DETAILED ACTION
Remarks
The instant application having Application Number 18/606,237 filed on March 15, 2024 has a total of 20 claims pending in the application; there are 3 independent claims and 17 dependent claims, all of which are presented for examination by the examiner.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
The examiner requests, in response to this Office action, supports are shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Claim Objections
Claims 10 and 20 objected to because of the following informalities:
Claim 10, line 3 recites “the method comprising:”, please change to “comprising:”.
Claim 20, line 1 recites “The system of claim 1” please change to “The system of claim 19”
Appropriate correction is required.
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 an abstract idea without significantly more.
Regarding independent Claims 1, 11, and 20:
Step 1 Analysis:
Claim 1 recites “A method…”, the claim recites a series of steps and therefore is process.
Claim 10 recites “A computer readable storage medium”, therefore the claim is a manufacture.
Claim 19 recites “A system …”; therefore, the claim is a machine.
Step 2A Prong One Analysis: The claim, under the broadest reasonable interpretation, recites limitations directed to an abstract idea, including mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion), but for the recitation of mere instructions to apply an exception language. In particular, the following limitations are directed to an abstract idea:
detecting a change of state by an automated agent during a conversation;
accessing conversational context, external world state representation, and current agent operations data associated with the conversation;
generating a content sketch based on the accessed conversational context, external world state representation, and current agent operations data, the content sketch generated using a first machine learning model, the content sketch including a set of one or more programs; and
preparing a response based at least in part on the content sketch and a first set of instructions, the first set of instructions identified based on the content sketch and the change of state.
This limitation is a process that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a " “memory”, “processor”, “computer storage medium”, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, the “detecting”, “accessing” “generating” and “preparing” in the context of this claim encompasses a user mentally, and with the aid of pen and paper writing the changes down on a sheet of paper and examine the list to determine the relevant ones (rationale).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A - Prong Two: Integrated into a Practical Application
The judicial exception is not integrated into a practical application. In particular, the additional steps: the “detecting”, “accessing” “generating” and “preparing” steps mount to data gathering which are considered to be insignificant extra-solution activity (see MPEP 2106.05(g)), and the “generating” and “preparing” step is considered as a mere instruction to apply an exception to perform an existing process on a generic computer and/or no more than an idea of a solution or outcome on a generic computer (see MPEP 2106.05(f)). 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, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Step 2B: Claim provides an Inventive Concept
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities identified above, which include the data-gathering and the step of “detecting”, “accessing” “generating” and “preparing” are recognized by the courts as well-understood, routine, and conventional activities 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(d)(II)). For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
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.
Accordingly, claim 1 is directed to an abstract idea.
Independent claims 10 and 19 have the similar limitations as claim 1 and are rejected for at least the same reasons as claim 1.
Regarding claim 2. The method of claim 1, further including applying a program constraint to the content sketch before the response is prepared, the program constraint associated with at least one program in a set of one or more programs included in the content sketch.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 3. The method of claim 1, further comprising: accessing instructions from an instruction bank; and selecting a set of relevant instructions as a sub-set of the instructions accessed from the instruction bank, the set of relevant instructions selected at least in part on the content sketch, the set of relevant instructions being relevant to preparing a response to the communication.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 4. The method of claim 1, wherein identified instructions are selected as a subset of relevant instructions from a second larger set of instructions.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 5. The method of claim 1, wherein the content sketch provides a constraint that the response does not include freely generated text.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 6. The method of claim 1, wherein the content sketch allows for generation of free text to provide an explanation for the response.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 7. The method of claim 1, wherein the response is generated by a large language machine, wherein the prompt for the large language machine is based at least in part on the content sketch, the relevant instructions, and conversational context, external world state representation, and current agent operations data.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 8. The method of claim 1, wherein the change of state includes receipt of a message from a client.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 9. The method of claim 1, wherein the change of state includes a development while executing a program related to a client request.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
With respect to claims 11-18, although claims 11-18 directed to a medium, they are similar in scope to claims 2-9. Similar to claims 2-9, the claims 11-18 do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified.
With respect to claim 20, although claim 20 directed to a system, they are similar in scope to claim 2. Similar to claim 2, the claim 20 do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified.
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cabrera-Cordon et al. (US Patent Publication No. 2018/0077088 A1, ‘Cabreara’, hereafter) in view of Bailey et al. (US Patent Publication No. 2025/0068881 A1, ‘Bailey’, hereafter).
Regarding claim 1. Cabreara teaches a method for generating a response to a change of state using structured based constraints, comprising:
detecting a change of state by an automated agent during a conversation (the user provides a request, which the personalized automated agent receives and analyzes. A determination may be made that the user's intent is a request for information about text analytics based on natural language processing and recognition of keywords or related keywords. A query may be made on the knowledge database for documentation that may resolve the user's request (i.e., change of state by an automated agent during a conversation). … detecting a negative sentiment, the personalized automated agent 116 escalates the user's request to a higher urgency level to provide a more appropriate response or solution (i.e., change of state by an automated agent during a conversation), Cabreara [0036-0039] and Figs. 2-3);
accessing conversational context, external world state representation, and current agent operations data associated with the conversation (a link to a document is provided to the user in a first response. In a subsequent response, the personalized automated agent requests feedback from the user by asking if the provided documentation link is helpful. The user provides feedback that is received by the personalized automated agent and analyzed. In one aspect, the feedback is analyzed by the sentiment analyzer for determining a level of frustration of the user (i.e., conversation context). … the personalized automated agent queries the knowledge database for a person to route the request to or to suggest to the user to contact for the help that the user needs, and provides the contact information of the person to the user (i.e., agent operations data associated with the conversation). For example, the higher urgency level response (e.g., providing another expert's email contact information (i.e., external world state representation)) is a more costly approach than sending the user documentation because it may lead to an interruption for the agent owner, and is thus potentially more costly to the company than simply sending a document, Cabreara [0036-0039] and Figs. 2-3);
generating a content sketch based on the accessed conversational context, external world state representation, and current agent operations data (Cabreara [0036-0039] and Figs. 2-3 discloses conversation between the automated agent and the user to resolve an issue which requires attention. To do that automated agent send a link to the user to a knowledge database for collecting data to resolve the issue. If not, then automated agent prepares the user to have a person to person conversation. Therefore, content sketch based on accessed conversational context, external world state representation, and current agent operations data), the content sketch generated using a first machine learning model, the content sketch including a set of one or more programs (components of the automated agent system, and various use case examples with respect to FIGS. 1-3, FIG. 4A is a flow chart showing general stages involved in an example method for generating a knowledge database for providing personalized automated assistance. With reference now to FIG. 4A, the method 400 begins at START OPERATION 402, and proceeds to OPERATION 404, where the text mining and machine learning engine mines the agent owner's context (e.g., email, calendar, organizational chart, documentation database (i.e., content sketch)), and utilizes machine learning techniques for information for generating a knowledge database personalized to the agent owner. For example, the text mining and machine learning engine analyzes various data collections associated with the agent owner, Cabreara [0040]); and
Cabreara does not teaches
preparing a response based at least in part on the content sketch and a first set of instructions, the first set of instructions identified based on the content sketch and the change of state.
However, Bailey teaches
preparing a response based at least in part on the content sketch and a first set of instructions, the first set of instructions identified based on the content sketch and the change of state (the computer system or server collects data from various sources, including video conferencing services, instant messages, and email messages. To detect context of user's communication and topics of user's request in the chatbot prompt, the computer system or server will use the collected data from the various sources (i.e., content sketch), Bailey [0030]. The computer system or server then identifies an intent of the chatbot prompt, by using natural language processing to disambiguate the chatbot prompt … improves quality of the chatbot prompt and provides a modified chatbot prompt, according to the context and the intent (i.e., change of state) … selects datasets relevant to the context of the user's chatbot prompt … generates a response to the chatbot prompt, based on the modified chatbot prompt and datasets. Therefore, the context-aware chatbot responds to the user's chatbot prompt with the generated response which includes the most relevant and accurate information (i.e., preparing a response based at least in part on the content sketch and a first set of instructions, the first set of instructions identified based on the content sketch and the change of state), Bailey [0033-0037] and Fig. 2).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention was made having the teachings of Cabreara and Bailey before him/her, to modify Cabreara with the teaching of Bailey’s training a context-aware chatbot. One would have been motivated to do so for the benefit of training a context-aware chatbot in order to improve the quality of chatbot responses that can lead to a better user experience and time spent searching for the right information (Bailey, Abstract, [0002]).
Regarding claim 2. Cabreara as modified teaches, further including applying a program constraint to the content sketch before the response is prepared, the program constraint associated with at least one program in a set of one or more programs included in the content sketch (Bailey [0023-0025], [0030]).
Regarding claim 3. Cabreara as modified teaches, further comprising:
accessing instructions from an instruction bank (Bailey [0005], [0011-0014], [0046]); and
selecting a set of relevant instructions as a sub-set of the instructions accessed from the instruction bank, the set of relevant instructions selected at least in part on the content sketch, the set of relevant instructions being relevant to preparing a response to the communication (Bailey [0005], [0011-0014], [0046]).
Regarding claim 4. Cabreara as modified teaches, wherein identified instructions are selected as a subset of relevant instructions from a second larger set of instructions (Bailey [0036]).
Regarding claim 5. Cabreara as modified teaches, wherein the content sketch provides a constraint that the response does not include freely generated text (Cabreara [0036-0040] and Figs. 2-3).
Regarding claim 6. Cabreara as modified teaches, wherein the content sketch allows for generation of free text to provide an explanation for the response (Cabreara [0037] and Fig. 2).
Regarding claim 7. Cabreara as modified teaches, wherein the response is generated by a large language machine, wherein the prompt for the large language machine is based at least in part on the content sketch, the relevant instructions, and conversational context, external world state representation, and current agent operations data (Bailey [0023-0026], [0030], [0033], Fig. 2).
Regarding claim 8. Cabreara as modified teaches, wherein the change of state includes receipt of a message from a client (Cabreara [0036-0039]).
Regarding claim 9. Cabreara as modified teaches, wherein the change of state includes a development while executing a program related to a client request (Cabreara [0036-0039]).
Regarding claim 10. Cabreara teaches a non-transitory computer readable storage medium having embodied thereon a program, the program being executable by a processor to generating a response to a change of state using structured based constraints (system/computing device includes at least one processing unit 502 and a system memory, including computer readable instructions, which when executed by the processing unit, Cabreara [0050-0051]. Computer storage media include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, Cabreara [0054]), the method comprising:
although claim 10 directed to a medium, it is similar in scope to claim 1. The method steps of claim 1 substantially encompass the medium recited in claim 10. Therefore; claim 10 is rejected for at least the same reason as claim 1 above.
Regarding claims 11-18, the method steps of claims 2-9 substantially encompass the system recited in claims 11-18. Therefore, claims 11-18 are rejected for at least the same reason as claims 2-9 above.
Regarding claim 19. Cabreara teaches a system for generating a response to a change of state using structured based constraints, comprising: one or more servers, wherein each server includes a memory and a processor; and one or more modules stored in the memory and executed by at least one of the one or more processors (system/computing device includes at least one processing unit 502 and a system memory, including computer readable instructions, which when executed by the processing unit, Cabreara [0050-0051]. Computer storage media include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, Cabreara [0054]) to
although claim 19 directed to a system, it is similar in scope to claim 1. The method steps of claim 1 substantially encompass the system recited in claim 19. Therefore; claim 19 is rejected for at least the same reason as claim 1 above.
Regarding claim 20, the method steps of claim 2 substantially encompass the system recited in claim 20. Therefore, claim 20 is rejected for at least the same reason as claim 2 above.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant’s disclosure.
Rahmani et al. (US Patent Publication No. 2023/0176829 A1) discloses a multi-modal approach to generate software programs that match a solution program description. The solution program description may include natural language, input-output examples, partial source code, desired operators, or other hints. Some embodiments use optimized prompts to a pre-trained language model to obtain initial candidate programs. Maximal program components are extracted and then recombined variously using component-based synthesis. Beam search reduces a solution program search space by discarding some candidates from a given synthesis iteration. Relevance metrics, string similarity metrics, operator frequency distributions, token rareness scores, and other optimizations may be employed. By virtue of optimizations and the multi-modal approach, a solution program may be obtained after fewer iterations than by use of a language model alone. The multi-modal approach is domain agnostic, as illustrated by examples using regular expression and cascading style sheet selector domain specific languages.
Galitsky (US Patent Publication No. 2021/0191988 A1) discloses systems, devices, and methods discussed herein provide improved autonomous agent applications that are configured to generate automated answers to a question using summarized logical forms (SLFs). A myriad of techniques may be utilized to manually or automatically generate one or more summarized logical forms for an answer, where the summarized logical form(s) identifies the main entities/informative portions of the answer. Instead of indexing the whole of the answer as in conventional methods, an answer can be indexed using the summarized logical forms. A subsequent query may be matched to the SLF and the answer may be provided in response to the question. By indexing the answer with its informative portions, the speed and accuracy of identifying the answer is improved.
Stoops et al. (US Patent Publication No. 2022/0366427 A1) discloses a method of training an artificial intelligence system to handle long-tail interactions according to an embodiment includes receiving a user question from a user, analyzing the user question with a natural language understanding engine to determine whether an intent of the user question matches an answer in an answer knowledgebase of the system, transferring at least the user question of the interaction to a primary subject matter expert in response to determining that the intent of the user question does not match an answer in the answer knowledgebase, receiving an expert answer to the user question from the primary subject matter expert, transferring an interaction package including the user question and the expert answer to at least one evaluator for validation, and automatically training the natural language understanding engine based on the user question and the expert answer in response to successful validation of the expert answer.
Morris et al. (US Patent Publication No. 2011/0252108 A1) discloses a system is described in which a user can add one or more automated agents as "friends" in a social network service. In operation, an automated agent observes an information need expressed by the user via the social network service, e.g., in the form of an original message posted to the social network service; determines whether it is appropriate to reply to the information need; uses automated functionality to generate a reply message to the information need (if it is deemed appropriate to reply to the information need); and sends the reply message to a target destination, such as a social network page associated with the user. For example, without limitation, one type of automated agent performs a question-answering function. Another type of automated agent performs a social referral service.
Rusak et al. (US Patent Publication No. 2019/0103092 A1) discloses a method for a dialogue system includes establishing a dialogue session between an application executing on a server and a remote machine. The dialogue session includes one or more utterances received from a user at the remote machine. A natural language processing machine identifies a request associated with a computer-readable representation of an utterance. A dialogue expansion machine generates a plurality of alternative actions for responding to the request. A previously-trained machine learning confidence model assesses a confidence score for each alternative. If a highest confidence score for a top alternative does not satisfy a threshold, the plurality of alternatives including the top alternative are transmitted to a remote machine (which may be the same remote machine or a different remote machine) for review by a human reviewer. After the dialogue system and/or the human reviewer select an alternative, computer-readable instructions defining the selected alternative are executed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HASANUL MOBIN whose telephone number is (571)270-1289. The examiner can normally be reached on 9AM to 6:00PM EST M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached at 571-272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HASANUL MOBIN/
Primary Examiner, Art Unit 2168
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