9, 20,
Bhardwaj as modified discloses the method of claim 7, wherein the executing the generative model comprises: based on the at least one detail linked to the identified code index being same as the details of the simplified prompt, obtaining, from the task database, an API request corresponding to the at least one detail linked to the identified code index and executing the API request without performing computation of the generative model (paragraph 79 – 83, 122, 124 and 142, Kulkarni).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230394038 A1 teaches a software application configured to receive a query that includes a textual representation of a problem, and generate, by the machine learning model and based on the textual representation of the query, a query intent therefor. When the query intent is determined to be one of the query intents mapped to a predetermined solution, the predetermined solution for the query may be selected from the predetermined solutions based on the mapping.
US 20190370398 A1 teaches a query associated with the audio input based at least on the audio input, wherein the query comprises one or more entities each associated with one or more contents; determining whether the query is related to a historical activity based at lease on the one or more entities each associated with the one or more contents; and in response to determining that the query is related to a historical activity, searching historical data based on the query associated with the audio input.
US 20250342182 A1 teaches a user query intended for a Large Language Model (LLM), analyze the user query, and determine prompt-enriching information that is combined with the user query to generate an enriched user query that is ultimately communicated to the LLM. In this manner, additional prompt-enriching information or context is added to the user query before being communicated to the LLM so that the additional prompt-enriching information, along with the user query, can be tokenized to better guide the LLM to a more accurate answer without modifying weights, parameters, or training of the LLM.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVNEET K GMAHL whose telephone number is (571)272-5636.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SANJIV SHAH can be reached on . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAVNEET GMAHL/Examiner, Art Unit 2166 Dated: 7/23/2026
/KHANH B PHAM/Primary Examiner, Art Unit 2166
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