DETAILED ACTION
This communication is in response to the Amendments and Arguments filed on 7/31/2026.
Claims 1-2,4-11, 13-21, and 23 are pending and have been examined.
All previous objections / rejections not mentioned in this Office Action have been withdrawn by the examiner.
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 .
Response to Amendments
Regarding the Applicant’s arguments for the rejections under 35 U.S.C. § 101, applicant has amended independent claim 1, 10, and 19 and added dependent claims 23. Applicant asserts that the amended claims recite a specific technical solution to a technical problem in generating workflow from text. Applicant asserts that the improvement stems from the use of encoder-only transformer model that avoids the use of a decoder which requires lots of computing resources to train. Examiner respectfully disagrees. The determination made in step 2A prong two should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application. MPEP 2106.04(d). The amended independent claims, as a whole, does not include the specific technical solution to a technical problem in generating workflow from text. Currently, the amended independent claims utilizes an encoder-only transformer model to tag tokens representing sub-sentences with a BIOE tag. A person of ordinary skill in the art would recognize that an encoder-only transformer model is utilized for tasks such as classification and named entity recognition. Therefore, utilizing an encoder-only transformer to tag sub-sentences with BIOE tag does not provide the necessary improvement or specific details to generate workflow from text. In contrast to the amended claim recitation, the specification includes the necessary steps, as a whole, to generate workflow from text utilizing encoder-only transformer models. The specification Fig. 5A demonstrates the use of a first encoder-only transformer model to output BIOE (begin inside outside end) tag of each token in the input sentence. Spec. P0128. Then, the second encoder-only transformer model, a binary classification model, outputs indication information for indicating whether the input sub-sentence includes a workflow component. Spec. P0139. Lastly, a third encoder-only transformer model, a binary classification model, outputs indication information for indicating whether the order of the input two sub-sentences is correct. Spec. P0148. Thus, the currently amended claim recitation lacks specific improvement details, that are detailed in the specification, in solving the technical problem of generating workflow from text utilizing encoder-only transformer models.
Regarding the Applicant’s arguments for the rejections under 35 U.S.C. § 103, applicant asserts that prior art references do not teach the encoder-only transformer model for splitting sentences into sub-sentences by way of tagging tokens with BOIE tags to identify sub-sentence boundaries. Examiner respectfully disagrees. Prior art reference Burton teaches the use of a transformer model to annotate or markup text. Prior art reference Gao teaches the tagging of text utilizing BIOE tags. Burton in combination of Gao teach the independent claim limitation.
Claim Rejections - 35 USC § 112
Claims 1, 10, and 17 recites the limitation "tokens" in the limitation “a plurality of sequentially connected encoders, each encoder of which configured to process tokens of an input sentence and output hidden state vectors”. There is insufficient antecedent basis for this limitation in the claim.
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 1, 10, and 17 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. The as filed disclosure does not teach “wherein the first encoder-only transformer model excludes cross-attention layers and autoregressive sequence generation”, “a plurality of sequentially connected encoders”, and “each classification head of which connected to an output of a corresponding encoder and configured to assign a Begin-Inside-Outside-End (BIOE) tag”. Specifically, the as filed disclosure does not include any details or mention of excluding “cross-attention layers and autoregressive sequence generation”. The specification does not teach “sequentially connected encoders” for the “first encoder-based model”. The specification only teaches “sequentially connected encoders” for the “second encoder-based model” and “third encoder-based model”. Lastly, each classification head is not connected to an output of a corresponding encoder. The classification head is connected to an encoder output that includes hidden state vector corresponding to a token. Spec. P0129. The encoder output hidden state vector is input to the FFNN, and after the normalization process of the softmax function, to obtain probability values of the BIOE tags of the token. Spec. P0129.
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.
Claim 1-2,4-11, 13-21, and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, 10, and 19 the limitations of “receiving, as input to a workflow generation application executing on the computing device, text for describing a process of a task for which a workflow is to be generated”, “obtaining, using one or more encoder-only transformer models, a first sub-sentence group from the text for describing the process of the task, wherein the first sub-sentence group includes a plurality of sub-sentences arranged in an order in which the process occurs, each of the plurality of sub-sentences in the first sub-sentence group includes a workflow component, and the workflow component is configured to indicate information of a corresponding functional unit of a plurality of functional units for performing the task, wherein the obtaining the first sub-sentence group includes”, “decomposing the text for describing the process of the task to obtain a third sub-sentence group, wherein each of a plurality of sub-sentences included in the third sub-sentence group includes at most one workflow component, wherein the decomposing the text includes”, “performing sentence boundary detection on the text to obtain a plurality of sentences of the text”, “splitting, by a first encoder-only transformer model of the one or more encoder- only transformer models, each of the plurality of sentences of the text into one or more sub- sentences to obtain the third sub-sentence group, wherein the first encoder-only transformer model excludes cross-attention layers and autoregressive sequence generation, and wherein the first encoder-only transformer model comprises:”, “a plurality of sequentially connected encoders, each encoder of which configured to process tokens of an input sentence and output hidden state vectors”, “a plurality of classification heads, each classification head of which connected to an output of a corresponding encoder and configured to assign a Begin-Inside-Outside-End (BIOE) tag to each token to identify sub-sentence boundaries, wherein the splitting comprises:”, “splitting each sentence based on the BIOE tags”, “obtaining the first sub-sentence group based on the third sub-sentence group”, “determining a workflow label corresponding to each sub-sentence in the first sub-sentence group, wherein workflow labels corresponding to the plurality of sub-sentences in the first sub-sentence group include a first workflow label for indicating a first workflow component or a second workflow label for indicating a workflow pattern”, “generating the workflow of the task based on the workflow labels corresponding to the plurality of sub-sentences in the first sub-sentence group”, and “generating a visual representation of the workflow for display by the workflow generation application”, as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. More specifically, the mental process of a human reading text, thinking of phrases in the text along with a tag identification of the phrases, thinking of workflow label for the phrases, thinking of the task workflow that the phrases pertain, and writing on paper using a pen or pencil the workflow. The use of encoder-only transformer model, interpreted broadly, is interpreted as a set of rules or instructions to identify sub-sentences or phrases that can practically be performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the --Mental Processes-- grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the recitation of an application on a computing device in claim 1 and 10 and a non-transitory computer readable storage medium in claims 19, reads to generalized computer components, based upon the claim interpretation wherein the structure is interpreted using P0087-P0108 and P0213-225 in the specification. 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.
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 the integration of the abstract idea into a practical application, the additional element of using generalized computer components to read text, think of phrases in the text along with a tag identification of the phrases, think of workflow label for the phrases, think of the task workflow that the phrases pertain, and write on paper using a pen or pencil the workflow amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claim 2, 11, and 20, the claim recites “filtering the plurality of sub-sentences in the third sub-sentence group to obtain a fourth sub-sentence group, wherein each of a plurality of sub-sentences included in the fourth sub-sentence group includes one workflow component”, “reordering the plurality of sub-sentences in the fourth sub-sentence group to obtain the first sub-sentence group”, “the obtaining the first sub-sentence group from the text for describing the process of the task further includes”, “filtering the plurality of sub-sentences in the third sub-sentence group to obtain the fourth sub-sentence group, wherein each of the plurality of sub-sentences included in the fourth sub-sentence group includes one workflow component”, “reordering the plurality of sub-sentences in the fourth sub-sentence group to obtain a second sub-sentence group”, and “inserting at least one identifier of at least one workflow pattern into the second sub-sentence group to obtain the first sub-sentence group, wherein the at least one workflow pattern is configured to indicate a structure of the process of the task and a relationship between the plurality of functional units”, which reads on a human segmenting, filtering, reordering, and labeling words read in the mind. No additional limitations are present.
With respect to claim 4 and 13, the claim recites “determining, by a second encoder-only transformer model of the one or more encoder-only transformer models, whether each sub-sentence of the plurality of sub-sentences in the third sub-sentence group includes one workflow component” and “if the sub-sentence in the third sub-sentence group includes zero workflow component, removing the sub-sentence in the third sub-sentence group, so as to obtain the fourth sub-sentence group”, which reads on a human filtering words in a sentence in the mind. No additional limitations are present.
With respect to claim 5 and 14, the claim recites “reordering, by a third encoder-only transformer model of the one or more encoder-only transformer models, the plurality of sub-sentences in the fourth sub-sentence group, wherein an input of the third encoder-only transformer model is two sub-sentences in the fourth sub-sentence group, and an output of the third encoder-only transformer model is information for indicating whether an order of the two sub-sentences is correct”, which reads on a human reordering phrases read in the mind. No additional limitations are present.
With respect to claim 6 and 15, the claim recites “determining the at least one workflow pattern of the second sub-sentence group; for each workflow pattern of the at least one workflow pattern, obtaining the first sub-sentence group by performing, on the second sub-sentence group, at least one of the following”, “if no sub-sentence for indicating a boundary of the workflow pattern exists in the second sub-sentence group, inserting the workflow pattern boundary indicating sentence of the workflow pattern into the second sub-sentence group”, and “if no sub-sentence serving as the workflow pattern indicator exists in the second sub-sentence group, inserting the workflow pattern indicator of the workflow pattern into the second sub-sentence group”, which reads on a human putting labels on words read in the mind. No additional limitations are present.
With respect to claim 7 and 16, the claim recites “performing a pattern keyword detection on a plurality of sub-sentences in the second sub-sentence group” and “determining the at least one workflow pattern according to at least one pattern keyword that is detected, wherein each of the at least one pattern keyword is configured to indicate a workflow pattern of the at least one respective workflow pattern”, which reads on a human detecting keywords from read text in the mind. No additional limitations are present.
With respect to claim 8 and 17, the claim recites “matching each sub-sentence in the first sub-sentence group to a plurality of workflow labels included in a label dictionary, so as to determine the workflow label corresponding to each sub-sentence in the first sub-sentence group from the label dictionary”, which reads on a human detecting keywords from read text that match words on a list in the mind. No additional limitations are present.
With respect to claim 9 and 18, the claim recites “performing semantic similarity processing to match each sub-sentence in the first sub-sentence group to the pre-defined values in the label dictionary” and “determining a workflow label corresponding to a pre-defined value with a highest similarity score with respect to the sub-sentence in the pre-defined values as the workflow label corresponding to the sub-sentence”, which reads on a human detecting keywords from read text that are similar words on a list in the mind. No additional limitations are present. No additional limitations are present.
With respect to claim 21, the claim recites “wherein the first encoder-only transformer model is a pre-trained transformer model comprising”, “a plurality of sequentially connected encoders, each encoder of which having a plurality of input terminals and a plurality of output terminals, and a plurality of classification heads, each classification head including”, and “a feed-forward neural network (FFNN),a softmax function configured to normalize probability values, and an argmax function configured to select a highest probability value.”, which reads on a human utilizing a set of instructions or computations on paper using a pen or pencil. No additional limitations are present. No additional limitations are present.
With respect to claim 23, the claim recites “wherein the workflow labels corresponding to the plurality of sub-sentences in the first sub-sentence group include the first workflow label indicating a functional unit for performing the task and the second workflow label indicating a structural relationship between the plurality of functional units for performing the task”, which reads on human labeling sub-sentence as functional unit or workflow pattern indicating relationship between functional units. No additional limitations are present. No additional limitations are present.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
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.
Claims 1, 8, 10, 17, 19, 21, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Brunswig et al. (U.S. PG Pub No. 20170185255), hereinafter Brunswig, in view of Maes et al. (U.S. PG Pub No. 20200192975), hereinafter Maes, in view of Burton (U.S. PG Pub No. 20250165717), and further view of Gao et al. (U.S. PG Pub No. 20200065374), hereinafter Gao.
Regarding claim 1, 10 and 19 Brunswig teaches:
(Claim 1) A text processing method, performed by a computing device, comprising: (A text processing method, performed by a computing device, comprising:)
(Claim 10) A computing device, comprising: a memory; and at least one processor connected to the memory; wherein the memory is configured to store computer instructions that, when executed by the at least one processor, cause the computing device to implement: (A computing device, comprising: a memory; and at least one processor connected to the memory; wherein the memory is configured to store computer instructions that, when executed by the at least one processor, cause the computing device to implement:)
(Claim 19) A non-transitory computer-readable storage medium having stored computer instructions thereon, wherein when executed by a computer, the computer instructions cause the computer to implement: (A non-transitory computer-readable storage medium having stored computer instructions thereon, wherein when executed by a computer, the computer instructions cause the computer to implement:)
receiving, as input to a workflow generation application executing on the computing device, text for describing a process of a task for which a workflow is to be generated; (P0061, FIG. 6 is a diagram of example screens illustrating extraction of data elements or data objects from user input, and the use of the data elements or data objects in generating an assistant item. The screen can be at least generally similar to the screen, including presenting a text entry field. The text entry field is shown as containing text entered by a user. The text can represent, for example, a note that the user wishes to record with the workflow assistant. The user can enter the text into the workflow assistant for processing (e.g., for analysis or storage) by selecting a send icon.)
obtaining, using one or more encoder-only transformer models, a first sub-sentence group from the text for describing the process of the task, wherein the first sub-sentence group includes a plurality of sub-sentences arranged in an order in which the process occurs, each of the plurality of sub-sentences in the first sub-sentence group includes a workflow component, and the workflow component is configured to indicate information of a corresponding functional unit of a plurality of functional units for performing the task, wherein the obtaining the first sub-sentence group includes: (P0062, As part of the processing of the text, the workflow assistant, or a component in communication with the workflow assistant, can parse the text, such as to associate text elements (e.g., words or phrases) with reference keywords.)
decomposing the text for describing the process of the task to obtain a third sub-sentence group, wherein each of a plurality of sub-sentences included in the third sub-sentence group includes at most one workflow component, wherein the decomposing the text includes: (P0062, As part of the processing of the text, the workflow assistant, or a component in communication with the workflow assistant, can parse the text, such as to associate text elements (e.g., words or phrases) with reference keywords. Reference keywords, in some cases, can represent data objects or data elements maintained in a computing system associated with the workflow assistant.)
determining a workflow label corresponding to each sub-sentence in the first sub-sentence group, wherein workflow labels corresponding to the plurality of sub-sentences in the first sub-sentence group include a first workflow label for indicating a workflow component or a second workflow label for indicating a workflow pattern; (P0062, As part of the processing of the text, the workflow assistant, or a component in communication with the workflow assistant, can parse the text, such as to associate text elements (e.g., words or phrases) with reference keywords. Reference keywords, in some cases, can represent data objects or data elements maintained in a computing system associated with the workflow assistant.)
generating the workflow of the task based on the workflow labels corresponding to the plurality of sub-sentences in the first sub-sentence group; and (P0064, An analysis component of, or in communication with, the workflow assistant can also analyze the text to try and predict actions a user may wish to take, or suggest actions that may be helpful to a user. For example, the occurrence of a particular data object or data element associated with the text, or a particular collection of such objects or elements, may often be associated with one, or more, subsequent actions, such as the creation of a new data object, assistant item, or collection. In a specific case, the occurrence of the name of a company, or the name of an individual associated with a company, in connection with other keywords (e.g., order, invoice, sale), the name of a product, other data objects or data elements, or combinations thereof, may indicate that a user may wish to generate a new sales quote, or that the user may wish to consider doing so.)
generating a visual representation of the workflow for display by the workflow generation application. (P0067, The screen can show the user the result of taking the suggested action after selection of the action interface element, or guide the user in completing the suggested action. The screen can includes fields that can be populated with information from the text, context information of the base application, or data elements or data objects associated with a current assistant item or collection.)
Brunswig does not specifically teach:
performing sentence boundary detection on the text to obtain a plurality of sentences of the text, and
splitting, by a first encoder-only transformer model of the one or more encoder-only transformer models, each of the plurality of sentences of the text into one or more sub- sentences to obtain the third sub-sentence group; wherein the first encoder-only transformer model excludes cross-attention layers and autoregressive sequence generation, and wherein the first encoder-only transformer model comprises:
a plurality of sequentially connected encoders, each encoder of which configured to process tokens of an input sentence and output hidden state vectors
a plurality of classification heads, each classification head of which connected to an output of a corresponding encoder and configured to assign a Begin-Inside-Outside-End (BIOE) tag to each token to identify sub-sentence boundaries, wherein the splitting comprises:
splitting each sentence based on the BIOE tags; and
obtaining the first sub-sentence group based on the third sub-sentence group;
Maes, however, teaches:
splitting, by a first encoder-only transformer model of the one or more encoder-only transformer models, each of the plurality of sentences of the text into one or more sub- sentences to obtain the third sub-sentence group; wherein the first encoder-only transformer model excludes cross-attention layers and autoregressive sequence generation, and wherein the first encoder-only transformer model comprises: (P0044, The natural language processing engine may parse text represented by the input data to separate the text into words associated with a particular language construct and then further encode the parsed text into tokens.; P0048, Natural language processing engine may be a neural machine learning model, such as a recurrent neural network (RNN) model.)
obtaining the first sub-sentence group based on the third sub-sentence group; (P0045, The input sequence for the example depicted in FIG. 1A may be parsed into the following units of text: Migrate; VM-1; from; DataCenter1; to; and DataCenter2. Based on the units of text and other criteria, the natural language processing engine may then encode the units into corresponding tokens.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to split sentences into sub-sentences. It would have been obvious to combine the references because the input data that represents a sequence of steps to solve a particular problem may not be sufficient to solve the problem and modification may be required by the natural learning generation engine. (Maes P0022)
Brunswig in view of Maes does not specifically teach:
performing sentence boundary detection on the text to obtain a plurality of sentences of the text, and
splitting, by a first encoder-only transformer model of the one or more encoder-only transformer models, each of the plurality of sentences of the text into one or more sub- sentences to obtain the third sub-sentence group; wherein the first encoder-only transformer model excludes cross-attention layers and autoregressive sequence generation, and wherein the first encoder-only transformer model comprises:
a plurality of sequentially connected encoders, each encoder of which configured to process tokens of an input sentence and output hidden state vectors; a plurality of classification heads, each classification head of which connected to an output of a corresponding encoder and configured to assign a Begin-Inside-Outside-End (BIOE) tag to each token to identify sub-sentence boundaries, wherein the splitting comprises: splitting each sentence based on the BIOE tags; and
Burton, however, teaches:
performing sentence boundary detection on the text to obtain a plurality of sentences of the text, and (P0015, Sentence boundary detection.)
splitting, by a first encoder-only transformer model of the one or more encoder-only transformer models, each of the plurality of sentences of the text into one or more sub- sentences to obtain the third sub-sentence group; wherein the first encoder-only transformer model excludes cross-attention layers and autoregressive sequence generation, and wherein the first encoder-only transformer model comprises: (P0015, The annotations have been synthetically generated by a processing topology of neural language models (e.g. transformer type neural networks existing in parallel, serial, or cascade arrangements) and the transformed original markup and text has been subjected to processes including, but not limited to: data extraction, document understanding, and processing familiar to practitioners in the field of computational linguistics, such as named entity recognition, dependency parsing, constituency parsing, sentence boundary detection, word and sentence vector embedding, semantic markup tagging, entity and lexical unit linkage, sentiment analysis, framing analysis, and readability score analysis.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to perform sentence boundary detection and splitting sentences into sub-sentences. It would have been obvious to combine the references because sentence boundary detection is a known technique to yield a predictable result of annotating or labeling sentences or phrases in sentences for further language processing. (Burton P0015)
Brunswig in view of Maes and further view of Burton does not specifically teach:
a plurality of sequentially connected encoders, each encoder of which configured to process tokens of an input sentence and output hidden state vectors; a plurality of classification heads, each classification head of which connected to an output of a corresponding encoder and configured to assign a Begin-Inside-Outside-End (BIOE) tag to each token to identify sub-sentence boundaries, wherein the splitting comprises: splitting each sentence based on the BIOE tags; and
Gao, however, teaches:
a plurality of sequentially connected encoders, each encoder of which configured to process tokens of an input sentence and output hidden state vectors; a plurality of classification heads, each classification head of which connected to an output of a corresponding encoder and configured to assign a Begin-Inside-Outside-End (BIOE) tag to each token to identify sub-sentence boundaries, wherein the splitting comprises: splitting each sentence based on the BIOE tags; and (P0043, Relation classification component further include relation encoder layer.; P0044, Relation classification component further includes softmax layer to predict the relation labels based on the output of relation encoder layer.; Fig. 3, Sequentially connected layers.; P0026-P0030, NER can use the BIOES (Begin, Inside, Outside, End, Single) tagging scheme. For example, if there are two entity tags T1 and T2, all available labels are {B-T1, I-T1, O-T1, E-T1, S-T1, B-T2, I-T2, O-T2, E-T2, S-T2}, and each word will be assigned such a label. Training text “Jim had a bypass heart surgery at the Massachusetts General Hospital in 2010” can be tagged as: … corresponding relation labels may be as follows: (operated on, Bypass Heart Surgery, Jim) (treated in, Jim, Massachusetts General Hospital) (happened during, Bypass Heart Surgery, 2010))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to use an encoder to assign tags. It would have been obvious to combine the references because the use of a neural network with softmax and argmax are a known technique to yield a predictable result of predicting labels for input text. (Gao P0043, P0044)
Regarding claim 8 and 17 Brunswig, in view of Maes, in view of Burton, and further view of Gao teach claim 1 and 10.
Brunswig further teaches:
matching each sub-sentence in the first sub-sentence group to a plurality of workflow labels included in a label dictionary, so as to determine the workflow label corresponding to each sub-sentence in the first sub-sentence group from the label dictionary. (P0092, A data object can be associated with a dictionary template, such that potential keywords or search terms can be extracted from the data object. The search client can determine whether words, phrases, numbers, or other content of the text analysis results may be associated with, or related to, data objects or data elements used in the architecture and provide search results. A matching component can access the search results and text analysis results and, using matching rules, determine whether the elements of the text analysis results should be associated with a corresponding element of the search results.)
Regarding claim 21 Brunswig, in view of Maes, in view of Burton, and further view of Gao teach claim 1.
Brunswig, in view of Maes, and in further view of Burton does not specifically teach:
wherein the first encoder-only transformer model is a pre-trained transformer model comprising:a plurality of sequentially connected encoders, each encoder of which having a plurality of input terminals and a plurality of output terminals, and a plurality of classification heads, each classification head including:a feed-forward neural network (FFNN),a softmax function configured to normalize probability values, and an argmax function configured to select a highest probability value.
Gao, however, teaches:
wherein the first encoder-only transformer model is a pre-trained transformer model comprising: a plurality of sequentially connected encoders, each encoder of which having a plurality of input terminals and a plurality of output terminals, and a plurality of classification heads, each classification head including: a feed-forward neural network (FFNN), a softmax function configured to normalize probability values, and an argmax function configured to select a highest probability value. (P0034, NER network for predicting named entity tags and a RE network for predicting relation labels. In some embodiments, NER network may include several layers, such as a word representing layer where several types of representations are determined, a sequential encoder layer, and a decoding layer. In some embodiments, word representation layer may further include an attention-based subword encoder sublayer, a dilation-based subword encoder sublayer, a capitalization embedding sublayer, and a word embedding sublayer. In some embodiments, as shown by FIG. 2, RE network may also include several layers, such as a position embedding layer, a relation encoder layer, and a softmax layer. In some embodiments, RE network may share word representation layer with NER.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to use an encoder with multiple layers. It would have been obvious to combine the references because the use of a neural network with softmax and argmax are a known technique to yield a predictable result of predicting labels for input text. (Gao P0043, P0044)
Regarding claim 23 Brunswig, in view of Maes, in view of Burton, and further view of Gao teach claim 1.
Brunswig further teaches:
wherein the workflow labels corresponding to the plurality of sub-sentences in the first sub-sentence group include the first workflow label indicating a functional unit for performing the task and the second workflow label indicating a structural relationship between the plurality of functional units for performing the task. (P0082, Information used in an application or a workflow assistant item can be bound to a data model. The binding information can be used to identify data elements associated with data objects or workflow assistant items. The data objects or workflow assistant items can include annotation information that can be used to provide context, layout, or formatting information regarding the data object or workflow assistant item or its consistent data elements or data objects. In some cases, the annotations can represent metadata regarding the data object or workflow assistant item. Example of annotations include a title or description of, or icon associated with, the data object or workflow assistant item, or its component data element (such as labels for data fields). The annotations can also provide for navigation between data element components of the data object or workflow assistant item, or provide or define graphical representations (such as a chart) of data elements of a data object or workflow assistant item.)
Claims 2, 4-7, 11, 13-16, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Brunswig in view of Maes, in view of Burton, in view of Gao, and further view of Suparna et al. (U.S. PG Pub No. 20180113850), hereinafter Suparna.
Regarding claim 2, 11, and 20 Brunswig in view of Maes, in view of Burton, and in further view of Gao teach claim 1, 10, and 19.
Brunswig further teaches:
obtaining the first sub-sentence group from the text for describing the process of the task further includes: (P0062, As part of the processing of the text, the workflow assistant, or a component in communication with the workflow assistant, can parse the text, such as to associate text elements (e.g., words or phrases) with reference keywords. Reference keywords, in some cases, can represent data objects or data elements maintained in a computing system associated with the workflow assistant.)
inserting at least one identifier of at least one workflow pattern into the second sub-sentence group to obtain the first sub-sentence group, wherein the at least one workflow pattern is configured to indicate a structure of the process of the task and a relationship between the plurality of functional units. (P0064, The workflow assistant can also analyze the text to try and predict actions a user may wish to take, or suggest actions that may be helpful to a user. For example, the occurrence of a particular data object or data element associated with the text, or a particular collection of such objects or elements, may often be associated with one, or more, subsequent actions, such as the creation of a new data object, assistant item, or collection.; P0092, The search client can determine whether words, phrases, numbers, or other content of the text analysis results may be associated with, or related to, data objects or data elements used in the architecture and provide search results.; P0093, If a search result is identified as relevant, it can be identified for the user. For example, the name or other identifier of the search result can be displayed to the user.)
Brunswig does not specifically teach:
filtering the plurality of sub-sentences in the third sub-sentence group to obtain a fourth sub-sentence group, wherein each of a plurality of sub-sentences included in the fourth sub-sentence group includes one workflow component; and
reordering the plurality of sub-sentences in the fourth sub-sentence group to obtain the first sub-sentence group; or
filtering the plurality of sub-sentences in the third sub-sentence group to obtain the fourth sub-sentence group, wherein each of the plurality of sub-sentences included in the fourth sub-sentence group includes one workflow component;
reordering the plurality of sub-sentences in the fourth sub-sentence group to obtain a second sub-sentence group; and
Maes, however, teaches:
reordering the plurality of sub-sentences in the fourth sub-sentence group to obtain the first sub-sentence group; or (P0047, For the example of FIG. 2, the machine translation produces a single output cloud orchestration workflow that includes the following ordered sequence of operations.)
reordering the plurality of sub-sentences in the fourth sub-sentence group to obtain a second sub-sentence group; and (P0047, For the example of FIG. 2, the machine translation produces a single output cloud orchestration workflow that includes the following ordered sequence of operations.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to reorder subsentences. It would have been obvious to combine the references because the input data that represents a sequence of steps to solve a particular problem may not be sufficient to solve the problem and modification may be required by the natural learning generation engine. (Maes P0022)
Brunswig in view of Maes does not specifically teach:
filtering the plurality of sub-sentences in the third sub-sentence group to obtain a fourth sub-sentence group, wherein each of a plurality of sub-sentences included in the fourth sub-sentence group includes one workflow component; and
filtering the plurality of sub-sentences in the third sub-sentence group to obtain the fourth sub-sentence group, wherein each of the plurality of sub-sentences included in the fourth sub-sentence group includes one workflow component;
Suparna, however, teaches:
filtering the plurality of sub-sentences in the third sub-sentence group to obtain a fourth sub-sentence group, wherein each of a plurality of sub-sentences included in the fourth sub-sentence group includes one workflow component; and (P0024, Each word in the natural language statement may be classified, for example, as one of a noun, a verb, an adjective and a preposition, Words that do not fit one of the classifications may be ignored.; P0028, Second word mapper may remove any prepositions from the group of unmapped words.)
filtering the plurality of sub-sentences in the third sub-sentence group to obtain the fourth sub-sentence group, wherein each of the plurality of sub-sentences included in the fourth sub-sentence group includes one workflow component; (P0024, Each word in the natural language statement may be classified, for example, as one of a noun, a verb, an adjective and a preposition, Words that do not fit one of the classifications may be ignored.; P0028, Second word mapper may remove any prepositions from the group of unmapped words.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to filter sub-sentences. It would have been obvious to combine the references because the filtering process is a known technique to yield a predictable result of disregarding words that are not part of potential workflows. (Suparna P0033)
Regarding claim 4 and 13 Brunswig, in view of Maes, in view of Burton, in view of Gao, and further view of Suparna teach claim 2 and 11.
Brunswig does not specifically teach:
determining, by a second encoder-only transformer model of the one or more encoder-only transformer models, whether each sub-sentence of the plurality of sub-sentences in the third sub-sentence group includes one workflow component; and
if the sub-sentence in the third sub-sentence group includes zero workflow component, removing the sub-sentence in the third sub-sentence group, so as to obtain the fourth sub-sentence group.
Maes, however, teaches:
determining, by a second encoder-only transformer model of the one or more encoder-only transformer models, whether each sub-sentence of the plurality of sub-sentences in the third sub-sentence group includes one workflow component; and (P0045, The input sequence for the example depicted in FIG. 1A may be parsed into the following units of text: Migrate; VM-1; from; DataCenter1; to; and DataCenter2. Based on the units of text and other criteria, the natural language processing engine may then encode the units into corresponding tokens and apply the machine translation to these tokens based on the natural language processing model to generate one or multiple cloud orchestration workflows.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to determine workflow component from sub-sentence. It would have been obvious to combine the references because determining workflow component from sub-sentence is a known technique to yield a predictable result of generating a workflow from a sentence. (Maes P0022)
Brunswig in view of Maes, in view of Burton, and further view of Gao does not specifically teach:
if the sub-sentence in the third sub-sentence group includes zero workflow component, removing the sub-sentence in the third sub-sentence group, so as to obtain the fourth sub-sentence group.
Suparna, however, teaches:
if the sub-sentence in the third sub-sentence group includes zero workflow component, removing the sub-sentence in the third sub-sentence group, so as to obtain the fourth sub-sentence group. (P0033, If no matching implementations are found, the noun may marked as blank.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to remove sub-sentences. It would have been obvious to combine the references because the filtering process is a known technique to yield a predictable result of disregarding words that are not part of potential workflows. (Suparna P0033)
Regarding claim 5 and 14 Brunswig, in view of Maes, in view of Burton, in view of Gao, and further view of Suparna teach claim 2 and 11.
Brunswig does not specifically teach:
reordering, by a third encoder-only transformer model of the one or more encoder-only transformer models, the plurality of sub-sentences in the fourth sub-sentence group, wherein an input of the third encoder-only transformer model is two sub-sentences in the fourth sub-sentence group, and an output of the third encoder-only transformer model is information for indicating whether an order of the two sub-sentences is correct.
Maes, however, teaches:
reordering, by a third encoder-only transformer model of the one or more encoder-only transformer models, the plurality of sub-sentences in the fourth sub-sentence group, wherein an input of the third encoder-only transformer model is two sub-sentences in the fourth sub-sentence group, and an output of the third encoder-only transformer model is information for indicating whether an order of the two sub-sentences is correct. (P0047, For the example of FIG. 2, the machine translation produces a single output cloud orchestration workflow that includes the following ordered sequence of operations.; P0049, Translate a sequence of words or tokens describing the input sequence to a corresponding output sequence.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to reorder subsentences. It would have been obvious to combine the references because the input data that represents a sequence of steps to solve a particular problem may not be sufficient to solve the problem and modification may be required by the natural learning generation engine. (Maes P0022)
Regarding claim 6 and 15 Brunswig, in view of Maes, in view of Burton, in view of Gao, and further view of Suparna teach claim 2 and 11.
Brunswig further teaches:
determining the at least one workflow pattern of the second sub-sentence group; for each workflow pattern of the at least one workflow pattern, obtaining the first sub-sentence group by performing, on the second sub-sentence group, at least one of the following: if no sub-sentence for indicating a boundary of the workflow pattern exists in the second sub-sentence group, inserting the workflow pattern boundary indicating sentence of the workflow pattern into the second sub-sentence group; or if no sub-sentence serving as the workflow pattern indicator exists in the second sub-sentence group, inserting the workflow pattern indicator of the workflow pattern into the second sub-sentence group. (P0064, The workflow assistant can also analyze the text to try and predict actions a user may wish to take, or suggest actions that may be helpful to a user. For example, the occurrence of a particular data object or data element associated with the text, or a particular collection of such objects or elements, may often be associated with one, or more, subsequent actions, such as the creation of a new data object, assistant item, or collection.; P0092, The search client can determine whether words, phrases, numbers, or other content of the text analysis results may be associated with, or related to, data objects or data elements used in the architecture and provide search results.; P0093, If a search result is identified as relevant, it can be identified for the user. For example, the name or other identifier of the search result can be displayed to the user.)
Regarding claim 7 and 16 Brunswig, in view of Maes, in view of Burton, in view of Gao, and further view of Suparna teach claim 6 and 15.
Brunswig further teaches:
performing a pattern keyword detection on a plurality of sub-sentences in the second sub-sentence group; and determining the at least one workflow pattern according to at least one pattern keyword that is detected, wherein each of the at least one pattern keyword is configured to indicate a respective workflow pattern of the at least one workflow pattern. (P0064, The workflow assistant can also analyze the text to try and predict actions a user may wish to take, or suggest actions that may be helpful to a user. For example, the occurrence of a particular data object or data element associated with the text, or a particular collection of such objects or elements, may often be associated with one, or more, subsequent actions, such as the creation of a new data object, assistant item, or collection.; P0092, The search client can determine whether words, phrases, numbers, or other content of the text analysis results may be associated with, or related to, data objects or data elements used in the architecture and provide search results.; P0093, If a search result is identified as relevant, it can be identified for the user. For example, the name or other identifier of the search result can be displayed to the user.)
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Brunswig Brunswig, in view of Maes, in view of Burton, in view of Gao, and in further view of Carus et al. (U.S. PG Pub No. 20160350283), hereinafter Carus.
Regarding claim 9 and 18 Brunswig, in view of Maes, in view of Burton, and in further view of Gao teach claim 8 and 10.
Brunswig further teaches:
performing semantic similarity processing to match each sub-sentence in the first sub-sentence group to the pre-defined values in the label dictionary; and (P0092, A data object can be associated with a dictionary template, such that potential keywords or search terms can be extracted from the data object. The search client can determine whether words, phrases, numbers, or other content of the text analysis results may be associated with, or related to, data objects or data elements used in the architecture and provide search results. A matching component can access the search results and text analysis results and, using matching rules, determine whether the elements of the text analysis results should be associated with a corresponding element of the search results.)
Brunswig, in view of Maes, in view of Burton, and in further view of Gao does not specifically teach:
determining a workflow label corresponding to a pre-defined value with a highest similarity score with respect to the sub-sentence in the pre-defined values as the workflow label corresponding to the sub-sentence.
Carus, however, teaches:
determining a workflow label corresponding to a pre-defined value with a highest similarity score with respect to the sub-sentence in the pre-defined values as the workflow label corresponding to the sub-sentence. (P0052, Said terms in the at least two texts are allowed by contributing their respective distances only once and only with their respective highest matching scores, where the at least two texts are ranked for similarity.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to determine semantic similarity based on similarity score. It would have been obvious to combine the references because the measure of semantic similarity of words, phrases, and texts is a known technique to yield a predictable result of obtaining similar words or phrases. (Carus P0004)
Conclusion
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/DANIEL W CHUNG/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
