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 .
In response to Applicant’s claims filed on March 13, 2026, claims 1, 3-13 and 15-16 and 18-20 are now pending for examination in the application.
Response to Arguments
This office action is in response to amendment filed 03/13/2026. In this action Claim(s) 1, 3-8, 16 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US Pub. No. 20220138826) and Yu et al. (US Pub. No. 20200327445) in further view of Luo et al. (US Pub. No. 20200019546). The Wang reference has been added to address the amendment of processing the one or more tokens of the data unit using a natural language processing MLM of the plurality of MLMs, wherein the natural language processing MLM comprises a neural network trained to generate a second embedding vector comprising a second plurality of components, wherein an individual component of the second plurality of components comprises a lexical score that characterizes a lexical association of the one or more tokens of the data unit with the one or more anchor tokens of the respective cluster of the plurality of clusters.
In this action claim(s) 9-13 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US Pub. No. 20220138826) and Manda et al. (US Pub. No. 20230153641) in further view Mihindukulasooriya et al. (US Pub. No. 20220129770). The Wang reference has been added to address the amendment of training, by the processing device of the server machine, a first machine learning model (MLM) using a training data unit stored in a data pod of a plurality of data pods, the training data units comprising one or more tokens, wherein training the first MLM comprises: causing the first MLM to access a first cluster score of each token of the one or more tokens, the first cluster score characterizing affinity of a respective token with the one or more anchor tokens of a first cluster of the plurality of cluster.
Applicant’s arguments:
In regards to claim 1 on Page(s) 10, applicant argues, “Claim 1 as amended recites specific technical operations that cannot practically be performed in the human mind.”
Examiner’s Reply:
Applicant argues that the claims comprises statutory subject matter. Examiner respectfully disagrees. The examiner notes that the computer as recited in the claims are being used for standardizing data for querying (the computer is being used as a generic tool). Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application. Identifying, Generating, aggregating query data for data standardization and generating a report does not improve the functioning of a computing system. Receiving and retrieving data related to standardization is insignificant extra solution activity.
Applicant’s arguments:
In regards to claim 1 on Page(s) 10, applicant argues, “A neural network generating embedding vectors is inherently a computational process that cannot be performed in the human mind. Additionally, claim 1 as amended recites "aggregating, using at least one of a maximum pooling classifier, an average pooling classifier, or a weighted pooling classifier, the first embedding vector and the second embedding vector to generate a third embedding vector." Pooling classifiers performing mathematical aggregation of multi-dimensional embedding vectors are specific computational operations that cannot practically be performed mentally.”
Examiner’s Reply:
Applicant argues that the claims comprises statutory subject matter. Examiner respectfully disagrees. The examiner notes that the computer as recited in the claims are being used for standardizing data for querying (the computer is being used as a generic tool). Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application. Aggregating numerical operations is a mathematical process and does not improve the functioning of a computing system. Pooling said classifiers does not improve the functioning of a computer.
Applicant’s arguments:
In regards to claim 1 on Page(s) 11, applicant argues “Claim 1 as amended recites a specific technical architecture: receiving a query "by a processing device of a server machine from a client machine via a network," processing through dual MLMs with a pooling classifier, and "retrieving over the network, from one or more data pods target data stored in the one or more data pods." The claim further produces a tangible technical result: "generating a report associated with the query and comprising the target data retrieved from the one or more data pods.”
Examiner’s Reply:
Applicant argues that the claims comprises statutory subject matter. Examiner respectfully disagrees. The examiner notes that the computer as recited in the claims are being used for standardizing data for querying (the computer is being used as a generic tool). Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application. Receiving and retrieving data related to standardization is insignificant extra solution activity. The merely automation (using MLMs) of the claimed steps are no more than mere instructions to apply the exception using generic computer components.
Applicant’s arguments:
In regards to claim 1 on Page(s) 12, applicant argues, “storage architecture. The ordered combination of storing clusters and anchors in a data structure, training on data from data pods, writing modified scores back to storage, generating embedding vectors, processing through a machine learning classifier to obtain probabilities, and retrieving from data pods over a network represents a complete technical pipeline that cannot be performed in the human mind.”
Examiner’s Reply:
Applicant argues that the claims comprises statutory subject matter. Examiner respectfully disagrees. The examiner notes that the computer as recited in the claims are being used for standardizing data for querying (the computer is being used as a generic tool). Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application. Storing, retrieving, and obtaining data recites insignificant extra solution activity.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 1, 3-13, 15-16, 18-20 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. With respect to claim 1 and 15 There is no support for “…generate a third embedding vector”. With respect to claim 9. There is no support for “…a token data structure”.
Dependent claims 3-8, 10-13, 16, 18-20 is/are also rejected for inheriting the deficiencies of the independent claims from which they depend on.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-13, 15-16, 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claim 1, 3-13, 15-16, 18-20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below, on Claim Rejections - 35 USC 101 accordance with the "2019 Revised Patent Subject Matter Eligibility Guidance" (published on 1/7/2019 in Fed, Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the "2019 PEG").
Step 1. in accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted the claim method (claims 1, 3-8), a method (claim 9-13), a system (claim 16 and 18-20), are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mathematical Concepts & Mental Processes enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1 and 16 are directed towards the Mathematical Concepts & Mental Process Grouping of Abstract Ideas.
Independent claim(s) 1 and 16 recites the following limitations directed towards a Mathematical Concepts & Mental Processes:
representing, by the processing device, the data unit via one or more tokens (This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Nothing in the claim element precludes the step from practically being performed in the mind. For example, the claim encompasses representing with tokens. Thus, the claim recites a mental process);
processing, by the processing device, the one or more tokens using a plurality of machine learning models (MLMs) to identify affinity of the data unit to one or more clusters of a plurality of clusters, wherein each of the plurality of clusters is associated with one or more anchor tokens (This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Nothing in the claim element precludes the step from practically being performed in the mind. For example, the claim encompasses identifying clusters. Thus, the claim recites a mental process), wherein processing the one or more tokens comprises:
processing the one or more tokens of the data unit using a statistical MLM of the plurality of MLMs, wherein the natural language processing MLM comprises a neural network trained to generate a first embedding vector comprising a first plurality of components, wherein each component of the first plurality of components characterizes a number of times the one or more tokens of the data unit have been historically encountered with the one or more anchor tokens of a respective cluster of the plurality of clusters (This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Nothing in the claim element precludes the step from practically being performed in the mind. For example, the claim encompasses generating a vector. Thus, the claim recites a mental process); and
processing the one or more tokens of the data unit using a natural language processing MLM of the plurality of MLMs, wherein the natural language processing MLM comprises a neural network trained to generate a second embedding vector comprising a second plurality of components, wherein each component of the second plurality of components comprises a lexical score that characterizes a lexical association of the one or more tokens of the data unit with the one or more anchor tokens of the respective cluster of the plurality of clusters (This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Nothing in the claim element precludes the step from practically being performed in the mind. For example, the claim encompasses generating a vector. Thus, the claim recites a mental process); and
aggregating, using at least one of a maximum pooling classifier, an average pooling classifier, or a weighted average pooling classifier, the first embedding vector and the second embedding vector to generate a third embedding vector (This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Nothing in the claim element precludes the step from practically being performed in the mind. For example, the claim encompasses aggregating data and generating a vector. Thus, the claim recites a mental process); and
identifying the one or more target clusters of the plurality of clusters using the third embedding vector (This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Nothing in the claim element precludes the step from practically being performed in the mind. For example, the claim encompasses identifying a cluster. Thus, the claim recites a mental process);
generating a report associated with the query and comprising the target data
retrieved from the one or more data pods (This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Nothing in the claim element precludes the step from practically being performed in the mind. For example, the claim encompasses generating a report. Thus, the claim recites a mental process).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1 and 16:
receiving, by a processing device of a server machine from a client machine via a network, a query comprising a data unit (recites insignificant extra solution activity that amounts to mere data gathering);
processing, by the processing device, the one or more tokens using a plurality of machine learning models (MLMs) to identify one or more clusters of a plurality of clusters, wherein each of the one or more identified clusters is associated with at least one token of the one or more tokens ((machine learning models (i.e., merely automate the claimed steps and are no more than mere instructions to apply the exception using generic computer components), wherein processing the one or more tokens comprises:
processing the one or more tokens of the data unit using a statistical MLM of the plurality of MLMs, wherein the natural language processing MLM comprises a neural network trained to generate a first embedding vector comprising a first plurality of components, wherein each component of the first plurality of components characterizes a number of times the one or more tokens of the data unit have been historically encountered with the one or more anchor tokens of a respective cluster of the plurality of clusters (machine learning models (i.e., merely automate the claimed steps and are no more than mere instructions to apply the exception using generic computer components); and
processing the one or more tokens of the data unit using a natural language processing MLM of the plurality of MLMs, wherein the natural language processing MLM comprises a neural network trained to generate a second embedding vector comprising a second plurality of components, wherein each component of the second plurality of components comprises a lexical score that characterizes a lexical association of the one or more tokens of the data unit with the one or more anchor tokens of the respective cluster of the plurality of clusters ((machine learning models (i.e., merely automate the claimed steps and are no more than mere instructions to apply the exception using generic computer components); and
retrieving, using the one or more clusters, the data stored in the data store in association with the one or more clusters (recites insignificant extra solution activity that amounts to retrieving query data).
The claim as a whole merely describes how to generally “apply” the exception in a
computer environment. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any
meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include
additional elements that are sufficient to amount to significantly more than the judicial
exception. Because the additional elements of the independent claims amount to insignificant
extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
storing, in a token data structure of a data store, initiating a plurality of clusters initiated by a processing device of a server machine storing one or more anchor tokens in association to each cluster of the plurality of clusters; and training, by the processing device of the server machine, a first machine learning model (MLM) using a training data unit stored in a data pod of a plurality of data pods, the training data units comprising one or more tokens, wherein training the first MLM comprises: causing the first MLM to access a first cluster score of each token of the one or more tokens, the first cluster score characterizing affinity of a respective token with the one or more anchor tokens of a first cluster of the plurality of clusters; causing the first MLM to modify the first cluster score of each token of the one or more tokens in view of a first number of occurrences, in the training data unit, of the one or more anchor tokens assigned to the first cluster of the plurality of clusters; and storing the modified first cluster score of each token in a token data structure of the data store; receiving a query comprising one or more query tokens; causing the first MLM to generate, using the first cluster scores of each of the one or more query tokens, an embedding vector comprising a plurality of components, wherein an individual component of the plurality of components characterizes a likelihood of a historical association of the one or more query tokens with the one or more anchor tokens of an individual cluster of the plurality of clusters; and
processing, using a machine learning classifier, the embedding vector to obtain a plurality of probabilities characterizing association of the query to the plurality of clusters; and retrieving, by the processing device of the server machine over a network, from one or more data pods of the plurality of data pods and using the plurality of probabilities, data stored in the one or more data pods and associated with the query.
Independent claim(s) 9 recites the following limitations directed towards a Mathematical Concepts & Mental Processes:
training, by the processing device of the server machine, a first machine learning model (MLM) using a training data unit stored in a data pod of a plurality of data pods, the training data units comprising one or more tokens, wherein training the first MLM comprises:
causing the first MLM to modify the first cluster score of each token of the one or more tokens in view of a first number of occurrences, in the training data unit, of the one or more anchor tokens assigned to the first cluster of the plurality of clusters (This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Nothing in the claim element precludes the step from practically being performed in the mind. For example, the claim encompasses modifying a score. Thus, the claim recites a mental process); and
causing the first MLM to generate, using the first cluster scores of each of the one or more query tokens, an embedding vector comprising a plurality of components, wherein an individual component of the plurality of components characterizes a likelihood of a historical association of the one or more query tokens with the one or more anchor tokens of an individual cluster of the plurality of clusters (This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Nothing in the claim element precludes the step from practically being performed in the mind. For example, the claim encompasses generating a vector. Thus, the claim recites a mental process).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 9:
storing, in a token data structure of a data store, a plurality of clusters initiated by a processing device of a server machine storing one or more anchor tokens in association to each cluster of the plurality of clusters (recites insignificant extra solution activity that amounts to storing data);
training, by the processing device of the server machine, a first machine learning model (MLM) using a training data unit stored in a data pod of a plurality of data pods, the training data units comprising one or more tokens ((machine learning models (i.e., merely automate the claimed steps and are no more than mere instructions to apply the exception using generic computer components), wherein training the first MLM comprises:
causing the first MLM to access a first cluster score of each token of the one or more tokens, the first cluster score characterizing affinity of a respective token with the one or more anchor tokens of a first cluster of the plurality of clusters (recites insignificant extra solution activity that amounts to accessing data);
storing the modified first cluster score of each token in a token data structure of the data store (recites insignificant extra solution activity that amounts to storing data);
receiving a query comprising one or more query tokens (recites insignificant extra solution activity that amounts to mere data gathering);
processing, using a machine learning classifier, the embedding vector to obtain a plurality of probabilities characterizing association of the query to the plurality of clusters (recites insignificant extra solution activity that amounts to obtaining data); and
retrieving, by the processing device of the server machine over a network, from one or more data pods of the plurality of data pods and using the plurality of probabilities, data stored in the one or more data pods and associated with the query (recites insignificant extra solution activity that amounts to retrieving data).
The claim as a whole merely describes how to generally “apply” the exception in a
computer environment. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any
meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include
additional elements that are sufficient to amount to significantly more than the judicial
exception. Because the additional elements of the independent claims amount to insignificant
extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 9, and 16 are rejected under 35 U.S.C. 101.
With respect to claim(s) 3 and 18:
Step 2A, prong one of the 2019 PEG:
wherein the number of times the one or more tokens of the data unit have been historically encountered with the one or more anchor tokens of the respective cluster of the plurality of clusters characterizes a number of times the one or more tokens of the data unit have been encountered in training data units together with the one or more anchor tokens of the respective given cluster (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a vector).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4:
Step 2A, prong one of the 2019 PEG:
wherein the training data units comprise data units previously processed by the statistical MLM (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a vector).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5:
Step 2A, prong one of the 2019 PEG:
updating one or more parameters of the statistical MLM in view of the one or more tokens of the data unit and at least a subset of the plurality of clusters (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by updating a parameter).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6 and 19:
Step 2A, prong one of the 2019 PEG:
wherein each cluster of at least a subset of the plurality of clusters is associated with different lexical units having a substantially same semantic meaning (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by identifying a cluster).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7:
Step 2A, prong one of the 2019 PEG:
a plurality of training data units, and
ground truth labels generated by application of the statistical MLM to the plurality of training data units (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by identifying a cluster).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 8 and 20:
Step 2A, prong one of the 2019 PEG:
correcting spelling of one or more words in the data unit (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by coreccting spelling),
translating one or more foreign-language words in the data unit (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by translating words),
replacing one or more acronyms in the data unit (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by coreccting spelling),
expanding one or more abbreviations in the data unit (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by expanding abbreviations), or
adding one or more spaces between two or more words in the data unit (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by spacing words).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 10:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
causing the first MLM to access a second cluster score of each token of the one or more tokens (recites insignificant extra solution activity that amounts to mere accessing cluster data); and
causing the first MLM to modify the second cluster score of each token of the one or more tokens in view of a second number of occurrences in the training data unit of the one or more anchor tokens assigned to a second cluster of the plurality of clusters (recites insignificant extra solution activity that amounts to mere accessing cluster data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The specification does not provide any indication that the accessing cluster, and its contents, was done by anything other than a generic, off-the-shelf computer component. Additionally, the Symantec, TLI, and OIP Techs court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well- understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that this limitation is a well-understood, routine, and conventional activity is supported. The claim is not patent eligible.
With respect to claim(s) 11:
Step 2A, prong one of the 2019 PEG:
wherein each cluster of at least a subset of the plurality of clusters is associated with different lexical units having a substantially same semantic meaning (The limitation recites a mental process of observation and/or evaluation capable of being performed by identifying cluster-related data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 12:
Step 2A, prong one of the 2019 PEG:
wherein, prior to processing training the first MLM using the training data unit, the first cluster score characterizes a number of times a respective token of the one or more tokens has been encountered in previous training data units together with the one or more anchor tokens assigned to the first cluster (The limitation recites a mental process of observation and/or evaluation capable of being performed by the scoring cluster-related data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 13:
Step 2A, prong one of the 2019 PEG:
wherein a higher first cluster score indicates a higher probability of occurrence, in a same data unit, of a respective token of the one or more tokens with the one or more anchor tokens assigned to the first cluster (The limitation recites a mental process of observation and/or evaluation capable of being performed by the scoring cluster-related data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 15:
Step 2A, prong one of the 2019 PEG:
training a natural language processing MLM using plurality of training data united stored in the one or more data pods of the plurality of data pods and ground truth labels generated by application of the first MLM to the plurality of training data units, wherein the natural language processing MLM comprises a neural network (The limitation recites a mental process of observation and/or evaluation capable of being performed by the identifying cluster data);
causing the natural language processing MLM to generate a second embedding vector comprising a second plurality of components, wherein an individual component of the second plurality of components comprises a lexical score that characterizes a lexical association of the one or more query tokens with the one or more anchor tokens of a respective cluster of the plurality of clusters (The limitation recites a mental process of observation and/or evaluation capable of being performed by the generating a vector).
Step 2A Prong Two Analysis:
wherein to obtain the plurality of probabilities, the machine learning classifier is further to process the second embedding vector (recites insignificant extra solution activity that amounts to obtaining probability data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-8, 16 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US Pub. No. 20220138826) and Yu et al. (US Pub. No. 20200327445) and Luo et al. (US Pub. No. 20200019546) in further view of Drew et al. (US Pub. No. 20140344195).
With respect to claim 1, Wang teaches receiving, by a processing device of a server machine from a client machine via a network, a query comprising a data unit (Paragraph 84 discloses converting one or more queries and/or item titles (or other search result candidates) from natural language sequences and symbols into vectors (or “tokens”) and embedding them in vector space 407);
representing, by the processing device, the data unit via one or more tokens (Paragraph 84 discloses converting one or more queries and/or item titles (or other search result candidates) from natural language sequences and symbols into vectors (or “tokens”) and embedding them in vector space 407);
processing, by the processing device, the one or more tokens using a plurality of machine learning models (MLMs) to identify affinity of the data unit to one or more target clusters of a plurality of clusters, wherein each of the plurality of clusters is associated with one or more anchor tokens (Paragraph 59 discloses learn the parameters of the machine learning model(s) so that similar features of the input are closer to each other in vector space), wherein processing the one or more tokens comprises:
processing the one or more tokens of the data unit using a natural language processing MLM of the plurality of MLMs, wherein the natural language processing MLM comprises a neural network trained to generate a second embedding vector comprising a second plurality of components, wherein an individual component of the second plurality of components comprises a lexical score that characterizes a lexical association of the one or more tokens of the data unit with the one or more anchor tokens of the respective cluster of the plurality of clusters (Paragraph 84 discloses word tagging model 403 is generally responsible for converting one or more queries and/or item titles (or other search result candidates) from natural language sequences and symbols into vectors (or “tokens”) and embedding them in vector space 407). Wang does not disclose aggregating, using at least one of a maximum pooling classifier, an average pooling classifier, or a weighted average pooling classifier, the first embedding vector and the second embedding vector to generate a third embedding vector; identifying the one or more target clusters...; retrieving over the network, from one or more data pods and using the one or more target clusters, target data…
However, Yu et al. discloses aggregating, using at least one of a maximum pooling classifier, an average pooling classifier, or a weighted average pooling classifier, the first embedding vector and the second embedding vector to generate a third embedding vector (Paragraph 121 discloses Character embeddings are used to represent such OOV words where each character in the word is represented by a De dimensional vector and the vectors of all characters in a word are aggregated using a character level convolutional neural network (CNN) and Paragraph 84 discloses data classifier wherein the data classifier is applied to one or more cluster centers); and
identifying the one or more target clusters of the plurality of clusters using the third embedding vector (Paragraph 123 discloses a nonparametric clustering method i.e. database scan (DB Scan) to be used on the feature vectors);
retrieving over the network, from one or more data pods and using the one or more target clusters, target data stored in the one or more data pods in association with the one or more target clusters (Paragraph 124 discloses the item can be queried across all the detected clusters and the list of Handling units with the best match cluster to pack the item could be retrieved).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Wang’s search engine with Yu et al.’s text classification. This would have facilitated deep learning for machine learning.
Wang’s as modified by Yu et al. does not disclose generating a report associated with the query and comprising the target data retrieved from the one or more data pods.
However, Luo et al. teaches generating a report associated with the query and comprising the target data retrieved from the one or more data pods (Paragraph 179 discloses generate a report, obtain data for presentation in a user interface, process a query, etc).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Wang’s search engine and Yu et al.’s text classification with Luo et al’s data processing. This would have facilitated deriving metrics for improved query processing.
The Wang reference as modified by Yu et al. and Luo et al. does not disclose processing the one or more tokens of the data unit using a statistical MLM of the plurality of MLMs to generate a first embedding vector comprising a first plurality of components, wherein an individual component of the first plurality of components characterizes a number of times the one or more tokens of the data unit have been historically encountered with the one or more anchor tokens of a respective cluster of the plurality of clusters.
However, Drew et al. discloses processing the one or more tokens of the data unit using a statistical MLM of the plurality of MLMs to generate a first embedding vector comprising a first plurality of components, wherein an individual component of the first plurality of components characterizes a number of times the one or more tokens of the data unit have been historically encountered with the one or more anchor tokens of a respective cluster of the plurality of clusters (Paragraph 88 discloses When gene tokens, hashes of gene tokens, or gene token annotations are used, embodiment values in the category totals collection 20 may represent the total number of times various gene token values are encountered for a particular category ID).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Wang’s search engine and Yu et al.’s text classification and Luo et al.’s data processing with Drew et al.’s machine learning. This would have facilitated classifying large volumes of data.
The Wang reference as modified by Yu et al. and Luo et al. and Drew et al. teaches all the limitations of claim 1. Regarding claim 3, Wang discloses the method of claim 1, wherein the number of times the one or more tokens of the data unit have been historically encountered with the one or more anchor tokens of the respective cluster of the plurality of clusters characterizes a number of times the one or more tokens have been encountered in training data units together with the one or more anchor tokens associated with the respective given cluster (Paragraph 85 discloses the inputs to the word tagging model 403 is a set of training data, such as query/title pairs or other historical queries and search result candidates that are oriented in vector space via training).
The Wang reference as modified by Yu et al. and Luo et al. and Drew et al. teaches all the limitations of claim 3. Regarding claim 4, Wang discloses the method of claim 3, wherein the training data units comprise data units previously processed by the first MLM (Paragraph 96 discloses machine learning model(s) are able to cluster samples of new unseen queries (e.g., any query received after training)).
The Wang reference as modified by Yu et al. and Luo et al. and Drew et al. teaches all the limitations of claim 3. Regarding claim 5, Wang discloses the method of claim 3, further comprising:
updating one or more parameters of the first MLM in view of the one or more tokens and the one or more identified clusters (Paragraph 59 discloses learn the parameters of the machine learning model(s) so that similar features of the input are closer to each other in vector space (e.g., all parts that that are within a threshold price range are closer to each other in feature space). In some embodiments, this training is done in supervised manner using a loss function (e.g. Triplet loss or GE2E loss) that try to map similar features into one classification or cluster. Once the model is trained, embodiments can represent a feature vector in vector space by aggregating (e.g. mean/median or dot product) the features of the inputs obtained by passing these inputs through the trained model).
Acron
The Wang reference as modified by Yu et al. and Luo et al. and Drew et al. teaches all the limitations of claim 1. Regarding claim 6, Wang discloses the method of claim 1, wherein each cluster of at least a subset of the plurality of clusters is associated with different lexical units having a substantially same semantic meaning (Paragraph 91 discloses generate semantic relationships between the query and search result candidates (and/or other queries via the query/search result semantic relationship mapper 103).
The Wang reference as modified by Yu et al. and Luo et al. and Drew et al. teaches all the limitations of claim 1. Regarding claim 7, Wang discloses the method of claim 1, wherein the natural language processing MLM is trained using:
a plurality of training data units, and ground truth labels generated by application of the first MLM to the plurality of training data units (Paragraph 105 discloses in a supervised context where inputs are labelled, inputs with the same label are identified in preparation for training. In an illustrative example, pairs of inputs that have the same label can be paired, as well as pairs of inputs that have differing labels can be paired).
The Wang reference as modified by Yu et al. and Luo et al. and Drew et al. teaches all the limitations of claim 1. Regarding claim 8, Wang discloses the method of claim 1, wherein representing the data unit via one or more tokens comprises performing at least one of:
correcting spelling of one or more words in the data unit, translating one or more foreign-language words in the data unit, replacing one or more acronyms in the data unit,
expanding one or more abbreviations in the data unit, or adding one or more spaces between two or more words in the data unit (Paragraph 47 discloses NLP analyzes summary information, keywords, text descriptions included in the set of data, and uses syntactic and semantic elements present in this information to identify the interest contexts. The syntactic and semantic elements can include information such as word frequency, word meanings, text font, italics, hyperlinks, proper names, noun phrases, parts-of-speech (e.g., noun, adverb, adjective, and the like) and/or the context of surrounding words. Other syntactic and semantic elements are also possible).
With respect to claim 16, Wang teaches a system comprising:
a memory (See Fig. 11); and
a processing device coupled to the memory (See Fig. 11), the processing device to perform operations comprising:
receiving, by a processing device of a server machine from a client machine via a network, a query comprising a data unit (Paragraph 84 discloses converting one or more queries and/or item titles (or other search result candidates) from natural language sequences and symbols into vectors (or “tokens”) and embedding them in vector space 407);
representing, by the processing device, the data unit via one or more tokens (Paragraph 84 discloses converting one or more queries and/or item titles (or other search result candidates) from natural language sequences and symbols into vectors (or “tokens”) and embedding them in vector space 407);
processing, by the processing device, the one or more tokens using a plurality of machine learning models (MLMs) to identify affinity of the data unit to one or more target clusters of a plurality of clusters, wherein each of the plurality of clusters is associated with one or more anchor tokens (Paragraph 59 discloses learn the parameters of the machine learning model(s) so that similar features of the input are closer to each other in vector space), wherein processing the one or more tokens comprises:
processing the one or more tokens of the data unit using a natural language processing MLM of the plurality of MLMs, wherein the natural language processing MLM comprises a neural network trained to generate a second embedding vector comprising a second plurality of components, wherein an individual component of the second plurality of components comprises a lexical score that characterizes a lexical association of the one or more tokens of the data unit with the one or more anchor tokens of the respective cluster of the plurality of clusters (Paragraph 84 discloses word tagging model 403 is generally responsible for converting one or more queries and/or item titles (or other search result candidates) from natural language sequences and symbols into vectors (or “tokens”) and embedding them in vector space 407). Wang does not disclose aggregating, using at least one of a maximum pooling classifier, an average pooling classifier, or a weighted average pooling classifier, the first embedding vector and the second embedding vector to generate a third embedding vector; identifying the one or more target clusters...; retrieving over the network, from one or more data pods and using the one or more target clusters, target data…
However, Yu et al. discloses aggregating, using at least one of a maximum pooling classifier, an average pooling classifier, or a weighted average pooling classifier, the first embedding vector and the second embedding vector to generate a third embedding vector (Paragraph 121 discloses Character embeddings are used to represent such OOV words where each character in the word is represented by a De dimensional vector and the vectors of all characters in a word are aggregated using a character level convolutional neural network (CNN) and Paragraph 84 discloses data classifier wherein the data classifier is applied to one or more cluster centers); and
identifying the one or more target clusters of the plurality of clusters using the third embedding vector (Paragraph 123 discloses a nonparametric clustering method i.e. database scan (DB Scan) to be used on the feature vectors);
retrieving over the network, from one or more data pods and using the one or more target clusters, target data stored in the one or more data pods in association with the one or more target clusters (Paragraph 124 discloses the item can be queried across all the detected clusters and the list of Handling units with the best match cluster to pack the item could be retrieved).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Wang’s search engine with Yu et al.’s text classification. This would have facilitated deep learning for machine learning.
Wang’s as modified by Yu et al. does not disclose generating a report associated with the query and comprising the target data retrieved from the one or more data pods.
However, Luo et al. teaches generating a report associated with the query and comprising the target data retrieved from the one or more data pods (Paragraph 179 discloses generate a report, obtain data for presentation in a user interface, process a query, etc).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Wang’s search engine and Yu et al.’s text classification with Luo et al’s data processing. This would have facilitated deriving metrics for improved query processing.
The Wang reference as modified by Yu et al. and Luo et al. does not disclose processing the one or more tokens of the data unit using a statistical MLM of the plurality of MLMs to generate a first embedding vector comprising a first plurality of components, wherein an individual component of the first plurality of components characterizes a number of times the one or more tokens of the data unit have been historically encountered with the one or more anchor tokens of a respective cluster of the plurality of clusters.
However, Drew et al. discloses processing the one or more tokens of the data unit using a statistical MLM of the plurality of MLMs to generate a first embedding vector comprising a first plurality of components, wherein an individual component of the first plurality of components characterizes a number of times the one or more tokens of the data unit have been historically encountered with the one or more anchor tokens of a respective cluster of the plurality of clusters (Paragraph 88 discloses When gene tokens, hashes of gene tokens, or gene token annotations are used, embodiment values in the category totals collection 20 may represent the total number of times various gene token values are encountered for a particular category ID).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Wang’s search engine and Yu et al.’s text classification and Luo et al.’s data processing wit Drew et al.’s machine learning. This would have facilitated deriving metrics for improved structural analysis of text.
With respect to claim 18, it is rejected on grounds corresponding to above rejected claim 3, because claim 18 is substantially equivalent to claim 3.
With respect to claim 19, it is rejected on grounds corresponding to above rejected claim 6, because claim 19 is substantially equivalent to claim 6.
With respect to claim 20, it is rejected on grounds corresponding to above rejected claim 8, because claim 20 is substantially equivalent to claim 8.
Claim(s) 9-13 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US Pub. No. 20220138826) and Manda et al. (US Pub. No. 20230153641) in further view Mihindukulasooriya et al. (US Pub. No. 20220129770).
With respect to claim 9, Wang teaches a method comprising:
storing, in a token data structure of a data store, a plurality of clusters initiated by a processing device of a server machine storing one or more anchor tokens in association to each cluster of the plurality of clusters (Paragraph 96 discloses machine learning model(s) are able to cluster samples of new unseen queries (e.g., any query received after training)); and
training, by the processing device of the server machine, a first machine learning model (MLM) using a training data unit stored in a data pod of a plurality of data pods, the training data units comprising one or more tokens, wherein training the first MLM comprises: causing the first MLM to access a first cluster score of each token of the one or more tokens, the first cluster score characterizing affinity of a respective token with the one or more anchor tokens of a first cluster of the plurality of clusters (Paragraph 72 discloses an output embedding vector representation can be generated, which is indicative of the actual coordinates that a vector will be plotted in vector space 209 based on semantic similarity to other words and/or averaging or otherwise combining the output embedding vectors for all of the words within the message vectors);
receiving a query comprising one or more query tokens (Paragraph 84 discloses converting one or more queries and/or item titles (or other search result candidates) from natural language sequences and symbols into vectors (or “tokens”) and embedding them in vector space 407).
Wang does not disclose causing the first MLM to generate, using the first cluster scores of each of the one or more query tokens, an embedding vector …
However, Manda et al. discloses causing the first MLM to generate, using the first cluster scores of each of the one or more query tokens, an embedding vector comprising a plurality of components, wherein an individual component of the plurality of components characterizes a likelihood of a historical association of the one or more query tokens with the one or more anchor tokens of an individual cluster of the plurality of clusters (Paragraph 48 discloses the extraction engine 150 can generate performance metrics and/or other historical feedback for a particular machine learning model and store this data relationally to the inputs and/or outputs of the machine learning model).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Wang’s search engine with Manda et al.’s machine learning. This would have facilitated formatting for machine learning.
Wang as modified by Manda et al. does not disclose processing, using a machine learning classifier, the embedding vector…; and retrieving, by the processing device of the server machine over a network, from one or more data pods of the plurality of data pods and using the plurality of probabilities, data stored…
However, Mihindukulasooriya et al. discloses processing, using a machine learning classifier, the embedding vector to obtain a plurality of probabilities characterizing association of the query to the plurality of clusters (See Paragraph 122 discloses neural model produces dense embedding vectors for input questions and Paragraph 124 discloses a neural network M is trained on D with the purpose to predict the correct relation type r.sub.k given the instance x.sub.k by minimizing the cross-entropy loss regarding the conditional probability p.sub.M(⋅|x.sub.k) modeled by M, with respect to the true relation t.sub.k); and
retrieving, by the processing device of the server machine over a network, from one or more data pods of the plurality of data pods and using the plurality of probabilities, data stored in the one or more data pods and associated with the query (See Paragraph 152 discloses a document collection (DC), a question in a natural language, a placeholder for the subject, and a placeholder for the object. The method provides as output a list of relation types sorted by probability scores).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Wang’s search engine with Manda et al.’s machine learning with Mihindukulasooriya et al’s. relation linking. This would have facilitated natural language processing.
The Wang reference as modified by Manda et al. and Mihindukulasooriya et al. teaches all the limitations of claim 9. Regarding claim 10, Manda et al. discloses the method of claim 9, wherein processing the training the first MLM data unit further comprises:
causing the first MLM to access a second cluster score of each token of the one or more tokens (Paragraph 66 discloses confidence scores, expected similarity scores, or similar metrics, which can improve over time as the model is trained on additional data and Paragraph 56 discloses cluster documents by their relevant content, and ability to highlight key sentences in documents); and
causing the first MLM to modify the second cluster score of each token of the one or more tokens in view of a second number of occurrences in the training data unit of the one or more anchor tokens assigned to a second cluster of the plurality of clusters (Paragraph 66 discloses confidence scores, expected similarity scores, or similar metrics, which can improve over time as the model is trained on additional data and Paragraph 56 discloses cluster documents by their relevant content, and ability to highlight key sentences in documents). The motivation to combine statement previously provided in the rejection of dependent claim 9 provided above, combining the Wang reference and the Manda et al. reference is applicable to dependent claim 10.
The Wang reference as modified by Manda et al. and Mihindukulasooriya et al. teaches all the limitations of claim 9. Regarding claim 11, Wang discloses the method of claim 9, wherein each cluster of at least a subset of the plurality of clusters is associated with different lexical units having a substantially same semantic meaning (Paragraph 91 discloses generate semantic relationships between the query and search result candidates (and/or other queries via the query/search result semantic relationship mapper 103).
The Wang reference as modified by Manda et al. and Mihindukulasooriya et al. teaches all the limitations of claim 9. Regarding claim 12, Manda et al. discloses the method of claim 9, wherein, prior to processing training the first MLM using the training data unit, the first cluster score characterizes a number of times a respective token of the one or more tokens has been encountered in previous training data units together with the one or more anchor tokens assigned to the first cluster (Paragraph 48 discloses the extraction engine 150 can generate performance metrics and/or other historical feedback for a particular machine learning model and store this data relationally to the inputs and/or outputs of the machine learning model). The motivation to combine statement previously provided in the rejection of dependent claim 9 provided above, combining the Wang reference and the Manda et al. reference is applicable to dependent claim 12.
The Wang reference as modified by Manda et al. and Mihindukulasooriya et al. teaches all the limitations of claim 9. Regarding claim 13, Manda et al. discloses the method of claim 9, wherein a higher first cluster score indicates a higher probability of occurrence, in a same data unit, of a respective token of the one or more tokens with the one or more anchor tokens assigned to the first cluster (Paragraph 66 discloses confidence scores, expected similarity scores, or similar metrics, which can improve over time as the model is trained on additional data and Paragraph 56 discloses cluster documents by their relevant content, and ability to highlight key sentences in documents). The motivation to combine statement previously provided in the rejection of dependent claim 9 provided above, combining the Wang reference and the Manda et al. reference is applicable to dependent claim 13.
The Wang reference as modified by Manda et al. and Mihindukulasooriya et al. teaches all the limitations of claim 9. Regarding claim 15, Wang discloses the method of claim 9, further comprising:
training a natural language processing MLM using a plurality of training data units stored in the one or more data pods of the plurality of data pods and ground truth labels generated by application of the first MLM to the plurality of training data units, wherein the natural language processing MLM comprises a neural network (Paragraph 57 discloses the accessory/sub-accessory determiner 109 is or uses one or more machine learning models to generate a particular decision statistic prediction (e.g., a classification prediction of a classifier model, a clustering prediction of a clustering model, or a regression prediction for a regression model). Such machine learning model(s) may be any suitable model of any suitable type. For example, such machine learning model can be supervised or unsupervised and can be or include a neural network (e.g., a Convolutional Neural Network (CNN) or Siamese Neural Network), decision trees, random forests, support vector machine, Naïve Bayes, and or clustering (e.g., K-means clustering));
causing the natural language processing MLM to generate a second embedding vector comprising a second plurality of components, wherein an individual component of the second plurality of components comprises a lexical score that characterizes of a lexical association of the one or more query tokens with the one or more anchor tokens of a respective cluster of the plurality of clusters (Paragraph 84 discloses word tagging model 403 is generally responsible for converting one or more queries and/or item titles (or other search result candidates) from natural language sequences and symbols into vectors (or “tokens”) and embedding them in vector space 407); and
wherein to obtain the plurality of probabilities, the machine learning classifier is further to process the second embedding vector (Paragraph 74 discloses rows within the vector space to be normalized for summing to 1 to become a probability distribution);
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-PUB 20200097587 is directed to MACHINE LEARNING DETECTION OF DATABASE INJECTION ATTACKS: [0035] automatically detect query language statements that may include a SQL injection attack, or otherwise represent malicious code. In a first method, a machine learning technique is trained by comparing two versions of a query language statement. A version is created that uses processed user input, such as user input processed using parameter (or user input) escaping, and a version is created that is not processed, such as not being escaped. Tokens, and optionally relationships between tokens, produced by the two versions are compared. Comparison can include a token-by-token comparison, or calculating hash values of sets of tokens, with a SQL injection attempt implicated if different hash values are produced. If the versions differ, a SQL injection attempt can be presumed, and the query, or a portion thereof (e.g., the user input, the pattern of escaping, tokens included in the query and their order) can be used (as part of a machine learning model) to analyze test data (e.g., data to be classified using the trained classifier).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.E.A/Examiner, Art Unit 2154
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154
