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 .
DETAILED ACTION
1. This Office Action is in response to the application filed on 08/27/2025.
Claims 1-15 are pending.
Priority
2. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
3. The information disclosure statement (IDS) filed on 08/27/2025 complies with the provisions of M.P.E.P. 609. The examiner has considered it.
Claim Rejections - 35 USC § 101
4. 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.
5. Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
At Step 1:
Independent claims 1, 14 and 15 are directed to a "system", a “method” and a “program product” and thus directed to a statutory category
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
• " acquire inquiry information relating to an inquiry from a user in a predetermined service" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 2, page 7 and paragraph 2 where one can mentally visualize acquiring inquiry information.
• " acquire classification information relating to a classification that relates to the inquiry and that is defined in advanced in the predetermined service" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 2, page 7 and paragraph 3 where one can mentally visualize acquiring inquiry classification information.
• " input the inquiry information and the classification information to a large language model to acquire a model answer relating to the classification and a certainty degree of the classification which are generated by the large language model" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 2, page 8 and paragraph 1 where one can mentally visualize acquire inputting the inquiry information and the classification information to a large language model to acquire a model answer.
• " control output of the model answer based on the certainty degree" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 2, page 10 and paragraph 2 where one can mentally visualize controlling output of the model answer based on the certainty degree.
At Step 2A, Prong Two:
• The claim recites no additional elements. At most one might consider that a "at least one processor configured to" as claimed might be considered to represent a computer-implemented system and method consistent with Fig. 1 even though the claim does not recite any computer. At most this would be a high-level recitation of a generic computer components and represents mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
• Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
• The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
• Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Dependent Claims 2-13
The limitations as recited in dependent claim 2 recites, “output the model answer to … acquire an additional question to…acquire user answer information relating to …input the user answer information … control … output of the model answer …” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Claim 4 recites, “wherein the acquisition and the output of the additional question… wherein at least one processor is configured to output an answer...” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claim 5 recites, “wherein the at least one processor is configured to determine the predetermined number of times based on the certainty degree...” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claim 6 recites, “wherein the at least one processor is configured to further input a history relating to...” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claim 7 recites, “wherein the classification is a classification relating to …wherein the at least one processor is configured to acquire...” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
The limitations as recited in dependent claim 8 recites, “acquire a consideration item that is to be .. output the consideration item…” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Claim 9 recites, “wherein the classification is a classification of a frequently asked … wherein the at least one processor is configured to acquire…...” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claim 10 recites, “wherein the at least one processor is configured to search an FAQ database that stores FAQ information…...” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
The limitations as recited in dependent claim 11 recites, “determine whether new input is received from the user … restricting the output of the model answer…” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
The limitations as recited in dependent claim 12 recites, “determine whether a period of time is required to acquire the model answer … output, to the user, another asnwer…” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Claim 13 recites, “wherein the at least one processor is configured to cause the large language model to generate the summary…...” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Examiner’s Note
6. Zhuo et al, US 20250156651, [Zhuo: Abstract (“to recommend a clarification based on the cluster result, comprising computing a distance between a cluster and a response of a large language model to the user query where an option list is updated with the clarification where the clarification is based on the cluster and the distance. The embodiment also detects by the Recommendation System a selection in the option list, responsive to the detected selection, generates a prompt based on the selection where the prompt is inputted into the Recommendation System and the large language mode”)] [Zhuo: Paragraph 5 (“provide for Clarification recommendations for a large language model answer with various understandings or multiple subtopics. An embodiment includes detecting, by a Clustering Component of a Recommendation System, a candidate content based on a user query, responsive to the detected candidate content, executing a clustering algorithm on the detected candidate content to output a cluster and a cluster result. The embodiment includes deciding, by a Recommendation Clarification Component of the Recommendation System, to recommend a clarification based on the cluster result, comprising computing a distance between a cluster and a response of a large language model to the user query wherein an option list is updated with the clarification wherein the clarification is based on the cluster and the distance. The embodiment also includes detecting, by the Recommendation System, a selection in the option list, responsive to the detected selection, generating a prompt based on the selection wherein the prompt is inputted into the Recommendation System and the large language model.”)] [Zhuo: Paragraphs 49 and 54 (“Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, reported, and invoiced, providing transparency for both the provider and consumer of the utilized service.” And “the service provider requests payment directly from a customer account at a banking or financial institution”, i.e., ‘predetermined service’)] [Zhuo: Paragraph 66 (“a user query 510 is inputted into a large language model (LLM) 520 and a Recommendation System 580. The Recommendation System comprises a Clustering Component 530, a Recommendation Clarification Component 540, a search engine 550 and a central processing unit (CPU) 570. A user interface output 560 may display the output of the large language model and the Recommendation System. In some embodiments, the LLM and the Recommendation System may execute on the same CPU.”)] [Zhuo: Paragraph 62 (“vectoring or embedding, score the distance to every cluster, by document, paragraph or sentence level respectively. Compare to average distance for every cluster, if less than threshold, means the LLM answer hit the cluster, marked as checked in the option list with cluster label”, i.e., ‘certainty degree’)].
Simons et al, US 20220399086, [Simons: Abstract and paragraph 3 (“classifying and answering medical inquiries based on machine-generated data resources and machine learning models. A CCDA document including clinical information and observations of a patient are received from a requestor and utilized to generate a FHIR model instance specific to the CCDA document. Question text having medical inquires for the patient, and received with the CCDA document, is processed by a machine learning model to determine question categories for the medical inquiries which are utilized to map the inquires to objects of the model instance using another machine learning model. The model instance is queried based on the mapping to return values associated with the inquiries. The values are transmitted back to the requestor”)] [Simons: Paragraphs 7-9 and 21 (“utilize machine learning models for classifying and answering medical inquiries based on machine-generated data resources and machine learning (ML) models. An ML model that is trained on prior medical inquiries is utilized to determine categories for received medical inquiries, e.g., as text question data, from requestors.”)] [Simons: Paragraph 24 (“as well as inquiry classification and responses, i.e., systems for classifying and answering medical inquiries based on machine-generated data resources and machine learning models, e.g., computing devices, of a trading partner(s), a vendor service(s), a doctor or doctor's office (including nurses and/or other staff)”)] [Simons: Paragraph 35 (“An ML category model is configured to determine answers for medical inquiries from the appropriate clinical data source based on the determined category(ies). In embodiments, the ML models herein may determine probabilistic or statistical scores/indices in determining answerability, categories, and/or answers for medical inquiries, and these scores/indices may be subsequently used to identify areas for retraining and/or updating the ML models. In embodiments, scores/indices may be required to meet or exceed a threshold value for a positive determinations, and it is contemplated herein that scores/indices within a pre-determined amount of meeting/exceeding such a threshold may be identified for further refinement by the ML models herein. The data and information determined to answer medical inquiries is provided to the requestor at requestor system 106 over communication link 1”)] [Simons: Paragraphs 41 and 43 (“Subsequently, inquiry classifier and response system 204 may utilize the CCDA document to generate a FHIR instance model corresponding thereto, determine categories for the medical inquiries via a first ML model, map via second ML model the medical inquiries to objects in the FHIR instance model, and query the FHIR instance model to obtain values of the queried-for objects given the mapping. The information in the query result may then be provided back to the appropriate requestor system. As noted above, a third ML model (e.g., trained on a set of prior inquiries for answerability) may be implemented to determine if a given medical inquiry is answerable by the system prior to the first ML model determining the categories. “, i.e., ‘classification information’)] .
Claim Rejections - 35 USC § 103
7. 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 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.
8. 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.
9. Claims 1, 7-8, 11 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Simons et al (US 20220399086), in view of Zhuo et al (US 20250156651).
Claim 1:
Simons suggests an inquiry answering system, comprising at least one processor configured to: acquire inquiry information relating to an inquiry from a user in a predetermined service [Simons: Paragraphs 2-3 (“classifying and answering medical inquiries based on machine-generated data resources and machine learning models,”, i.e., “medical” or “healthcare information” = ‘predetermined service’)]. Simons suggests acquiring classification information relating to a classification that relates to the inquiry and that is defined in advance in the predetermined service [Simons: Paragraphs 41 and 43 (“Subsequently, inquiry classifier and response system 204 may utilize the CCDA document to generate a FHIR instance model corresponding thereto, determine categories for the medical inquiries via a first ML model, map via second ML model the medical inquiries to objects in the FHIR instance model, and query the FHIR instance model to obtain values of the queried-for objects given the mapping. The information in the query result may then be provided back to the appropriate requestor system. As noted above, a third ML model (e.g., trained on a set of prior inquiries for answerability) may be implemented to determine if a given medical inquiry is answerable by the system prior to the first ML model determining the categories. “, i.e., ‘classification information’)]. Simons suggests inputting the inquiry information and the classification information to a large language model to acquire a model answer relating to the classification and a certainty degree of the classification which are generated by the large language model [Simons: Paragraph 35 (“An ML category model is configured to determine answers for medical inquiries from the appropriate clinical data source based on the determined category(ies). In embodiments, the ML models herein may determine probabilistic or statistical scores/indices in determining answerability, categories, and/or answers for medical inquiries, and these scores/indices may be subsequently used to identify areas for retraining and/or updating the ML models. In embodiments, scores/indices may be required to meet or exceed a threshold value for a positive determinations, and it is contemplated herein that scores/indices within a pre-determined amount of meeting/exceeding such a threshold may be identified for further refinement by the ML models herein. The data and information determined to answer medical inquiries is provided to the requestor at requestor system 106 over communication link 1”)]. Simons suggests controlling output of the model answer based on the certainty degree [Simons: Paragraph 35 (“An ML category model is configured to determine answers for medical inquiries from the appropriate clinical data source based on the determined category(ies). In embodiments, the ML models herein may determine probabilistic or statistical scores/indices in determining answerability, categories, and/or answers for medical inquiries, and these scores/indices may be subsequently used to identify areas for retraining and/or updating the ML models. In embodiments, scores/indices may be required to meet or exceed a threshold value for a positive determinations, and it is contemplated herein that scores/indices within a pre-determined amount of meeting/exceeding such a threshold may be identified for further refinement by the ML models herein. The data and information determined to answer medical inquiries is provided to the requestor at requestor system 106 over communication link 1”)].
Zhuo suggests implementing a large language model [Zhuo: Abstract (“to recommend a clarification based on the cluster result, comprising computing a distance between a cluster and a response of a large language model to the user query where an option list is updated with the clarification where the clarification is based on the cluster and the distance. The embodiment also detects by the Recommendation System a selection in the option list, responsive to the detected selection, generates a prompt based on the selection where the prompt is inputted into the Recommendation System and the large language mode”)].
Both references (Simons and Zhuo) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as data processing. It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the teachings of Simons and Zhuo before him/her, to modify the system of Simons with the teaching of Zhuo in order to implement a large language model [Zhuo: Abstract].
Claim 7:
The combined teachings of Simons and Zhuo suggest wherein the classification is a classification relating to a contact point to respond to the inquiry, and wherein the at least one processor is configured to acquire, as the model answer, an answer relating to the contact point corresponding to the classification to which the inquiry belongs [Simons: Paragraph 24 (“The clinical information may include information like patient name, address, title, contact information, age, gender, clinical observations from doctor visits (e.g., weight, temperature, blood pressure, symptoms, diagnoses, and/or the like),”)].
Claim 8:
The combined teachings of Simons and Zhuo suggest wherein the at least one processor is configured to: further acquire a consideration item that is to be considered by the contact point and that is generated by the large language model; and output the consideration item to the contact point [Simons: Paragraph 24 (“systems for classifying and answering medical inquiries based on machine-generated data resources and machine learning models … The clinical information may include information like patient name, address, title, contact information, age, gender, clinical observations from doctor visits (e.g., weight, temperature, blood pressure, symptoms, diagnoses, and/or the like),”)].
Claim 11:
The combined teachings of Simons and Zhuo suggest wherein the at least one processor is configured to: determine whether new input is received from the user during one of the acquisition of the model answer and the certainty degree or the output of the model answer; and restrict the output of the model answer when the at least one processor determines that the new input is received during one of the acquisition or the [Simons: Paragraph 35 (“An ML category model is configured to determine answers for medical inquiries from the appropriate clinical data source based on the determined category(ies). In embodiments, the ML models herein may determine probabilistic or statistical scores/indices in determining answerability, categories, and/or answers for medical inquiries, and these scores/indices may be subsequently used to identify areas for retraining and/or updating the ML models. In embodiments, scores/indices may be required to meet or exceed a threshold value for a positive determinations, and it is contemplated herein that scores/indices within a pre-determined amount of meeting/exceeding such a threshold may be identified for further refinement by the ML models herein. The data and information determined to answer medical inquiries is provided to the requestor at requestor system 106 over communication link 1”)] [Simons: Paragraph 28 (“via a category ML model that maps inquiry/category characteristics to the FHIR model instance, by applying applicable processes to generate new data as determined by the category. The query result may be either raw data or the generated data and is returned to the upstream system to be used to automatically populate the answers to the medical inquiries”)].
Claim 14:
Claim 14 is essentially the same as claim 1 except that it sets forth the claimed invention as a method rather than a system and rejected under the same reasons as applied above.
Claim 15:
Claim 15 is essentially the same as claim 1 except that it sets forth the claimed invention as a program product rather than a system and rejected under the same reasons as applied above.
10. Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Simons et al (US 20220399086), in view of Zhuo et al (US 20250156651), and further in view of Mohri (US 20210365501).
Claim 9:
The combined teachings of Simons, Zhuo and Mohri suggest wherein the classification is a classification of a frequently asked question (FAQ) in the predetermined service, and wherein the at least one processor is configured to acquire, as the model answer, an answer in the FAQ corresponding to the classification to which the inquiry belongs [Mohri: Paragraphs 4, 11, 44 and 45-46 (“As illustrated in FIGS. 5A, 5B, 5C, and 5D, the FAQ database 393 is configured that fields storing the tags associated with the questions and the answer information are associated with each other. As an example, the answer information stored in the FAQ database 393 can each be set as an item for the question that is frequently asked (FAQ). For example, in the case where the sales department is frequently inquired about product specifications, as illustrated in FIG. 5A, the specifications of each of the products are stored in association with each other by product name in a sales department FAQ database 393a. In addition, the sales department FAQ database 393a may store, as the tag information, the words and the phrases associated with each of the products,”)].
Three references (Simons, Zhuo and Mohri) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as data processing. It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the teachings of Simons, Zhuo and Mohri before him/her, to modify the system of Simons and Zhuo with the teaching of Mohri in order to utilize frequently asked questions [Mohri: Paragraphs 4, 11, 44 and 45-46].
Claim 10:
The combined teachings of Simons, Zhuo and Mohri suggest wherein the at least one processor is configured to search an FAQ database that stores FAQ information relating to the FAQ based on the inquiry information, to thereby acquire the classification information [Mohri: Paragraphs 4, 11, 44 and 45-46 (“As illustrated in FIGS. 5A, 5B, 5C, and 5D, the FAQ database 393 is configured that fields storing the tags associated with the questions and the answer information are associated with each other. As an example, the answer information stored in the FAQ database 393 can each be set as an item for the question that is frequently asked (FAQ). For example, in the case where the sales department is frequently inquired about product specifications, as illustrated in FIG. 5A, the specifications of each of the products are stored in association with each other by product name in a sales department FAQ database 393a. In addition, the sales department FAQ database 393a may store, as the tag information, the words and the phrases associated with each of the products,”)].
Three references (Simons, Zhuo and Mohri) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as data processing. It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the teachings of Simons, Zhuo and Mohri before him/her, to modify the system of Simons and Zhuo with the teaching of Mohri in order to utilize frequently asked questions [Mohri: Paragraphs 4, 11, 44 and 45-46].
Allowable Subject Matter
11. Claims 2-6 and 12-13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to [Hung D. Le], whose telephone number is [571-270-1404]. The examiner can normally be communicated on [Monday to Friday: 9:00 A.M. to 5:00 P.M.].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on [571-272-4080]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Hung Le
07/13/2026
/HUNG D LE/Primary Examiner, Art Unit 2161
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