DETAILED ACTION
This Office Action is responsive to Request for Continued Examination filed on February 17th, 2026.
Claims 1, 8 and 15 are amended, claims 4-6, 11-13 and 18-20 are cancelled. Claims 1-3, 7-10 and 14-17 are pending and have been examined.
Any rejections/objection not addressed in this Office Action have been withdrawn by the Examiner.
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 .
Response to Amendments and Arguments
Rejections under 35 U.S.C. 101:
Regarding rejections made under 35 U.S.C. 101, Applicant argues “that the amended independent claims 1, 8, and 15 include the components or steps of the invention that provide the improvement described in the specification. For example, independent claims 1, 8, and 15 have been amended to recite, in part, ‘classifying the query to determine whether the query is associated with a user with a disability, wherein classifying the query includes processing metadata from the query with contextual information about disability-related functionality enabled on a user's computing device, wherein classifying the query as being associated with a user with a disability includes performing a multi- class classification associated with the query using a classification-based machine learning model’. That is, by classifying the query as being associated with a user with a disability in a particular class of a multi-class classification, a particular customized LLM corresponding to that particular class may be used to process the user's query in a manner consistent with the classified disability. The query is classified by processing metadata from the query with contextual information about disability-related functionality enabled on the user's computing device. In this manner, amended independent claim 1, 8, and 15 include the components or steps of the invention that provide the improvement described in the specification,” (emphasis original, page 9 of Remarks).
Applicants arguments are not persuasive. Each core action described by the claims may be performed by a human actor, even if the object of the action implies a technical application. Under the broadest reasonable interpretation, the human mind is capable of classifying a query based on metadata – or information outside the body of the query – choosing a suitable protocol for responding to the query, then processing that query appropriately. Accordingly, the rejections under 35 U.S.C. 101 are maintained.
Rejections under 35 U.S.C. 103:
Starting on page 11 of Remarks, Applicant argues, “that the combination of Nguyen, Burstein, and/or Pillitteri is not understood to teach, disclose, or suggest that classifying the query includes processing metadata from the query with contextual information about disability-related functionality enabled on a user's computing device.
“As such, Applicant respectfully submits the combination of Nguyen, Burstein, and/or Pillitteri is not understood to teach, disclose, or suggest Applicant's claimed ‘classifying the query to determine whether the query is associated with a user with a disability, wherein classifying the query includes processing metadata from the query with contextual information about disability-related functionality enabled on a user's computing device, wherein classifying the query as being associated with a user with a disability includes performing a multi-class classification associated with the query using a classification-based machine learning model.’ Therefore, Applicant respectfully submits that the combination of Nguyen, Burstein, and/or Pillitteri does not render the presently claimed features of amended independent claim 1 obvious under 35 U.S.C. § 103,” (emphasis original).
Applicant’s argument is moot, as new grounds of rejection are made with consideration of U.S. Patent Application Publication 2015/0363476 to Li. Further details are provided below.
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 1-3, 7-10 and 14-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a mental process that includes acts of observation, evaluation and judgement. This judicial exception is not integrated into a practical application because the recited generic computer elements do not add meaningful limitations and amount to simply applying a computer to an abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception because no element of the claims would preclude their performance as a mental process.
Regarding claim 1, the claim recites “A computer-implemented method, executed on a computing device, comprising:processing a query from a user on a website;classifying the query to determine whether the query is associated with a user with a disability, wherein classifying the query includes processing metadata from the query with contextual information about disability-related functionality enabled on a user's computing device, wherein classifying the query as being associated with a user with a disability includes performing a multi-class classification associated with the query using a classification-based machine learning model;identifying a particular customized large language model (LLM) corresponding to a particular class associated with the query from the multi-class classification from a plurality of customized LLMs corresponding to respective classes from the multi-class classification; andin response to classifying the query as being associated with a user with a disability, processing the query using the particular customized LLM corresponding to the particular class associated with the query.”
The limitations of “processing a query from a user on a website,” “classifying the query… processing metadata form the query with contextual information…” “identifying a particular customized large language model…” and “processing the query using the particular customized LLM…” as drafted cover mental activities which can be performed in the mind or with the aid of pen and paper. Taken individually, or as a whole, these limitations describe acts which are equivalent to human mental work of intaking and sorting requests.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recited classification-based machine learning model and large language model may be embodied with generic computing elements, and may be interpreted broadly as subordinate steps of the method that may be carried out by an individual as mental process. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible.
Regarding claim 2, the claim depends from claim 1, and therefore includes the limitations of claim 1, wherein “in response to classifying the query as not being associated with a user with a disability, processing the query using a query processing engine associated with the website.”
Taken individually, or as a whole with claim 1, these limitations describe acts which are equivalent to human mental work of intaking and sorting requests. The claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible.
Regarding claim 3, the claim depends from claim 1, and therefore includes the limitations of claim 1, “wherein classifying the query as being associated with a user with a disability includes comparing one or more portions of the query against a database of phrases associated with disabilities.”
Taken individually, or as a whole with claim 1, these limitations describe acts which are equivalent to human mental work of intaking and sorting requests based on a reference. The claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible.
Regarding claim 7, the claim depends from claim 1, and therefore includes the limitations of claim 1, further comprising “generating a result for the query using the customized LLM; andprocessing feedback from the user concerning the generated result to update the classifying of subsequent queries.”
Taken individually, or as a whole with claim 1, these limitations describe acts which are equivalent to a customer service operator providing a response to the customer, receiving a dissatisfied reply, and altering the routing of the next customer message. Accordingly, the claim is directed to an abstract idea without significantly more. The claim is not patent eligible.
Regarding claims 8-10 and 14, computer-readable medium claims 8-10 and 14 and method claims 1-3 and 7 are related as method and computer-readable medium for performing the same, with each computer-readable medium element’s function corresponding to the method step. Accordingly, claims 8-10 and 14 are similarly rejected under the same rationale as applied to claims 1-3 and 7.
Regarding claims 15-17, system claims 15-17 and method claims 1-3 are related as a method and system of using the same, with each system element’s function corresponding to the method step. Accordingly, claims 15-17 are similarly rejected under the same rationale as applied to claims 1-3.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 1, 3, 8, 10, 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2023/0005070 to Nguyen et al. (hereinafter, "Nguyen") in view of U.S. Patent Application Publication 2022/0142535 to Burstein et al. (hereinafter, "Burstein") and further in view of U.S. Patent Application Publication 2015/0363476 to Li (hereinafter, “Li”).
Regarding claims 1, 8 and 15, Nguyen teaches a method, system and computer readable medium comprising processing a query from a user on a website (paragraph [0028] "A bot service application programming interface (API) may accept a user's question, and may classify the question to identify a high level user intent,” and paragraph [0029], “More specifically, described herein are systems/methods for centralized knowledge-based content and for dynamically transforming the content into platform specific formats to display or render. Such channels may include web, mobile, chat systems, voice systems, and/or other channels.”);classifying the query […], wherein classifying the query […] includes performing a multi-class classification associated with the query using a classification-based machine learning model (paragraph [0051], “In some instances, the bot management platform 102 may parse the query and identify multi-layer classes that may each correspond to a particular model (e.g., expert bot).”);identifying a particular customized large language model (LLM) corresponding to a particular class associated with the query from the multi-class classification from a plurality of customized LLMs corresponding to respective classes from the multi-class classification (paragraph [0028], “The manager bot may route the request to a corresponding endpoint to fulfill the user's request. Skilled bots may accept a user's question, and classify the details, topics, entities, and/or other information to fulfill the user's request.”); andin response to classifying the query […], processing the query using the particular customized LLM corresponding to the particular class associated with the query (paragraph [0028], “The manager bot may route the request to a corresponding endpoint to fulfill the user's request. Skilled bots may accept a user's question, and classify the details, topics, entities, and/or other information to fulfill the user's request.”).
While Nguyen describes performing classification based on metadata (paragraph [0051], “In some instances, the bot management platform 102 may parse the query and identify multi-layer classes that may each correspond to a particular model (e.g., expert bot). For example, the bot management platform 102 may parse a query such as ‘is collision coverage required.’ Rather than having a queued 1:1 response to this question, the bot management platform 102 may classify this text to return a multi-layered category. To do so, in some instances, the bot management platform 102 may select a first machine learning technique to apply to identify a top level category corresponding to the query (e.g., based on accuracy of the corresponding technique in analyzing data corresponding to the given category (e.g., based on a number of utterances corresponding to the query or a portion of the query being used to identify the particular category, an amount of labelled data, an amount of unlabeled data, data type, and/or other reasons)).”), Nguyen does not teach query classification “wherein classifying the query includes processing metadata from the query with contextual information about disability-related functionality enabled on a user's computing device,” and thus, Li is introduced.
Li teaches classifying the query […], wherein classifying the query includes processing metadata from the query with contextual information about […] functionality enabled on a user's computing device (paragraph [0101], "In an embodiment, runtime serving subsystem 714 processes search queries received from a user 736, retrieves search results, and then presents them back to user 736. The runtime serving workflow may include the following stages. A front door 738 receives a user query via a user interface 740 and attaches additional metadata such as the user's market settings, location information, information about the user's platform, e.g., type of device, device capabilities, installed applications, and so forth. Other metadata may also be attached. In an embodiment, front door 738 includes one or more servers that communicate with a client-side application, such as user interface 740. A query processor 742 hosts a pool of query intent classifiers and annotators. The query intent classifiers classify the query received from front door 738 into one or more intent classes, such as navigational, informational, music queries, movie queries, people queries, and so on.").
Nguyen and Li are considered analogous because they are each concerned with classifying user queries. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have included the metadata classifier of Li in the classification process of Nguyen for the purpose of improving classification performance, with the substitution yielding predictable results.
The combination of Nguyen and Li does not explicitly teach classifying user queries for determination of a user with disability, and thus Burstein is introduced. Burstein teaches classifying the query to determine whether the query is associated with a user with a disability, wherein classifying the query as being associated with a user with a disability includes performing a multi-class classification associated with the query using a classification-based machine learning model (paragraph [0147], "Classification module 410A may be configured to classify the [human subject] according to the received data elements (e.g., one or more data elements or features 30A pertaining to behavior and/or [developmental condition]) to one or more groups or classes of [developmental condition]s. For example, one or more groups or classes of [developmental condition]s may pertain to a typical or normal [developmental condition] (e.g., in view of specific one or more profile parameters), and one or more groups or classes may pertain to one or more developmental impediments (e.g., Asperger syndrome, autism, Down syndrome, intellectual disability etc.).").
Nguyen, Li and Burstein are considered analogous because they are each concerned with creating specialized user responses. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have replaced the metadata classifier of Nguyen and Li with the classifier of Burstein for the purpose of serving a particular category of user, with the substitution yielding predictable results.
Regarding claims 3, 10 and 17, the combination of Nguyen and Li does not explicitly teach a method or system “wherein classifying the query as being associated with a user with a disability includes comparing one or more portions of the query against a database of phrases associated with disabilities,” however, Burstein teaches classifying the query as being associated with a user with a disability includes comparing one or more portions of the query against a database of phrases associated with disabilities (paragraph [0130], “According to some embodiments, analysis module 40 may include an expected behavior (EB) module 410C, configured to associate the [human subject] to a group of [human subject]s according to one or more profile parameters (e.g., age, gender, social background, etc.), and provide one or more indications of expected behavior in view of the [human subject]'s group association. EB module 410C may include or may be associated with an EB database 90A that may store one or more entries that associate between at least one group of [human subject]s and one or more indications of expected, typical or normal behavior.”)
Nguyen, Li and Burstein are considered analogous because they are each concerned with creating specialized responses. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Nguyen and Li with the teachings of Burstein for the purpose of improving classifier accuracy.
Claim 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen, Li and Burstein as applied to claims 1, 8 and 15 above, and further in view of U.S. Patent Application Publication 2024/0012860 to Pillitteri et al. (hereinafter, "Pillitteri").
Regarding claims 2, 9 and 16, the combination of Nguyen, Li and Burstein does not explicitly teach a method or system wherein “in response to classifying the query as not being associated with a user with a disability, processing the query using a query processing engine associated with the website,” and thus, Pillitteri is introduced.
Pillitteri teaches in response to classifying the query as not being associated with a user with a disability, processing the query using a query processing engine associated with the website (paragraph [0145], "While this application discusses uses for people with special needs, it will be appreciated that the extended reality education/therapy system could be used in regular education (e.g., language learning, music education, sports, etc.).").
Nguyen, Li, Burstein and Pillitteri are considered analogous because they are each concerned with creating specialized responses. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Nguyen, Li and Burstein with the teachings of Pillitteri for the purpose of serving additional categories of users.
Claim 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen, Li and Burstein as applied to claims 1 and 8 above, and further in view of U.S. Patent Application Publication 2018/0157747 to Tiwary et al. (hereinafter, "Tiwary").
Regarding claims 7 and 14, the combination of Nguyen, Li and Burstein does not explicitly teach a method or system comprising “generating a result for the query using the customized LLM; and processing feedback from the user concerning the generated result to update the classifying of subsequent queries,” and thus, Tiwary is introduced.
Tiwary teaches generating a result for the query using the customized LLM (paragraph [0046], "As such, the systems and method as described herein provide a direct and newly composed answer to the user query instead of copying relevant passages from retrieved search results as performed by previously utilized query search systems."); andprocessing feedback from the user concerning the generated result to update the classifying of subsequent queries (paragraph [0064], "The user feedback 132 is collected and provided to the deep learning algorithms or techniques utilized by query answer system 100 by the feedback system 118. As such, the feedback 132 may be utilized to update or train the deep learning techniques.").
Nguyen, Li, Burstein and Tiwary are considered analogous because they are each concerned with creating specialized responses. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Nguyen, Li and Burstein with the teachings of Tiwary for the purpose of improving language model performance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Patent Application Publication 2021/0158834 to Medan et al.
U.S. Patent Application Publication 2014/0372404 to Zeljkovic et al.
U.S. Patent Application Publication 2019/0222540 to Relangi et al.
U.S. Patent Application Publication 2022/0414741 to Ozcan et al.
U.S. Patent Application Publication 2019/0080225 to Agarwal et al.
U.S. Patent Application Publication 2023/004181 to Wang et al.
U.S. Patent Application Publication 2022/0147544 to Simard et al.
U.S. Patent Application Publication 2022/0415320 to Zheng et al.
U.S. Patent Application Publication 2022/0398477 to Alailima et al.
U.S. Patent Application Publication 2013/0018863 to Regan et al.
U.S. Patent Application Publication 2020/0227026 to Rajagopal et al.
U.S. Patent 11,809,886 to Karashchuk et al.
U.S. Patent 9,251,251 to Mengibar et al.
U.S. Patent 8,700,544 to Sontag et al.
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/SEAN THOMAS SMITH/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
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