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 .
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.
101 Subject Matter Eligibility Analysis
Step 1: Claims 1-20 are within the four statutory categories (a process, machine, manufacture or composition of matter).
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
None of the claims represent an improvement to technology.
Claims 1-8 are directed to a method consisting of a series of steps, meaning that it is directed to the statutory category of process. Claims 9-20 are directed to storage mediums and processors which are machines.
Regarding claim 1, the following claim elements are abstract ideas:
responsive to an occurrence of a user question, selecting a large language model interfacing technique by (This is an abstract idea of a mental process. It involves receiving a question, evaluating the subject matter of the question, and determining an appropriate technique for interacting with a large language model based on that evaluation. For example, a person could read a question, assess its topic or characteristics, and decide whether a particular approach, method, or procedure would be most suitable for responding to the question. Such observation, evaluation, and decision-making can practically be performed in the human mind or with pen and paper, and thus constitutes an abstract idea of a mental process.),
calculating a subject embedding vector based on at least a portion of the user question (This is an abstract idea of a mental process and mathematical concept. The limitation recites assigning numerical values to words or concepts in a user question and performing calculations to generate a numerical representation of the subject matter of the question. For example, a person could review a question, identify relevant terms, assign numerical values to those terms, and perform arithmetic operations to generate a numerical representation. Since it involves mathematical calculations and can practically be performed in the human mind or with basic computational tools, it falls within the mathematical concepts and mental process grouping of abstract ideas.);
comparing the subject embedding vector to one or more sample question embedding vectors to select a candidate one of the sample question embedding vectors that is similar to the subject embedding vector (This is an abstract idea of a mental process. The limitation recites comparing information associated with a user question to previously known examples and selecting the example that is most similar. For example, a person could review the subject matter of a question, compare it to a set of previously encountered questions, and determine which prior question is most similar based on observation and judgement. Since it involves evaluation, comparison, and selection that can be carried out in the human mind, it falls within the mental process grouping of abstract ideas.);
determining a classification of the subject embedding vector (This is an abstract idea of a mental process. The limitation recites categorizing information into a classification based on its characteristics. For example, a person could review the subject matter represented by a question, compare it to known categories, and determine which category best corresponds to the question. Since it involves evaluation, judgement, and classification that can be carried out in the human mind or with the aid of pen and paper, it falls within the mental process grouping of abstract ideas.); and
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
invoking at least one computing agent based at least in part on the classification (This limitation constitutes mere instructions to apply the abstract idea and insignificant extra-solution activity. See MPEP 2106.05(f) and 2106.05(g).).
Regarding claim 2, the rejection of claim 1 is incorporated herein. Further, claim 2 recites the following abstract ideas:
populating a dataset of sample question embedding vectors, wherein a particular one from a set of candidate sample question embedding vectors is associated with a corresponding classification (This an abstract idea of a mental process. The limitation recites organizing information into a dataset and associating examples with corresponding classifications. For example, a person could review a collection of sample questions, determine an appropriate category for each question, and organize the questions into a list along with their corresponding classifications. Since it involves evaluation, categorization, and organization of information that can be carried out in the human mind or with aid of pen and paper, it falls within the mental process grouping of abstract ideas.).
Regarding claim 3, the rejection of claim 1 is incorporated herein. Further, claim 3 recites the following additional elements, which taken alone or in combination with other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
gathering an output from the at least one computing agent and providing at least a portion of the output from the at least one computing agent to a large language model system (This limitation amounts to insignificant extra-solution activity because it merely gathers and provides information for use by another component. Such activity does not impose a meaningful limit on the judicial exception.).
Regarding claim 4, the rejection of claim 1 is incorporated herein. Further, claim 4 recites the following abstract ideas:
selecting a particular instance of a large language model system taken from a plurality of large language model system instances (This is an abstract idea of a mental process. The limitation recites selecting one option from a plurality of available options based on evaluation and judgement. For example, a person could review a plurality of available language model instances, consider their respective characteristics, and select a particular instance that is appropriate for a given question or task. Since it involves evaluation, judgement, and decision-making that can be carried out in the human mind, it falls within the mental process grouping of abstract ideas.).
Regarding claim 5, the rejection of claim 4 is incorporated herein. Further, claim 5 recites the following abstract ideas:
selecting the particular instance of the large language model system based at least in part on interfacing requirements of the particular instance of the large language model system (This is an abstract idea of a mental process. The limitation recites evaluating requirements associated with available options and selecting an option based on those requirements. For example, a person could review the requirements of a plurality of available language model instances, determine which instance satisfies the desired requirements, and select that instance for use. Since it involves evaluation, judgement, and decision-making that can be carried out in the human mind or with the aid of pen and paper, it falls within the mental process grouping of abstract ideas.).
Regarding claim 6, the rejection of claim 1 is incorporated herein. Further, claim 6 recites the following additional elements, which taken alone or in combination with other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the at least one computing agent of a content management system interfaces with an application programming interface of a large language model system (This limitation amounts to insignificant extra-solution activity because it merely specifies communication between components through an application programming interface. Such activity does not impose a meaningful limit on the judicial exception.).
Regarding claim 7, the rejection of claim 6 is incorporated herein. Further, claim 7 recites the following additional elements, which taken alone or in combination with other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
storing at least one embedding vector in a local embedding storage of a content management system (The step of “storing” an embedding vector in a storage location is merely generic data storage operation that amounts to storing and retrieving information in memory, which has been recognized by the courts as well-understood, routine, and conventional computer activity.).
Regarding claim 8, the rejection of claim 7 is incorporated herein. Further, claim 8 recites the following additional elements, which taken alone or in combination with other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the large language model system is in a first domain having a first security perimeter and wherein the content management system is in a second domain having a second security perimeter (This limitation amounts to insignificant extra-solution activity because it merely specifies the environment in which the abstract idea is performed. The recitation of a first domain having a first security perimeter and a second domain having a second security perimeter does not impose a meaningful limit on the judicial exception and merely describes the context in which the information processing occurs.).
Regarding claim 9, the following claim elements are abstract ideas:
responsive to an occurrence of a user question, selecting a large language model interfacing technique by (This is an abstract idea of a mental process. It involves receiving a question, evaluating the subject matter of the question, and determining an appropriate technique for interacting with a large language model based on that evaluation. For example, a person could read a question, assess its topic or characteristics, and decide whether a particular approach, method, or procedure would be most suitable for responding to the question. Such observation, evaluation, and decision-making can practically be performed in the human mind or with pen and paper, and thus constitutes an abstract idea of a mental process.),
calculating a subject embedding vector based on at least a portion of the user question (This is an abstract idea of a mental process and mathematical concept. The limitation recites assigning numerical values to words or concepts in a user question and performing calculations to generate a numerical representation of the subject matter of the question. For example, a person could review a question, identify relevant terms, assign numerical values to those terms, and perform arithmetic operations to generate a numerical representation. Since it involves mathematical calculations and can practically be performed in the human mind or with basic computational tools, it falls within the mathematical concepts and mental process grouping of abstract ideas.);
comparing the subject embedding vector to one or more sample question embedding vectors to select a candidate one of the sample question embedding vectors that is similar to the subject embedding vector (This is an abstract idea of a mental process. The limitation recites comparing information associated with a user question to previously known examples and selecting the example that is most similar. For example, a person could review the subject matter of a question, compare it to a set of previously encountered questions, and determine which prior question is most similar based on observation and judgement. Since it involves evaluation, comparison, and selection that can be carried out in the human mind, it falls within the mental process grouping of abstract ideas.);
determining a classification of the subject embedding vector (This is an abstract idea of a mental process. The limitation recites categorizing information into a classification based on its characteristics. For example, a person could review the subject matter represented by a question, compare it to known categories, and determine which category best corresponds to the question. Since it involves evaluation, judgement, and classification that can be carried out in the human mind or with the aid of pen and paper, it falls within the mental process grouping of abstract ideas.); and
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A non-transitory computer readable medium… one or more processors..(This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).).
invoking at least one computing agent based at least in part on the classification (This limitation constitutes mere instructions to apply the abstract idea and insignificant extra-solution activity. See MPEP 2106.05(f) and 2106.05(g).).
Regarding claim 10, the rejection of claim 9 is incorporated herein. The claim recites similar limitations corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 10.
Therefore, claim 10 is ineligible.
Regarding claim 11, the rejection of claim 9 is incorporated herein. The claim recites similar limitations corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 11.
Therefore, claim 11 is ineligible.
Regarding claim 12, the rejection of claim 9 is incorporated herein. The claim recites similar limitations corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 12.
Therefore, claim 12 is ineligible.
Regarding claim 13, the rejection of claim 12 is incorporated herein. The claim recites similar limitations corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 13.
Therefore, claim 13 is ineligible.
Regarding claim 14, the rejection of claim 9 is incorporated herein. The claim recites similar limitations corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 14.
Therefore, claim 14 is ineligible.
Regarding claim 15, the rejection of claim 14 is incorporated herein. The claim recites similar limitations corresponding to claim 7. Therefore, the same subject matter analysis that was utilized for claim 7, as described above, is equally applicable to claim 15.
Therefore, claim 15 is ineligible.
Regarding claim 16, the rejection of claim 15 is incorporated herein. The claim recites similar limitations corresponding to claim 8. Therefore, the same subject matter analysis that was utilized for claim 8, as described above, is equally applicable to claim 16.
Therefore, claim 16 is ineligible.
Regarding claim 17, the following claim elements are abstract ideas:
responsive to an occurrence of a user question, selecting a large language model interfacing technique by (This is an abstract idea of a mental process. It involves receiving a question, evaluating the subject matter of the question, and determining an appropriate technique for interacting with a large language model based on that evaluation. For example, a person could read a question, assess its topic or characteristics, and decide whether a particular approach, method, or procedure would be most suitable for responding to the question. Such observation, evaluation, and decision-making can practically be performed in the human mind or with pen and paper, and thus constitutes an abstract idea of a mental process.),
calculating a subject embedding vector based on at least a portion of the user question (This is an abstract idea of a mental process and mathematical concept. The limitation recites assigning numerical values to words or concepts in a user question and performing calculations to generate a numerical representation of the subject matter of the question. For example, a person could review a question, identify relevant terms, assign numerical values to those terms, and perform arithmetic operations to generate a numerical representation. Since it involves mathematical calculations and can practically be performed in the human mind or with basic computational tools, it falls within the mathematical concepts and mental process grouping of abstract ideas.);
comparing the subject embedding vector to one or more sample question embedding vectors to select a candidate one of the sample question embedding vectors that is similar to the subject embedding vector (This is an abstract idea of a mental process. The limitation recites comparing information associated with a user question to previously known examples and selecting the example that is most similar. For example, a person could review the subject matter of a question, compare it to a set of previously encountered questions, and determine which prior question is most similar based on observation and judgement. Since it involves evaluation, comparison, and selection that can be carried out in the human mind, it falls within the mental process grouping of abstract ideas.);
determining a classification of the subject embedding vector (This is an abstract idea of a mental process. The limitation recites categorizing information into a classification based on its characteristics. For example, a person could review the subject matter represented by a question, compare it to known categories, and determine which category best corresponds to the question. Since it involves evaluation, judgement, and classification that can be carried out in the human mind or with the aid of pen and paper, it falls within the mental process grouping of abstract ideas.); and
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
a storage medium (This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).).
one or more processors (This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).).
invoking at least one computing agent based at least in part on the classification (This limitation constitutes mere instructions to apply the abstract idea and insignificant extra-solution activity. See MPEP 2106.05(f) and 2106.05(g).).
Regarding claim 18, the rejection of claim 17 is incorporated herein. The claim recites similar limitations corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 18.
Therefore, claim 18 is ineligible.
Regarding claim 19, the rejection of claim 17 is incorporated herein. The claim recites similar limitations corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 19.
Therefore, claim 19 is ineligible.
Regarding claim 20, the rejection of claim 17 is incorporated herein. The claim recites similar limitations corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 20.
Therefore, claim 20 is ineligible.
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 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.
Claims 1-20 are rejected under the 35 U.S.C. 103 as being unpatentable over Pei et al., (US 20240086757 A1 (Filed: 2022) in view of Magliozzi et al., (US 20180131645 A1 (Filed: 2017)).
Regarding claim 1, Pei teaches the following limitations:
A method for selecting a large language model interfacing technique, the method comprising: responsive to an occurrence of a user question, selecting a large language model interfacing technique by (Pei, paragraph [0021] “Upon extracting text and determining client features, the intent classification system utilizes a machine learning model to generate predicted multi-level client intent classifications. For example, in one or more embodiments, the intent classification system utilizes a BERT or DistilBERT machine learning model. To illustrate, the intent classification system can utilize a machine learning model with a transformer encoder and one or more classification layers.” [0022] “the intent classification system utilizes the predicted multi-level client intent classifications and corresponding multi-level client intent classification probabilities to perform a variety of automatic processes. For example, the intent classification system generates an automated reply to the communication, takes an action associated with a selected multi-level client intent classification, or routes communications to a particular client device (e.g., a specialized agent device). “ – Pei teaches responsive to an occurrence of a user question because the system processes user communications using a BERT/DistlBERT transformer model with a transformer encoder and classification layers. Pei further teaches selecting a large language model interfacing technique because the system uses the generated classification to select downstream processing operations, including generated automated replies, taking actions, or routing communications to specialized agent devices. Under the broadest reasonable interpretation, selecting among downstream conversational processing operations based on transformer-generated classifications teaches selecting a large language model interfacing technique.),
calculating a subject embedding vector based on at least a portion of the user question (Pei, paragraph [0019] “the intent classification system can extract text from various fields in a communication (e.g., digital email or text) and identify an associated client device or user account.” [0048] “the intent classification system 102 can extract the communication features 206 including the communication text 208. To illustrate, in one or more embodiments, the intent classification system 102 retrieves text from the body of a communication and any other text associated with the communication.“ [0052] “ the intent classification system 102 can encode the client communication features 206 (e.g., using one hot encoding, an encoding layer, or a vector mapping) and then process the encoding utilizing the machine learning model 212.” [0053] “the machine learning model 212 is a natural language processing model, such as a transformer model. For instance, in one or more embodiments the intent classification system 102 utilizes BERT or DistilBERT machine learning model architectures…the intent classification system 102 embeds the communication features 206 and inputs the embedded communication features into a transformer encoder of the machine learning model 212.” – Pei extracts communication text from a user communication and encodes the extracted communication features using an encoding layer or vector mapping prior to processing by a transformer-based machine learning model. Pei further teaches using BERT of DistiBERT architectures and embedding vector generated from textual input, as vector mappings and transformer encoders are standard mechanisms for generating embedding vectors in natural-language processing systems.);
invoking at least one computing agent based at least in part on the classification (Pei, paragraph [0022] “the intent classification system utilizes the predicted multi-level client intent classifications and corresponding multi-level client intent classification probabilities to perform a variety of automatic processes. For example, the intent classification system generates an automated reply to the communication, takes an action associated with a selected multi-level client intent classification, or routes communications to a particular client device (e.g., a specialized agent device).” [0054] “In one or more embodiments, the intent classification system 102 determines that the top predicted multi-level client intent classification is associated with a multi-level client intent classification probability that satisfies the intent classification threshold. Based on this determination, the intent classification system 102 can take an action associated with the multi-level client intent classification.” – Pei teaches utilizing predicted multi-level client intent classifications to perform downstream automatic processes, generate automated replies, take actions associated with selected classifications, and route communications to a specialized agent device. Under BRI, performing automatic processes or routing communications to a specialized agent device based on a determined classification corresponds to invoking at least one computing agent based at least in part on the classification.).
However, Pei does not teach but Pei in view of Magliozzi teaches the following limitations:
comparing the subject embedding vector to one or more sample question embedding vectors to select a candidate one of the sample question embedding vectors that is similar to the subject embedding vector (Magliozzi, paragraph [0044] “The data in the pre-existing documents and/or webpages can be in structured format or in unstructured format. For example, a pre-existing document including Frequently Asked Questions (FAQs) and corresponding answers can be made available to the chatbot. The processor can scan the FAQs and extract question-answer pairs from the FAQs. In another example, raw text (e.g., raw HTML) can be extracted from a webpage.” [0124] “The neural network 600 encodes an incoming query as well as the data in the knowledge base… the neural network 600 can encode two natural language messages into corresponding vectors, thereby transforming them into respective mathematical representations. The neural network 600 can then determine the semantic similarity between the two natural language sentences by determining the angle between their vector representations.” [0125] “the NLP server may compare the query vector to the category vector as well as compare the query vector to individual utterance vectors within categories in the encoded knowledge base during the process of classifying the intent of the message.” – Magliozzi teaches comparing the subject embedding vector to one or more sample question embedding vectors because the neural network encodes an incoming query and data stored in a knowledge base, including question question-answer pairs extracted from FAQs, into vector representations. Magliozzi further teaches comparing the query vector to individual utterance vectors within categories in the encoded knowledge base and determining semantic similarity between the vector representations by determining the angle between the vectors. Under BRI, the encoded stored questions correspond to sample question embedding vectors and the comparison of the query vector to the stored question vectors identifies a candidate question vector that is similar to the subject embedding vector.);
determining a classification of the subject embedding vector (Magliozzi, paragraph [0124] “In another example, the neural network 600 analyzes a question from the user, such as “What is FAFSA?” The NLP server understands the intent of this communication and categorizes this communication as a question related to the Free Application for Federal Student Aid (FAFSA).” [0125] “It is also possible to create category vectors as well and compare the mathematical properties of a category vector with the mathematical properties of the query vector to see if the statement semantically belongs in that category. For instance, the NLP server may compare the query vector to the category vector as well as compare the query vector to individual utterance vectors within categories in the encoded knowledge base during the process of classifying the intent of the message.” – Magliozzi teaches determining a classification of the subject embedding vector because the NLP server compares the query vector to category vectors and individual utterance vectors within categories in the encoded knowledge base during classification of message intent. Magliozzi further teaches determining whether the query semantically belongs in a category by comparing mathematical properties of the query vector and category vector. Under BRI, the category corresponds to a classification, and determining that the query vector semantically belongs in the category corresponds to determining a classification of the subject embedding vector.); and
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having Pei and Magliozzi before them, to incorporate the vector-based query comparison and category classification techniques of Magliozzi into the intent classification system of Pei. One would have been motivated to make such a combination in order to compare an incoming user question to a previously stored questions represented as vectors, determine a classification based on semantic similarity between the user question and the stored questions, and utilize the resulting classification to select downstream processing actions. This would allow user questions to be matched against stored questions content within a knowledge base and classified according to semantically similar categories, thereby improving identification of user intent and enabling selection of appropriate automated actions associated with the determined classification.
Regarding claim 2, Pei in view of Magliozzi teaches all the elements of claim 1, therefore is rejected for the same reasons as those presented for claim 1. Pei in view of Magliozzi further teaches:
populating a dataset of sample question embedding vectors, wherein a particular one from a set of candidate sample question embedding vectors is associated with a corresponding classification (Magliozzi, paragraph [0041] “The database 112 can store question-answer pairs, user data, user preferences, important reminders, and tips.” [0044] “For example, a pre-existing document including Frequently Asked Questions (FAQs) and corresponding answers can be made available to the chatbot. The processor can scan the FAQs and extract question-answer pairs from the FAQs. In another example, raw text (e.g., raw HTML) can be extracted from a webpage. In this manner, a first set of data (e.g., a knowledge seed)” [0124] “ The neural network 600 encodes an incoming query as well as the data in the knowledge base.” [0125] “ It is also possible to create category vectors as well and compare the mathematical properties of a category vector with the mathematical properties of the query vector to see if the statement semantically belongs in that category. For instance, the NLP server may compare the query vector to the category vector as well as compare the query vector to individual utterance vectors within categories in the encoded knowledge base during the process of classifying the intent of the message.” – Magliozzi extracts question-answer pairs from FAQs and stores the question-answer pairs in a database/knowledge base. The neural network encodes the knowledge base data, including the stored questions/utterances, into vector representations. The encoded knowledge base entries are organized within categories, and the NLP server compares query vectors to individual utterance vectors and category vectors during classification of message intent. Thus, the stored and encoded FAQ questions/utterances provide the claimed dataset of sample questions embedding vectors, and the organization of those encoded utterances within categories provide the claimed association between a candidate sample question embedding vector and a corresponding classification.).
Regarding claim 3, Pei in view of Magliozzi teaches all the elements of claim 1, therefore is rejected for the same reasons as those presented for claim 1. Pei in view of Magliozzi further teaches:
gathering an output from the at least one computing agent and providing at least a portion of the output from the at least one computing agent to a large language model system (Pei, paragraph [0022] “the intent classification system generates an automated reply to the communication, takes an action associated with a selected multi-level client intent classification, or routes communications to a particular client device (e.g., a specialized agent device).” [0025] “ the intent classification system utilized predicted multi-level client intent classifications from a communication and monitored user interactions from a corresponding agent device, client device or user account to further train the machine learning model. More specifically, the intent classification system can utilize agent selection of provided multi-level client intent classifications and the associated communication text and client features to further train the machine learning model.” [0053] “ In some embodiments, the machine learning model 212 is a natural language processing model, such as a transformer model. For instance, in one or more embodiments the intent classification system 102 utilizes BERT or DistilBERT machine learning model architectures” [0054] “ the intent classification system 102 utilizes the machine learning model 212 to generate the predicted multi-level client intent classifications 214.” – Pei teaches utilizing monitored user interactions from a corresponding agent device and agent selection of provided classifications to further train the machine learning model. Under BRI, the monitored interactions and agent selections correspond to outputs of the computing agent that are supplied to the machine learning model for further training. Pei further teaches that the machine learning model is a transformer-based model utilizing BERT and DistilBERT architectures corresponding to the claimed large language model system.).
Regarding claim 4, Pei in view of Magliozzi teaches all the elements of claim 1, therefore is rejected for the same reasons as those presented for claim 1. Pei in view of Magliozzi further teaches:
selecting a particular instance of a large language model system taken from a plurality of large language model system instances (Pei, paragraph [0064] “ the intent classification system 102 utilizes multiple hierarchical intent architectures and multiple machine learning models to generate different predictions at different stages. For example, in response to receiving a client interaction (e.g., a call or chat), the intent classification system 102 can utilize a first machine learning model to determine an initial client disposition/intent.” [0065] “the intent classification system 102 then utilizes an additional machine learning model (as described herein) to generate predictions for an additional (e.g., more specific or granular) hierarchical intent architecture. Thus, the intent classification system 102 utilizes a second machine learning model to generate recommended multi-level client intent classifications” [0066] “ the intent classification system can utilize multiple different machine learning models for different intent classification tasks.” [0075] “ the intent classification system 102 utilizes separate machine learning models for ticket routing and final ticket classification.” – Pei teaches utilizing multiple machine learning models to generate different predictions at different stages and for different intent classification tasks. Pei further teaches utilizing separate machine learning models for ticket routing and final ticket classification, as well as utilizing a first machine learning model, a second machine learning model, and an additional machine learning model for different processing tasks. Under BRI, the multiple machine learning models correspond to a plurality of large language model system instances, and utilizing a particular machine learning model for a corresponding task constitutes selecting a particular instance from the plurality of large language model system instances.).
Regarding claim 5, Pei in view of Magliozzi teaches all the elements of claim 4, therefore is rejected for the same reasons as those presented for claim 4. Pei in view of Magliozzi further teaches:
selecting the particular instance of the large language model system based at least in part on interfacing requirements of the particular instance of the large language model system (Pei, paragraph [0064] “ the intent classification system 102 utilizes multiple hierarchical intent architectures and multiple machine learning models to generate different predictions at different stages.” [0066] “the intent classification system can utilize multiple different machine learning models for different intent classification tasks.” [0075] “ In some implementations, the intent classification system 102 utilizes separate machine learning models for ticket routing and final ticket classification.” [0144] “Particular embodiments may provide interfaces that enable a client device 906, or an inter-network facilitation system 104 to manage, retrieve, modify, add, or delete, the information stored in data store.” [0146] “ the inter-network facilitation system 104 may enable users to interact with each other or other entities, or to allow users to interact with these entities through an application programming interfaces (“API”) or other communication channels.” – Pei teaches utilizing multiple machine learning models for different processing tasks, including separate machine learning models for ticket routing and final ticket classification. Pei further teaches interfaces, application programming interfaces (APIs), and communication channels that enable interaction between system components. Under BRI, the interfaces, APIs, and communication channels correspond to interfacing requirements of a particular machine learning model instance. Thus, utilizing a particular machine learning model in conjunction with corresponding interfaces, APIs, or communication channels teaches selecting a particular instance based on at least in part on interfacing requirements of the particular instance.).
Regarding claim 6, Pei in view of Magliozzi teaches all the elements of claim 1, therefore is rejected for the same reasons as those presented for claim 1. Pei in view of Magliozzi further teaches:
wherein the at least one computing agent of a content management system interfaces with an application programming interface of a large language model system (Magliozzi, paragraph [0041] “The database 112 can store question-answer pairs, user data, user preferences, important reminders, and tips…the database 112 may include or be augmented with databases for users, bot knowledgebase, and message queue.” [0042] “The second communications interface 114 can be communicatively coupled to the load balancer 104, the dialogue manager 108, and the NLP server 110… The second communications interface 114 enables bidirectional communication” Pei, paragraph [0022] “ the intent classification system generates an automated reply to the communication, takes an action associated with a selected multi-level client intent classification, or routes communications to a particular client device (e.g., a specialized agent device).” [0053] “In some embodiments, the machine learning model 212 is a natural language processing model, such as a transformer model. For instance, in one or more embodiments the intent classification system 102 utilizes BERT or DistilBERT machine learning model architectures.” – Magliozzi teaches a framework in which a database and knowledge base store question-answer pairs and related information, and a communications interface enables bidirectional communication between system components. Under BRI, the database and knowledge base components correspond to a content management system, and the communications interface corresponds to an application programming interface that enables communication between processing components. Pei teaches a computing agent in the form of a specialized agent device and further teaches a transformer-based machine learning model utilizing BERT and DistilBERT architectures. Under BRI, the specialized agent device corresponds to the claimed computing agent and the transformer based machine learning model corresponds to the claim large language model system. Accordingly, communication through Magliozzi’s communications interface between the content management components and Pei’s transformer based language model components teaches a computing agent of a content management system interfacing with an application programming interface of a large language model system.).
Regarding claim 7, Pei in view of Magliozzi teaches all the elements of claim 6, therefore is rejected for the same reasons as those presented for claim 6. Pei in view of Magliozzi further teaches:
further comprising storing at least one embedding vector in a local embedding storage of a content management system (Magliozzi, paragraph [0039] “The local collection of answers can be accessed from the database 112 where it is stored.” [0040] “The database 112 is communicatively coupled with the dialogue manager 108 and the NLP server 110. It may include a knowledge base…” [0125] “It is also possible to create category vectors as well and compare the mathematical properties of a category vector with the mathematical properties of the query vector… the NLP server may compare the query vector to the category vector as well as compare the query vector to individual utterance vectors within categories in the encoded knowledge base” – Magliozzi teaches storing information within a database and knowledge base, including a local collection of answers maintained for a particular domain and accessed by the NLP server. Magliozzi further teaches creating category vectors and comparing the mathematical properties of category vectors with query vectors during classification processing within an encoded knowledge base. Under BRI, the category vectors correspond to embedding vectors because they are encoded vector representations of semantic categories used for similarity and classification operations. Further, the database and knowledge base storing the local collection of answers correspond to a local embedding storage of a content management system. Accordingly, storing the category vectors within the database and knowledge base teaches storing at least one embedding vector in a local embedding storage of a content management system.)
Regarding claim 8, Pei in view of Magliozzi teaches all the elements of claim 7, therefore is rejected for the same reasons as those presented for claim 7. Pei in view of Magliozzi further teaches:
wherein the large language model system is in a first domain having a first security perimeter and wherein the content management system is in a second domain having a second security perimeter (Magliozzi, [0040] “The database 112 is communicatively coupled with the dialogue manager 108 and the NLP server 110. It may include a knowledge base” [0041] “The database 112 can store question-answer pairs, user data, user preferences, important reminders, and tips.” Pei, paragraph [0035] “the environment includes server(s) 106 implementing the intent classification system 102 part of an inter-network facilitation system 104… and a secured account management system 110.” [0036] “ the intent classification system 102 utilizes the network 112 to communicate with the client device 108, the agent device 114, and/or the secured account management system 110.” [0038] “the inter-network facilitation system 104 or the intent classification system 102 determines the identity and permissions of the client device 108 by communicating with the secured account management system 110… The intent classification system 102 can determine permissions of the client device 108 prior to disclosing secure information to the client device 108.” [0147] “the inter-network facilitation system 104 may include… cross-institution network interface manager, notification controller, action log, third-party-content-object-exposure log, inference module, authorization/privacy server, search module, user-interface module, user-profile (e.g., provider profile or requester profile) store, connection store, third-party content store, or location store…may also include suitable components such as network interfaces, security mechanisms” – Magliozzi teaches a database and knowledge base storing question-answer and related content, which correspond to the claimed content management system. Pei teaches separate computing environments, including an inter-network facilitation system and a secured account management system that communicates across a network, and further teaches permission controls, authorization/privacy servers, security mechanisms, network interfaces, and cross-institution network interface managers. Under BRI, Pei’s transformer-based machine learning model corresponds to the claimed large language model system in a first domain, while Magliozzi’s database and knowledge base components correspond to the claimed content management system in a second domain. Further, Pei’s permission controls, authorization/privacy servers, security mechanisms, and cross-institution network interface managers correspond to security perimeters governing communication between the domains.).
Regarding claim 9, Pei teaches the following limitations:
A non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by one or more processors causes the one or more processors to perform a set of acts for selecting a large language model interfacing technique, the set of acts comprising (Pei, paragraph [0120] “ In particular, one or more of the processes described herein may be implemented at least in part as instructions embodied in a non-transitory computer-readable medium and executable by one or more computing devices (e.g., any of the media content access devices described herein). In general, a processor (e.g., a microprocessor) receives instructions, from a non-transitory computer-readable medium, (e.g., a memory, etc.), and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein.”):
responsive to an occurrence of a user question, selecting a large language model interfacing technique by (Pei, paragraph [0021] “Upon extracting text and determining client features, the intent classification system utilizes a machine learning model to generate predicted multi-level client intent classifications. For example, in one or more embodiments, the intent classification system utilizes a BERT or DistilBERT machine learning model. To illustrate, the intent classification system can utilize a machine learning model with a transformer encoder and one or more classification layers.” [0022] “the intent classification system utilizes the predicted multi-level client intent classifications and corresponding multi-level client intent classification probabilities to perform a variety of automatic processes. For example, the intent classification system generates an automated reply to the communication, takes an action associated with a selected multi-level client intent classification, or routes communications to a particular client device (e.g., a specialized agent device). “ – Pei teaches responsive to an occurrence of a user question because the system processes user communications using a BERT/DistlBERT transformer model with a transformer encoder and classification layers. Pei further teaches selecting a large language model interfacing technique because the system uses the generated classification to select downstream processing operations, including generated automated replies, taking actions, or routing communications to specialized agent devices. Under the broadest reasonable interpretation, selecting among downstream conversational processing operations based on transformer-generated classifications teaches selecting a large language model interfacing technique.),
calculating a subject embedding vector based on at least a portion of the user question (Pei, paragraph [0019] “the intent classification system can extract text from various fields in a communication (e.g., digital email or text) and identify an associated client device or user account.” [0048] “the intent classification system 102 can extract the communication features 206 including the communication text 208. To illustrate, in one or more embodiments, the intent classification system 102 retrieves text from the body of a communication and any other text associated with the communication.“ [0052] “ the intent classification system 102 can encode the client communication features 206 (e.g., using one hot encoding, an encoding layer, or a vector mapping) and then process the encoding utilizing the machine learning model 212.” [0053] “the machine learning model 212 is a natural language processing model, such as a transformer model. For instance, in one or more embodiments the intent classification system 102 utilizes BERT or DistilBERT machine learning model architectures…the intent classification system 102 embeds the communication features 206 and inputs the embedded communication features into a transformer encoder of the machine learning model 212.” – Pei extracts communication text from a user communication and encodes the extracted communication features using an encoding layer or vector mapping prior to processing by a transformer-based machine learning model. Pei further teaches using BERT of DistiBERT architectures and embedding vector generated from textual input, as vector mappings and transformer encoders are standard mechanisms for generating embedding vectors in natural-language processing systems.);
invoking at least one computing agent based at least in part on the classification (Pei, paragraph [0022] “the intent classification system utilizes the predicted multi-level client intent classifications and corresponding multi-level client intent classification probabilities to perform a variety of automatic processes. For example, the intent classification system generates an automated reply to the communication, takes an action associated with a selected multi-level client intent classification, or routes communications to a particular client device (e.g., a specialized agent device).” [0054] “In one or more embodiments, the intent classification system 102 determines that the top predicted multi-level client intent classification is associated with a multi-level client intent classification probability that satisfies the intent classification threshold. Based on this determination, the intent classification system 102 can take an action associated with the multi-level client intent classification.” – Pei teaches utilizing predicted multi-level client intent classifications to perform downstream automatic processes, generate automated replies, take actions associated with selected classifications, and route communications to a specialized agent device. Under BRI, performing automatic processes or routing communications to a specialized agent device based on a determined classification corresponds to invoking at least one computing agent based at least in part on the classification.).
However, Pei does not teach but Pei in view of Magliozzi teaches the following limitations:
comparing the subject embedding vector to one or more sample question embedding vectors to select a candidate one of the sample question embedding vectors that is similar to the subject embedding vector (Magliozzi, paragraph [0044] “The data in the pre-existing documents and/or webpages can be in structured format or in unstructured format. For example, a pre-existing document including Frequently Asked Questions (FAQs) and corresponding answers can be made available to the chatbot. The processor can scan the FAQs and extract question-answer pairs from the FAQs. In another example, raw text (e.g., raw HTML) can be extracted from a webpage.” [0124] “The neural network 600 encodes an incoming query as well as the data in the knowledge base… the neural network 600 can encode two natural language messages into corresponding vectors, thereby transforming them into respective mathematical representations. The neural network 600 can then determine the semantic similarity between the two natural language sentences by determining the angle between their vector representations.” [0125] “the NLP server may compare the query vector to the category vector as well as compare the query vector to individual utterance vectors within categories in the encoded knowledge base during the process of classifying the intent of the message.” – Magliozzi teaches comparing the subject embedding vector to one or more sample question embedding vectors because the neural network encodes an incoming query and data stored in a knowledge base, including question question-answer pairs extracted from FAQs, into vector representations. Magliozzi further teaches comparing the query vector to individual utterance vectors within categories in the encoded knowledge base and determining semantic similarity between the vector representations by determining the angle between the vectors. Under BRI, the encoded stored questions correspond to sample question embedding vectors and the comparison of the query vector to the stored question vectors identifies a candidate question vector that is similar to the subject embedding vector.);
determining a classification of the subject embedding vector (Magliozzi, paragraph [0124] “In another example, the neural network 600 analyzes a question from the user, such as “What is FAFSA?” The NLP server understands the intent of this communication and categorizes this communication as a question related to the Free Application for Federal Student Aid (FAFSA).” [0125] “It is also possible to create category vectors as well and compare the mathematical properties of a category vector with the mathematical properties of the query vector to see if the statement semantically belongs in that category. For instance, the NLP server may compare the query vector to the category vector as well as compare the query vector to individual utterance vectors within categories in the encoded knowledge base during the process of classifying the intent of the message.” – Magliozzi teaches determining a classification of the subject embedding vector because the NLP server compares the query vector to category vectors and individual utterance vectors within categories in the encoded knowledge base during classification of message intent. Magliozzi further teaches determining whether the query semantically belongs in a category by comparing mathematical properties of the query vector and category vector. Under BRI, the category corresponds to a classification, and determining that the query vector semantically belongs in the category corresponds to determining a classification of the subject embedding vector.); and
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having Pei and Magliozzi before them, to incorporate the vector-based query comparison and category classification techniques of Magliozzi into the intent classification system of Pei. One would have been motivated to make such a combination in order to compare an incoming user question to a previously stored questions represented as vectors, determine a classification based on semantic similarity between the user question and the stored questions, and utilize the resulting classification to select downstream processing actions. This would allow user questions to be matched against stored questions content within a knowledge base and classified according to semantically similar categories, thereby improving identification of user intent and enabling selection of appropriate automated actions associated with the determined classification.
Regarding claim 10, Pei in view of Magliozzi teaches all the elements of claim 9, therefore is rejected for the same reasons as those presented for claim 9. The claim recites similar limitations corresponding to claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding claim 11, Pei in view of Magliozzi teaches all the elements of claim 9, therefore is rejected for the same reasons as those presented for claim 9. The claim recites similar limitations corresponding to claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding claim 12, Pei in view of Magliozzi teaches all the elements of claim 9, therefore is rejected for the same reasons as those presented for claim 9. The claim recites similar limitations corresponding to claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Regarding claim 13, Pei in view of Magliozzi teaches all the elements of claim 12, therefore is rejected for the same reasons as those presented for claim 12. The claim recites similar limitations corresponding to claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding claim 14, Pei in view of Magliozzi teaches all the elements of claim 9, therefore is rejected for the same reasons as those presented for claim 9. The claim recites similar limitations corresponding to claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Regarding claim 15, Pei in view of Magliozzi teaches all the elements of claim 14, therefore is rejected for the same reasons as those presented for claim 14. The claim recites similar limitations corresponding to claim 7 and is rejected for similar reasons as claim 7 using similar teachings and rationale.
Regarding claim 16, Pei in view of Magliozzi teaches all the elements of claim 15, therefore is rejected for the same reasons as those presented for claim 15. The claim recites similar limitations corresponding to claim 8 and is rejected for similar reasons as claim 8 using similar teachings and rationale.
Regarding claim 17, Pei teaches the following limitations:
A system for selecting a large language model interfacing technique, the system comprising: a storage medium having stored thereon a sequence of instructions; and one or more processors that execute the sequence of instructions to cause the one or more processors to perform a set of acts, the set of acts comprising (Pei, paragraph [0129] “ In particular embodiments, processor(s) 802 includes hardware for executing instructions, such as those making up a computer program. As an example, and not by way of limitation, to execute instructions, processor(s) 802 may retrieve (or fetch) the instructions from an internal register, an internal cache, memory 804, or a storage device 806 and decode and execute them.”),
responsive to an occurrence of a user question, selecting a large language model interfacing technique by (Pei, paragraph [0021] “Upon extracting text and determining client features, the intent classification system utilizes a machine learning model to generate predicted multi-level client intent classifications. For example, in one or more embodiments, the intent classification system utilizes a BERT or DistilBERT machine learning model. To illustrate, the intent classification system can utilize a machine learning model with a transformer encoder and one or more classification layers.” [0022] “the intent classification system utilizes the predicted multi-level client intent classifications and corresponding multi-level client intent classification probabilities to perform a variety of automatic processes. For example, the intent classification system generates an automated reply to the communication, takes an action associated with a selected multi-level client intent classification, or routes communications to a particular client device (e.g., a specialized agent device). “ – Pei teaches responsive to an occurrence of a user question because the system processes user communications using a BERT/DistlBERT transformer model with a transformer encoder and classification layers. Pei further teaches selecting a large language model interfacing technique because the system uses the generated classification to select downstream processing operations, including generated automated replies, taking actions, or routing communications to specialized agent devices. Under the broadest reasonable interpretation, selecting among downstream conversational processing operations based on transformer-generated classifications teaches selecting a large language model interfacing technique.),
calculating a subject embedding vector based on at least a portion of the user question (Pei, paragraph [0019] “the intent classification system can extract text from various fields in a communication (e.g., digital email or text) and identify an associated client device or user account.” [0048] “the intent classification system 102 can extract the communication features 206 including the communication text 208. To illustrate, in one or more embodiments, the intent classification system 102 retrieves text from the body of a communication and any other text associated with the communication.“ [0052] “ the intent classification system 102 can encode the client communication features 206 (e.g., using one hot encoding, an encoding layer, or a vector mapping) and then process the encoding utilizing the machine learning model 212.” [0053] “the machine learning model 212 is a natural language processing model, such as a transformer model. For instance, in one or more embodiments the intent classification system 102 utilizes BERT or DistilBERT machine learning model architectures…the intent classification system 102 embeds the communication features 206 and inputs the embedded communication features into a transformer encoder of the machine learning model 212.” – Pei extracts communication text from a user communication and encodes the extracted communication features using an encoding layer or vector mapping prior to processing by a transformer-based machine learning model. Pei further teaches using BERT of DistiBERT architectures and embedding vector generated from textual input, as vector mappings and transformer encoders are standard mechanisms for generating embedding vectors in natural-language processing systems.);
invoking at least one computing agent based at least in part on the classification (Pei, paragraph [0022] “the intent classification system utilizes the predicted multi-level client intent classifications and corresponding multi-level client intent classification probabilities to perform a variety of automatic processes. For example, the intent classification system generates an automated reply to the communication, takes an action associated with a selected multi-level client intent classification, or routes communications to a particular client device (e.g., a specialized agent device).” [0054] “In one or more embodiments, the intent classification system 102 determines that the top predicted multi-level client intent classification is associated with a multi-level client intent classification probability that satisfies the intent classification threshold. Based on this determination, the intent classification system 102 can take an action associated with the multi-level client intent classification.” – Pei teaches utilizing predicted multi-level client intent classifications to perform downstream automatic processes, generate automated replies, take actions associated with selected classifications, and route communications to a specialized agent device. Under BRI, performing automatic processes or routing communications to a specialized agent device based on a determined classification corresponds to invoking at least one computing agent based at least in part on the classification.).
However, Pei does not teach but Pei in view of Magliozzi teaches the following limitations:
comparing the subject embedding vector to one or more sample question embedding vectors to select a candidate one of the sample question embedding vectors that is similar to the subject embedding vector (Magliozzi, paragraph [0044] “The data in the pre-existing documents and/or webpages can be in structured format or in unstructured format. For example, a pre-existing document including Frequently Asked Questions (FAQs) and corresponding answers can be made available to the chatbot. The processor can scan the FAQs and extract question-answer pairs from the FAQs. In another example, raw text (e.g., raw HTML) can be extracted from a webpage.” [0124] “The neural network 600 encodes an incoming query as well as the data in the knowledge base… the neural network 600 can encode two natural language messages into corresponding vectors, thereby transforming them into respective mathematical representations. The neural network 600 can then determine the semantic similarity between the two natural language sentences by determining the angle between their vector representations.” [0125] “the NLP server may compare the query vector to the category vector as well as compare the query vector to individual utterance vectors within categories in the encoded knowledge base during the process of classifying the intent of the message.” – Magliozzi teaches comparing the subject embedding vector to one or more sample question embedding vectors because the neural network encodes an incoming query and data stored in a knowledge base, including question question-answer pairs extracted from FAQs, into vector representations. Magliozzi further teaches comparing the query vector to individual utterance vectors within categories in the encoded knowledge base and determining semantic similarity between the vector representations by determining the angle between the vectors. Under BRI, the encoded stored questions correspond to sample question embedding vectors and the comparison of the query vector to the stored question vectors identifies a candidate question vector that is similar to the subject embedding vector.);
determining a classification of the subject embedding vector (Magliozzi, paragraph [0124] “In another example, the neural network 600 analyzes a question from the user, such as “What is FAFSA?” The NLP server understands the intent of this communication and categorizes this communication as a question related to the Free Application for Federal Student Aid (FAFSA).” [0125] “It is also possible to create category vectors as well and compare the mathematical properties of a category vector with the mathematical properties of the query vector to see if the statement semantically belongs in that category. For instance, the NLP server may compare the query vector to the category vector as well as compare the query vector to individual utterance vectors within categories in the encoded knowledge base during the process of classifying the intent of the message.” – Magliozzi teaches determining a classification of the subject embedding vector because the NLP server compares the query vector to category vectors and individual utterance vectors within categories in the encoded knowledge base during classification of message intent. Magliozzi further teaches determining whether the query semantically belongs in a category by comparing mathematical properties of the query vector and category vector. Under BRI, the category corresponds to a classification, and determining that the query vector semantically belongs in the category corresponds to determining a classification of the subject embedding vector.); and
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having Pei and Magliozzi before them, to incorporate the vector-based query comparison and category classification techniques of Magliozzi into the intent classification system of Pei. One would have been motivated to make such a combination in order to compare an incoming user question to a previously stored questions represented as vectors, determine a classification based on semantic similarity between the user question and the stored questions, and utilize the resulting classification to select downstream processing actions. This would allow user questions to be matched against stored questions content within a knowledge base and classified according to semantically similar categories, thereby improving identification of user intent and enabling selection of appropriate automated actions associated with the determined classification.
Regarding claim 18, Pei in view of Magliozzi teaches all the elements of claim 17, therefore is rejected for the same reasons as those presented for claim 17. The claim recites similar limitations corresponding to claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding claim 19, Pei in view of Magliozzi teaches all the elements of claim 17, therefore is rejected for the same reasons as those presented for claim 17. The claim recites similar limitations corresponding to claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding claim 20, Pei in view of Magliozzi teaches all the elements of claim 17, therefore is rejected for the same reasons as those presented for claim 17. The claim recites similar limitations corresponding to claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Conclusion
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/Daravanh Phakousonh/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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