DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1, 8 and 15 are objected to because of the following informalities: the limitation “responsive detecting a conflict of the new automated assistant transcript record with an existing record of the plurality of records, modifying one or more fields of the new automated assistant transcript record” has a typographical error. The word “to” should be included between the words ‘responsive’ and ‘detecting’. Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent Number 11,783,133. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application claims are a broader version of the claims that appear in the Patent, as they are both directed to use transcript records that include a user query, query class, the inferred intent of the query, and the action taken to respond; and further determines potential conflicts with similar existing records for either adding the record to training data or modify the training data.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent Claims 1, 8 and 15 recite the limitations:
“receiving, by a computer system, an automated assistant transcript comprising a plurality of records, wherein each record of the plurality of records comprises a query, a classification of the query, an intent associated with the query, and a responsive action associated with the intent;
comparing the new automated assistant transcript record to one or more records of the plurality of records;
responsive detecting a conflict of the new automated assistant transcript record with an existing record of the plurality of records, modifying one or more fields of the new automated assistant transcript record;
appending the new automated assistant transcript record to the automated assistant transcript; and
utilizing the automated assistant transcript for training a first set of classification models and a second set of classification models, wherein each classification model of the first set of classification models is employed to determine a degree of association of an input query with a topic of a predefined set of topics, and wherein each classification model of the second set of classification models is employed to determine a degree of association of the input query with an intent of a predefined set of intents associated with the specified topic”.
First, the term “the new automated assistant transcript record” is considered unclear and indefinite as failing to provide proper antecedent basis. The claim recites receiving “an automated assistant transcript”, but it does not recite further receiving “a new automated assistant transcript record” so that it can be compared with the first recited records, which the automated assistant transcript comprises. This lack of clarity renders the claim indefinite. Clarification is required.
Second, the term "responsive detecting a conflict" is unclear as there is no "detection" step recited previously, and it is unclear whether this detection is a result of the previous "comparison" step or not; therefore it lacks antecedent basis. Examiner understands that a conflict detection step should be recited before this limitation in order to clearly conclude the basis of it. In addition, there is a typographical error in this limitation, as this should read “responsive to detecting a conflict”. Clarification is required.
Claims 2-7, 9-14, 16-20 are rejected as being dependent claims. 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 stand rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 analysis:
In the instant case, the claims are directed to a method, system and a medium. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A analysis:
Based on the claims being determined to be within of the four categories (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically the abstract ideas of Mental Processes- “Concepts performed in the human mind (including an observation, evaluation, judgment, opinion)”, and Mathematical Concepts (including mathematical relationships, formulas, and/or calculations).
Step 2A: Prong 1 analysis:
Independent Claim 1 (and 8 and 15 as being analogous) recites:
“comparing the new automated assistant transcript record to one or more records of the plurality of records”- this limitation corresponds to evaluate and analyze records; being a mental process/abstract idea.
Step 2A: Prong 2 analysis:
This judicial exception is not integrated into a practical application because it only recites these additional elements:
“receiving, by a computer system, an automated assistant transcript comprising a plurality of records, wherein each record of the plurality of records comprises a query, a classification of the query, an intent associated with the query, and a responsive action associated with the intent” - this limitation amounts to necessary data gathering of records describing queries, classifications and intents, and this is considered a pre-solution activity (data gathering), being an insignificant extra solution activity (see MPEP 2106.05(g));
“responsive detecting a conflict of the new automated assistant transcript record with an existing record of the plurality of records, modifying one or more fields of the new automated assistant transcript record” - this limitation amounts to data manipulation such as modifying fields of records, which is an insignificant extra solution activity per 2106.05(g);
“appending the new automated assistant transcript record to the automated assistant transcript” - this limitation amounts to data manipulation such as appending new data, which is an insignificant extra solution activity per 2106.05(g);
“utilizing the automated assistant transcript for training a first set of classification models and a second set of classification models, wherein each classification model of the first set of classification models is employed to determine a degree of association of an input query with a topic of a predefined set of topics, and wherein each classification model of the second set of classification models is employed to determine a degree of association of the input query with an intent of a predefined set of intents associated with the specified topic” this limitation recites the training of a classification models at a high level of generality, for the purpose of determining the intent and topic of a query (which are mental processes, abstract ideas), therefore, this is considered mere instructions to apply an exception on a computer per 2106.05(f).
(Claim 8 only) “a memory; and a processing device”- these generic computer components are recited at a high level of generality such that it amounts no more than mere instructions to apply the judicial exception using a computer (see MPEP 2106.05(f)).
(Claim 15 only) “a non-transitory computer-readable storage medium”- these generic computer components are recited at a high level of generality such that it amounts no more than mere instructions to apply the judicial exception using a computer (see MPEP 2106.05(f)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B analysis:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements recited at Claims 1, 8 and 15 above amount to no more than insignificant extra solution activities, and mere instructions to apply a judicial exception on a computer.
Moreover, re-evaluation of the additional elements or combination of elements that were considered to be insignificant extra-solution activity at Claims 1, 8 and 15 are needed to determine if they are considered well-understood, routine and conventional limitations:
“receiving, by a computer system, an automated assistant transcript comprising a plurality of records, wherein each record of the plurality of records comprises a query, a classification of the query, an intent associated with the query, and a responsive action associated with the intent” - this limitation further amounts to receiving or transmitting dataset over a network, further considered well-understood, routine and conventional under MPEP 2106.05(d) II (i);
“responsive detecting a conflict of the new automated assistant transcript record with an existing record of the plurality of records, modifying one or more fields of the new automated assistant transcript record” - this limitation further amounts to electronic recordkeeping/updating data, considered well-understood, routine and conventional under MPEP 2106.05(d) II (iii);
“appending the new automated assistant transcript record to the automated assistant transcript” - this limitation further amounts to electronic recordkeeping/updating data, considered well-understood, routine and conventional under MPEP 2106.05(d) II (iii).
Dependent claims 2-7, 9-14, 16-20 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The claims are reciting further embellishment of the judicial exception.
Claim 2: this claim recites further embellishment about appending data, which further amounts to manipulating data and corresponds to insignificant extra solution activities under Step 2A Prong 2, and well-understood routine and conventional under Step 2B.
Claim 3: this claim recites validating models, which amounts to mental steps such as evaluating and judging models, being abstract ideas. Further, it recites publishing the models for deployment, which under broadest reasonable interpretation, amounts to transmitting the model, being data outputting and transmitting data over a network, being insignificant extra solution activities and well understood, routine and conventional.
Claim 4: this claim recites further embellishment about the mental processes for validation process using a quality metric, which is a mathematical value, and comparing to against a threshold, being mathematical relationships.
Claim 5: this claim recites computing values, which amount to mathematical calculations, being abstract ideas.
Claim 6: this claim recites representing a query as a vector, which amounts to mathematical representation/ relationships, being abstract ideas.
Claim 7: this claim recites further embellishment about the intent of que query being determined, being evaluation and judgment steps.
Claim 9 and Claim 16, they are rejected on the same basis as dependent claim 2, mutatis mutandis, since they are analogous claims.
Claim 10 and Claim 17, they are rejected on the same basis as dependent claim 3, mutatis mutandis, since they are analogous claims.
Claim 11 and Claim 18, they are rejected on the same basis as dependent claim 4, mutatis mutandis, since they are analogous claims.
Claim 12 and Claim 19, they are rejected on the same basis as dependent claim 5, mutatis mutandis, since they are analogous claims.
Claim 13 and Claim 20, they are rejected on the same basis as dependent claim 6, mutatis mutandis, since they are analogous claims.
Claim 14, it is rejected on the same basis as dependent claim 7, mutatis mutandis, since they are analogous claims.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5-9, 12-16, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Magliozzi et al (2018/0131645- hereinafter Magliozzi, as submitted in IDS dated 8/14/2023) in view of Dempsey et al (US Pub. No. 2002/0147754 - hereinafter Dempsey), in further in view of Lavallee et al (US Patent No. 10,108,603 - hereinafter Lavallee).
Referring to Claim 1, Magliozzi teaches a method, comprising:
receiving, by a computer system, an automated assistant transcript comprising a plurality of records, wherein each record of the plurality of records comprises a query, a classification of the query, an intent associated with the query, and a responsive action associated with the intent (see Magliozzi at [0044]: “included in the chatbot system can scan pre-existing documents and/or webpages on the Internet or an intranet to extract initial information for the chatbot. These documents can provide a historical understanding about a specific field, such as understanding/results based on historical data of user behavior in healthcare. 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. In this manner, a first set of data (e.g., a knowledge seed) is generated for the chatbot”. Therefore, since the chatbot is trained to receive a query, classify it, understand its intent and replying back, this training corresponds to the claimed "transcript");
utilizing the automated assistant transcript for training a first set of classification models and a second set of classification models, wherein each classification model of the first set of classification models is employed to determine a degree of association of an input query with a topic of a predefined set of topics, and wherein each classification model of the second set of classification models is employed to determine a degree of association of the input query with an intent of a predefined set of intents associated with the specified topic (see Magliozzi at [0045]: " the chatbot generates a language model using NLP techniques based on the first set of data. In one instance, the first set of data can be used as initial training data to train a neural network…. The processor included in the chatbot can implement machine-learning algorithms to parse, process, and classify the first set of data. The language model that is generated based on the first set of data (e.g., knowledge seed) forms the foundational framework based on which the chatbot is initially trained". Further at [0104]: “If the chatbot detects a question from the user, the chatbot determines a response to the question based on the language model. In some aspects, the chatbot determines a context for the question and attempts a semantically informed pattern match”. Further at [0124]: “For instance, the neural network 600 may encode a greeting from the user, such as “hello,” and the NLP server may categorize the intent of the user as a greeting based on the encoding. 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)”).
However, Magliozzi fails to teach:
comparing the new automated assistant transcript record to one or more records of the plurality of records;
responsive detecting a conflict of the new automated assistant transcript record with an existing record of the plurality of records, modifying one or more fields of the new automated assistant transcript record;
appending the new automated assistant transcript record to the automated assistant transcript; and
utilizing the automated assistant transcript for training a first set of classification models and a second set of classification models, wherein each classification model of the first set of classification models is employed to determine a degree of association of an input query with a topic of a predefined set of topics, and wherein each classification model of the second set of classification models is employed to determine a degree of association of the input query with an intent of a predefined set of intents associated with the specified topic.
Dempsey teaches, in an analogous system,
comparing the new automated assistant transcript record to one or more records of the plurality of records (see Dempsey at [0014]; "Association coefficients of a new data vector with one or more of the data vectors of the training data set may be used to form measures of conflict between the new data vector and the vectors of the training data set. These measures of conflict may then be used, for example, to decide whether the new data vector should be added to the training data set or used to retrain the trainable data classifier, or whether one or more vectors of the training data set should be discarded if the new data vector is added". Therefore, this measure of conflict corresponds to the claimed comparison of the records);
responsive detecting a conflict of the new automated assistant transcript record with an existing record of the plurality of records, modifying one or more fields of the new automated assistant transcript record (see Dempsey at [0014]; "Association coefficients of a new data vector with one or more of the data vectors of the training data set may be used to form measures of conflict between the new data vector and the vectors of the training data set. These measures of conflict may then be used, for example, to decide whether the new data vector should be added to the training data set or used to retrain the trainable data classifier, or whether one or more vectors of the training data set should be discarded if the new data vector is added");
appending the new automated assistant transcript record to the automated assistant transcript (see Dempsey at [0014]; "Association coefficients of a new data vector with one or more of the data vectors of the training data set may be used to form measures of conflict between the new data vector and the vectors of the training data set. These measures of conflict may then be used, for example, to decide whether the new data vector should be added to the training data set or used to retrain the trainable data classifier, or whether one or more vectors of the training data set should be discarded if the new data vector is added").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Magliozzi with the above teachings of Dempsey by training an automated assistant using records, as taught by Magliozzi, and detecting any conflict between records during training, as taught by Dempsey. The modification would have been obvious because one of ordinary skill in the art would be motivated to determine conflict between training data in order to modify training data sets, removing redundancy and thereby providing a more even coverage of the training data, as suggested by Dempsey at [0008 and 0014].
Lavallee teaches, in an analogous system, utilizing the automated assistant transcript for training a first set of classification models and a second set of classification models, wherein each classification model of the first set of classification models is employed to determine a degree of association of an input query with a topic of a predefined set of topics, and wherein each classification model of the second set of classification models is employed to determine a degree of association of the input query with an intent of a predefined set of intents associated with the specified topic (see Lavallee at Column 2: 37-46: “Text corresponding to the speech input is generated, and the text is processed using a context-free linguistic model and a context-specific linguistic model to generate one or more linguistic processing results for the processed text. The text and the corresponding linguistic processing results are then processed using a natural language understanding model to generate a natural language understanding recognition result. The natural language understanding recognition result comprises at least one intent and at least one mention corresponding to the text generated from the digital signal”. Therefore, the use of multiple models to determine an both an intent (interpreted as the claimed intent) and a mention (interpreted as the claimed topic) of an input text (interpreted as the claimed input query) is interpreted as the first and second set of models. Regarding the claimed “degree of association”, it is taught by Magliozzi as it can be seen at Magliozzi’s [0037]: “The NLP server 110 accepts the question (e.g., as a string) along with the user's data and the user's state (e.g., payload) and determines the intent of the question through a multi-stage preprocessing, parsing, and classification process. The NLP server 110 returns the result along with a match score and a corresponding answer to the dialogue manager 108”. Therefore, it would have been obvious to combine Magliozzi’s ‘match score’ system with Lavallee’s models for determining intent and topic to provide a proper response appropriately to the user query).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Magliozzi and Dempsey with the above teachings of Lavallee by training an automated assistant using records and detecting any conflict between records, as taught by Magliozzi and Dempsey, and training models to determine the intent and topic of the input query, as taught by Lavallee. The modification would have been obvious because one of ordinary skill in the art would be motivated use separate models in order to improve automated understanding of natural language input (as suggested by Lavalle at Col. 4: 46-51).
Referring to Claim 2, the combination of Magliozzi, Dempsey and Lavallee teaches the method of claim 1, further comprising:
responsive to failing to detect a conflict of the new automated assistant transcript record with the plurality of records, appending the new automated assistant transcript record to the automated assistant transcript (see Dempsey at [0014]; "Association coefficients of a new data vector with one or more of the data vectors of the training data set may be used to form measures of conflict between the new data vector and the vectors of the training data set. These measures of conflict may then be used, for example, to decide whether the new data vector should be added to the training data set or used to retrain the trainable data classifier, or whether one or more vectors of the training data set should be discarded if the new data vector is added").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Magliozzi with the above teachings of Dempsey by training an automated assistant using records, as taught by Magliozzi, and detecting any conflict between records, as taught by Dempsey. The modification would have been obvious because one of ordinary skill in the art would be motivated to determine conflict between training data in order to modify training data sets, removing redundancy and thereby providing a more even coverage of the training data, as suggested by Dempsey at [0008 and 0014].
Referring to Claim 5, the combination of Magliozzi, Dempsey and Lavallee teaches the method of claim 1, wherein comparing the new automated assistant transcript record to one or more records further comprises: computing values of one or more classification features associated with the new automated assistant transcript record (see Dempsey at [0014]; "Association coefficients of a new data vector with one or more of the data vectors of the training data set may be used to form measures of conflict between the new data vector and the vectors of the training data set. These measures of conflict may then be used, for example, to decide whether the new data vector should be added to the training data set or used to retrain the trainable data classifier, or whether one or more vectors of the training data set should be discarded if the new data vector is added". Therefore, these association coefficients to measure of conflict corresponds to the claimed computed values).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Magliozzi with the above teachings of Dempsey by training an automated assistant using records, as taught by Magliozzi, and comparing records to detect any conflict between records, as taught by Dempsey. The modification would have been obvious because one of ordinary skill in the art would be motivated to determine conflict between training data in order to modify training data sets, removing redundancy and thereby providing a more even coverage of the training data, as suggested by Dempsey at [0008 and 0014].
Referring to Claim 6, the combination of Magliozzi, Dempsey and Lavallee teaches the method of claim 1, wherein the query is represented by a vector of classification features, wherein each element of the vector represents a number of occurrences in the query of a word identified by an index of the element (see Magliozzi at [0130]: “However, if one character in the user's message is mistakenly embedded, the word-level dictionary may not return a value for the misspelled word. In such instances, the neural network 600 determines the correlation between the word vectors and the character vectors to determine the word and in turn determine user intent. For example, the neural network 600 may use correlations of the word-level encoding with the character-level encoding that it learned at training time”).
Referring to Claim 7, the combination of Magliozzi, Dempsey and Lavallee teaches the method of claim 1, wherein the query is associated with a parameter identifying an object of an action identified by the intent (see Magliozzi at [0124]: “The neural network 600 encodes an incoming query as well as the data in the knowledge base. The NLP server uses a matrix multiplication external to the neural network to categorize the encoded query. For instance, the neural network 600 may encode a greeting from the user, such as “hello,” and the NLP server may categorize the intent of the user as a greeting based on the encoding. 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)”).
Referring to independent Claim 8 and Claim 15, they are rejected on the same basis as independent claim 1, mutatis mutandis, since they are analogous claims.
Referring to dependent Claim 9 and Claim 16, they are rejected on the same basis as dependent claim 2, mutatis mutandis, since they are analogous claims.
Referring to dependent Claim 12 and Claim 19, they are rejected on the same basis as dependent claim 5, mutatis mutandis, since they are analogous claims.
Referring to dependent Claim 13 and Claim 20, they are rejected on the same basis as dependent claim 6, mutatis mutandis, since they are analogous claims.
Referring to dependent Claim 14, it is rejected on the same basis as dependent claim 7, mutatis mutandis, since they are analogous claims.
Claims 3-4, 10-11, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Magliozzi et al (2018/0131645- hereinafter Magliozzi, as submitted in IDS dated 8/14/2023) in view of Dempsey et al (US Pub. No. 2002/0147754 - hereinafter Dempsey), in view of Lavallee et al (US Patent No. 10,108,603 - hereinafter Lavallee), and further in view of Goel et al (US Pub. No. 2019/0043239- hereinafter Goel, as submitted in IDS dated 8/14/2023).
Referring to Claim 3, the combination of Magliozzi, Dempsey and Lavallee teaches the method of claim 1, however, fails to teach further comprising:
validating the classification models; and publishing the classification models to a model deployment environment.
Goel teaches, in an analogous system, validating the classification models; and publishing the classification models to a model deployment environment (see Goel at [0054]: “In some examples, a level of accuracy of the model generated by the neural network 302 is determined by an example avatar response validator 306. … Once the example neural network 302 reaches a threshold level of accuracy (e.g., the example network 302 is trained and ready for deployment), the example avatar response validator 306 outputs the model to the example avatar response engine 304”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Magliozzi, Dempsey and Lavallee with the above teachings of Goel by training an automated assistant using records and detecting any conflict between records, as taught by Magliozzi, Dempsey and Lavallee, and validating prior to deployment, as taught by Goel. The modification would have been obvious because one of ordinary skill in the art would be motivated to ensure the model is accurate according to desired metrics prior to use, as suggested by Goel at [0054].
Referring to Claim 4, the combination of Magliozzi, Dempsey, Lavallee and Goel teaches the method of claim 3, wherein validating a classification model further comprises:
running the classification model on a plurality of data items of a validation data set; evaluating a quality metric reflecting a difference between an actual output of the classification model and a desired output of the classification model; and determining whether the quality metric value falls within a predetermined range (see Goel at [0054]; Goel teaches validation data user for validating the model, comparing known and predicted data from the model to measure 95% accuracy yield, and then once it reaches the threshold level of accuracy it is deployed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Magliozzi, Dempsey and Lavallee with the above teachings of Goel by training an automated assistant using records and detecting any conflict between records, as taught by Magliozzi, Dempsey and Lavallee, and validating prior to deployment, as taught by Goel. The modification would have been obvious because one of ordinary skill in the art would be motivated to ensure the model is accurate according to desired metrics prior to use, as suggested by Goel at [0054].
Referring to dependent Claim 10 and Claim 17, they are rejected on the same basis as dependent claim 3, mutatis mutandis, since they are analogous claims.
Referring to dependent Claim 11 and Claim 18, they are rejected on the same basis as dependent claim 4, mutatis mutandis, since they are analogous claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS A SITIRICHE whose telephone number is (571)270-1316. The examiner can normally be reached M-F 9am-6pm.
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/LUIS A SITIRICHE/ Primary Examiner, Art Unit 2126
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