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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-20 are within the four statutory categories (a process, machine, manufacture, or composition of matter)
As to claim 1:
Step 2A Prong 1: this claim recites the following abstract ideas: transform the event trace into a semantic story using a generated story template (The step to transform event traces to semantic stories using a template is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
Step 2A Prong 2 and 2B: this claim recites the following additional elements:
A system (This limitation functions as mere instructions to implement an abstract idea on a generic computer. (2106.05(f))
Comprising a processor to (This limitation is a directed to generic processors, which is well-understood, routine, conventional activity. (2106.05(f))
Receive an event trace (This limitation is directed to transmitting data to a generic computer which is well-understood, routine, conventional activity. (2106.05(d)(II)(i))
input the semantic story into a fine-tuned model (The step of inputting represents a generic computer function. (2106.05(g))
receive a prediction from the fine-tuned model (This limitation is directed to transmitting data from a generic computer which is well-understood, routine, conventional activity. (2106.05(d)(II)(i))
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception.
As to claim 2:
Step 2A Prong 1: Claim 2, incorporates the rejection of claim 1, and recites the following abstract ideas:
wherein the event trace comprises an ordered set of skills. (The step of event traces comprising of ordered set of skills is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
Step 2A Prong 2 and 2B: this claim recites no additional elements.
As to claim 3:
Step 2A Prong 1: Claim 3, incorporates the rejection of claim 1, and no other abstract ideas:
Step 2A Prong 2 and 2B: this claim recites the following additional elements:
wherein the model comprises a paragraph-based language classifier (This limitation functions as mere instructions to implement an abstract idea on a generic computer. (2106.05(f))
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception.
As to claim 4:
Step 2A Prong 1: Claim 4, incorporates the rejection of claim 1, and recites the following abstract ideas:
extract traces from the event logs; (The step to extract event traces from event logs is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
transform each trace into semantic stories based on the story template; (The step to transform event traces to semantic stories using a template is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
generate the story template based on the extracted traces; (The step of generating templates from trace data is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
Step 2A Prong 2 and 2B: this claim recites the following additional elements:
wherein the processor is to (This limitation is a directed to generic processors, which is well-understood, routine, conventional activity. (2106.05(f))
receive event logs; (This limitation is directed to transmitting data to a generic computer which is well-understood, routine, conventional activity. (2106.05(d)(II)(i))
fine-tune the model based on the semantic stories. (This limitation functions as mere instructions to implement an abstract idea on a generic computer. (2106.05(f))
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception.
As to claim 5:
Step 2A Prong 1: Claim 5, incorporates the rejection of claim 1, and recites the following abstract ideas:
wherein the story template comprises an ordered list of skills connected with a connective word. (The step of template comprising of ordered list of skills is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
Step 2A Prong 2 and 2B: this claim recites no additional elements.
As to claim 6:
Step 2A Prong 1: Claim 6, incorporates the rejection of claim 5, and recites the following abstract ideas:
wherein the connective word comprises an adverb (The step of connective word comprising of an adverb is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
that describes a sequence and a position of the connected skills. (The step of describing a sequence and position is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
Step 2A Prong 2 and 2B: this claim recites no additional elements.
As to claim 7:
Step 2A Prong 1: Claim 7, incorporates the rejection of claim 1, and recites the following abstract ideas:
wherein the features comprise free text features. (The step of features comprising of free text is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
Step 2A Prong 2 and 2B: this claim recites no additional elements.
As to claim 8:
Step 2A Prong 1: Claim 8, incorporates the rejection of claim 1, and no other abstract ideas:
Step 2A Prong 2 and 2B: this claim recites no additional elements.
wherein the prediction comprises a next skill prediction (This limitation functions as mere instructions to implement an abstract idea on a generic computer. (2106.05(f))
As to claim 9:
Step 2A Prong 1: Claim 9 recites no other abstract ideas:
Step 2A Prong 2 and 2B: this claim recites the following additional elements:
extracting, via the processor, traces from the event logs; (Using a generic processor to extract data is mere instructions to apply the exception on a generic computer. (2106.05(f))
generating, via the processor, a story template based on the extracted traces; (Using a generic processor to generate data is mere instructions to apply the exception on a generic computer. (2106.05(f))
transforming, via the processor, each trace into semantic stories based on the story template; (Using a generic processor to transform data is mere instructions to apply the exception on a generic computer. (2106.05(f))
A computer-implemented method, comprising (This limitation functions as mere instructions to implement an abstract idea on a generic computer. (2106.05(f))
receiving, via a processor, event logs; (This limitation is directed to transmitting data to a generic computer which is well-understood, routine, conventional activity. (2106.05(d)(II)(i))
and fine-tuning, via the processor, a model for a prediction task based on the semantic stories. (This limitation functions as mere instructions to implement an abstract idea on a generic computer. (2106.05(f))
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception.
As to claim 10:
Step 2A Prong 1: Claim 10, incorporates the rejection of claim 9, and recites the following abstract ideas:
a detected next activity as a label for each of the samples. (The step of detecting next activity is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
Step 2A Prong 2 and 2B: this claim recites the following additional elements:
wherein fine-tuning the model comprises using the semantic stories as samples (This limitation functions as mere instructions to implement an abstract idea on a generic computer. (2106.05(f))
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception.
As to claim 11:
Step 2A Prong 1: Claim 11, incorporates the rejection of claim 9, and recites the following abstract ideas:
wherein the prediction task comprises a next skill prediction. (This limitation functions as mere instructions to implement an abstract idea on a generic computer.)
Step 2A Prong 2 and 2B: this claim recites no additional elements.
As to claim 12:
Step 2A Prong 1: Claim 12, incorporates the rejection of claim 9, and recites the following abstract ideas:
comprising generating a plurality of story templates and fine-tuning the model using the plurality of story templates, wherein the plurality of story templates comprise different features.
Step 2A Prong 2 and 2B: this claim recites no additional elements.
As to claim 13:
The claim recites similar limitations as corresponding claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 13.
Step 2A Prong 2 and 2B: this claim recites no additional elements.
As to claim 14:
Step 2A Prong 1: Claim 14, incorporates the rejection of claim 9, and recites the following abstract ideas:
wherein the story template comprises an ordered list of skills connected by an adverb (The step of template comprising of ordered list of skills connected by an adverb is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
that describes a sequence and a position of the connected skills. (The step of describing a sequence and position is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
Step 2A Prong 2 and 2B: this claim recites no additional elements.
As to claim 15:
Step 2A Prong 1: Claim 15, incorporates the rejection of claim 9, and recites the following abstract ideas: transforming the current event trace into a semantic story using the generated story template; (The step to transform event traces to semantic stories using a template is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
Step 2A Prong 2 and 2B: this claim recites the following additional elements:
The computer-implemented method (This limitation functions as mere instructions to implement an abstract idea on a generic computer. (2106.05(f))
Comprising a processor to (This limitation is a directed to generic processors, which is well-understood, routine, conventional activity. (2106.05(f))
receiving a current event trace (This limitation is directed to transmitting data to a generic computer which is well-understood, routine, conventional activity. (2106.05(d)(II)(i))
inputting the semantic story into a fine-tuned model (The step of inputting represents a generic computer function. (2106.05(g))
and receiving a next skill prediction from the fine-tuned model. (This limitation is directed to transmitting data from a generic computer which is well-understood, routine, conventional activity. (2106.05(d)(II)(iv))
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception.
As to claim 16:
Step 2A Prong 1: this claim recites the following abstract ideas:
extract traces from the event logs; (The step to extract event traces from event logs is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
transform each trace into semantic stories based on the story template; (The step to transform event traces to semantic stories using a template is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
generate the story template based on the extracted traces; (The step of generating templates from trace data is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
Step 2A Prong 2 and 2B: this claim recites the following additional elements:
A computer program product for training machine learning models, (This limitation functions as mere instructions to implement an abstract idea on a generic computer. (2106.05(f))
the computer program product comprising a computer-readable storage medium having program code embodied therewith, (This limitation is a directed to generic computer-readable medium storing, which is well-understood, routine, conventional activity. (2106.05(d)(II)(i))
the program code executable by a processor to cause the processor to: (This limitation functions as mere instructions to implement an abstract idea on a generic computer. (2106.05(f))
receive event logs; (This limitation is directed to transmitting data to a generic computer which is well-understood, routine, conventional activity. (2106.05(d)(II)(i))
and fine-tune a model for a prediction task based on the semantic stories. (This limitation functions as mere instructions to implement an abstract idea on a generic computer. (2106.05(f))
The claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception.
As to claim 17:
The claim recites similar limitations as corresponding claim 1 and 16. Therefore, the same subject matter analysis that was utilized for claim 1 and 16, as described above, is equally applicable to claim 17.
As to claim 18:
The claim recites similar limitations as corresponding claim 8. Therefore, the same subject matter analysis that was utilized for claim 8, as described above, is equally applicable to claim 18.
As to claim 19:
Step 2A Prong 1: Claim 19, incorporates rejection of claim 16, and recites the following abstract ideas: to generate the story template by connecting an ordered list of skills with a connective word (The step of generating templates by connecting ordered list of skills is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
Step 2A Prong 2 and 2B: this claim recites no additional elements.
As to claim 20:
Step 2A Prong 1: Claim 20, incorporates rejection of claim 16, and recites the following abstract ideas:
generate an additional story template in response (The step of generating a story template is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
to detecting that a trace comprises a different feature. (The step of template detecting a trace is a mental process that can be performed in the human mind with the physical aid of pen and paper.)
Step 2A Prong 2 and 2B: this claim recites no additional elements.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 4, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pasquadibisceglie et al. (A Multi-View Deep Learning Approach for Predictive Business Process Monitoring)
Regarding claim 1, Pasquadibisceglie teaches the following:
A system, comprising a processor to: receive an event trace; (Pasquadibisceglie, Section 1, “Such enterprise information systems commonly generate event logs that collect business process executions stored as traces”)
transform the event trace into a semantic story using a generated story template; (Pasquadibisceglie, Section 4, “The first step is to derive a multi-view input representation from the event log based on prefix traces.”)
input the semantic story into a fine-tuned model; (Pasquadibisceglie, Section 4, “The derived multi-view representation of the extracted event data is then used to feed a supervised deep learning architecture”)
and receive a prediction from the fine-tuned model. (Pasquadibisceglie, Section 4, “supervised deep learning architecture that is trained to predict the next activity of each prefix trace using multi-view inputs.”)
Regarding claim 2, Pasquadibisceglie teaches the following:
The system of claim 1, wherein the event trace comprises an ordered set of skills. (Pasquadibisceglie, Section 1, “Each trace is recorded as an ordered list of events, which are activities invoked at a specific timestamp by a process execution from the beginning of its execution to the end.")
Regarding claim 4, Pasquadibisceglie teaches the following:
The system of claim 1, wherein the processor is to: receive event logs; (Pasquadibisceglie, Section 1, “Such enterprise information systems commonly generate event logs that collect business process executions stored as traces”)
extract traces from the event logs; (Pasquadibisceglie, Section 3, “The pattern to make these predictions can be learned by resorting to a supervised predictive analytic with a labeled dataset that is extracted from the given event log L. The dataset comprises all the prefixes of all the traces in L, labelled with their next-activity.”)
generate the story template based on the extracted traces; (Pasquadibisceglie, Section 4, “The first step is to derive a multi-view input representation from the event log based on prefix traces.”)
transform each trace into semantic stories based on the story template; (Pasquadibisceglie, Section 4, “Each attribute generates a different view for a trace. Our idea is to consider simultaneously different views for each trace, so as to exploit the whole information available in the log.”)
and fine-tune the model based on the semantic stories (Pasquadibisceglie, Section 4, “The proposed multi-view scheme produces a multi-input representation of prefix traces that can be processed by a neural network, in order to learn a predictive model for next-activity prediction.”)
Regarding claim 7, Pasquadibisceglie teaches the following:
The system of claim 1, wherein the features comprise free text features. (Pasquadibisceglie, Section 4, “In particular, we consider both categorical attributes (e.g., the activities and the resources) and numerical attributes (e.g., the timestamp) to represent each event.”)
Regarding claim 8, Pasquadibisceglie teaches the following:
The system of claim 1, wherein the prediction comprises a next skill prediction. (Pasquadibisceglie, Section 3, “The next-activity prediction is a predictive process mining task [44] that learns a pattern predicting the executing activity of the next event in a running trace”)
Regarding claim 9, Pasquadibisceglie teaches the following:
receiving, via a processor, event logs (Pasquadibisceglie, Section 1, “Such enterprise information systems commonly generate event logs that collect business process executions stored as traces”)
extracting, via the processor, traces from the event logs (Pasquadibisceglie, Section 3, “The pattern to make these predictions can be learned by resorting to a supervised predictive analytic with a labeled dataset that is extracted from the given event log L. The dataset comprises all the prefixes of all the traces in L, labelled with their next-activity.”)
generating, via the processor, a story template based on the extracted traces (Pasquadibisceglie, Section 4, “The first step is to derive a multi-view input representation from the event log based on prefix traces.”)
transforming, via the processor, each trace into semantic stories based on the story template; (Pasquadibisceglie, Section 4, “Each attribute generates a different view for a trace. Our idea is to consider simultaneously different views for each trace, so as to exploit the whole information available in the log.”)
and fine-tuning, via the processor, a model for a prediction task based on the semantic stories. (Pasquadibisceglie, Section 4, “The proposed multi-view scheme produces a multi-input representation of prefix traces that can be processed by a neural network, in order to learn a predictive model for next-activity prediction.”)
Regarding claim 10, Pasquadibisceglie teaches the following:
wherein fine-tuning the model comprises using the semantic stories as samples (Pasquadibisceglie, Section 3, “The prediction is performed by considering the prefix traces, i.e., the sequence of multi-view information enclosed in past events recorded from the beginning of the current trace.”)
and a detected next activity as a label for each of the samples (Pasquadibisceglie, Section 4, “The derived multi-view representation of the extracted event data is then used to feed a supervised deep learning architecture that is trained to predict the next activity of each prefix trace”)
Regarding claim 11, Pasquadibisceglie teaches the following:
The computer-implemented method of claim 9, wherein the prediction task comprises a next skill prediction. (Pasquadibisceglie, Section 3, “The next-activity prediction is a predictive process mining task [44] that learns a pattern predicting the executing activity of the next event in a running trace”)
Regarding claim 12, Pasquadibisceglie teaches the following:
comprising generating a plurality of story templates and fine-tuning the model using the plurality of story templates, (Pasquadibisceglie, Section 1, “We propose to process multiple views through a flexible multi-input deep learning architecture that appropriately shares information among views.”)
wherein the plurality of story templates comprise different features. (Pasquadibisceglie, Section 1, “Specifically, we handle multiple characteristics of an event as multiple views of the same trace.”)
Regarding claim 15, Pasquadibisceglie teaches the following:
receiving a current event trace; (Pasquadibisceglie, Section 1, “Such enterprise information systems commonly generate event logs that collect business process executions stored as traces”)
transforming the current event trace into a semantic story using the generated story template; (Pasquadibisceglie, Section 4, “The first step is to derive a multi-view input representation from the event log based on prefix traces.”)
inputting the semantic story into a fine-tuned model; (Pasquadibisceglie, Section 4, “The derived multi-view representation of the extracted event data is then used to feed a supervised deep learning architecture”)
and receiving a next skill prediction from the fine-tuned model. (Pasquadibisceglie, Section 4, “supervised deep learning architecture that is trained to predict the next activity of each prefix trace using multi-view inputs.”)
Regarding claim 16, Pasquadibisceglie teaches the following:
receive event logs; (Pasquadibisceglie, Section 1, “Such enterprise information systems commonly generate event logs that collect business process executions stored as traces”)
extract traces from the event logs; (Pasquadibisceglie, Section 3, ““The pattern to make these predictions can be learned by resorting to a supervised predictive analytic with a labeled dataset that is extracted from the given event log L. The dataset comprises all the prefixes of all the traces in L, labelled with their next-activity.”)
generate a story template based on the extracted traces; (Pasquadibisceglie, Section 4, “The first step is to derive a multi-view input representation from the event log based on prefix traces.”)
transform each trace into semantic stories based on the story template; (Pasquadibisceglie, Section 4, “Each attribute generates a different view for a trace. Our idea is to consider simultaneously different views for each trace, so as to exploit the whole information available in the log.”)
and fine-tune a model for a prediction task based on the semantic stories. (Pasquadibisceglie, Section 4, “The proposed multi-view scheme produces a multi-input representation of prefix traces that can be processed by a neural network, in order to learn a predictive model for next-activity prediction.”)
Regarding claim 17, Pasquadibisceglie teaches the following:
further comprising program code executable by the processor to: receive an event trace; (Pasquadibisceglie, Section 1, “Such enterprise information systems commonly generate event logs that collect business process executions stored as traces”)
transform the event trace into a semantic story using a generated story template; (Pasquadibisceglie, Section 4, “The first step is to derive a multi-view input representation from the event log based on prefix traces.”)
input the semantic story into a fine-tuned model; (Pasquadibisceglie, Section 4, “The derived multi-view representation of the extracted event data is then used to feed a supervised deep learning architecture”)
and receive a prediction from the fine-tuned model. (Pasquadibisceglie, Section 4, “supervised deep learning architecture that is trained to predict the next activity of each prefix trace using multi-view inputs.”)
Regarding claim 18, Pasquadibisceglie teaches the following:
wherein the prediction comprises a next skill prediction. (Pasquadibisceglie, Section 3, “The next-activity prediction is a predictive process mining task [44] that learns a pattern predicting the executing activity of the next event in a running trace”)
Regarding claim 20, Pasquadibisceglie teaches the following:
further comprising program code executable by the processor to generate an additional story template (Pasquadibisceglie, Section 4, “Each attribute generates a different view for a trace. Our idea is to consider simultaneously different views for each trace, so as to exploit the whole information available in the log.”)
in response to detecting that a trace comprises a different feature. (Pasquadibisceglie, Section 4, “In this work we propose a richer representation that takes into account different perspectives for each trace. In particular, we consider both categorical attributes (e.g., the activities and the resources) and numerical attributes (e.g., the timestamp) to represent each event”)
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) 3 are rejected under 35 U.S.C. 103 as being unpatentable over Pasquadibisceglie et al. (A Multi-View Deep Learning Approach for Predictive Business Process Monitoring) in view of Zaramoodi et al. (US 20230098783 A1)
Regarding claim 3, Pasquadibisceglie in view of Zaramoodi teaches:
Regarding claim 3, Pasquadibisceglie teaches all the elements of claim 1 outlined above.
As outlined above, Pasquadibisceglie teaches all elements of claim 1. Pasquadibisceglie does not explicitly disclose:
The system of claim 1, wherein the model comprises a paragraph-based language classifier
However, Pasquadibisceglie in view of Zaramoodi teaches the limitation:
The system of claim 1, wherein the model comprises a paragraph-based language classifier (Zaramoodi, Col. 12, Paragraph 0117, “The language model 402 can be a pre-trained language model such as BERT, ROBERTa, and ALBERT, etc.”)
Pasquadibisceglie in view of Zaramoodi are analogous art because they are from the same field of endeavor and their application relates to training predictive models using collected data
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing data of the claimed invention, to modify the invention of Pasquadibisceglie to incorporate Zaramoodi’s technique of using a model such as BERT, ROBERTa, and ALBERTa. One would have been motivated to make such a combination in order to solve the problem of expense and difficulty that comes with training a natural language processing model (Zaramoodi, Col. 3, Paragraph 0031, “Because of the expense and difficulty of training a model, such as a natural language processing model, conventional artificial intelligence-based training approaches often utilize a fine-tuning framework to train a model for various services or tasks including intent detection, natural language to query language conversion, or any other NLP or chatbot related service.”)
Claim(s) 5, 6, 13, 14, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Pasquadibisceglie et al. (A Multi-View Deep Learning Approach for Predictive Business Process Monitoring) in view of Mairttha et al. (Integrating a spoken dialogue system, nursing records, and activity data collection based on smartphones - ScienceDirect)
Regarding claim 5, Pasquadibisceglie teaches the following:
The system of claim 1, wherein the story template (Pasquadibisceglie, Section 3, “The first step is to derive a multi-view input representation from the event log based on prefix traces.”)
As outlined above, Pasquadibisceglie teaches generating a story template. Pasquadibisceglie does not explicitly disclose:
comprises an ordered list of skills connected with a connective word.
However, Pasquadibisceglie in view of Mairttha teaches the limitation:
comprises an ordered list of skills connected with a connective word. (Mairittha, Section 4.1, “For example, the user may use some common signal words with present continuous like now, at the moment, this week to current record activities, or mention the time when the action was completed with an adverb or adverbial phrase like yesterday and two hours ago to record past activities.”)
Pasquadibisceglie in view of Mairttha are analogous art because they are from the same field of endeavor and their application relates to predicting next events using data from user actions.
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing data of the claimed invention, to modify the invention of Pasquadibisceglie to incorporate Mairttha’s technique of using adverbs or adverbial phrases to connect ordered list of skills. One would have been motivated to make such a combination in order to solve the problem of distinguishing lexical and grammatical properties of words in order to classify activities (Mairttha, Section 4, “distinguish additional lexical and grammatical properties of words” and “to classify and extract activity labels and care records from user utterances”).
Regarding claim 6, Pasquadibisceglie in view of Mairttha teaches the elements of claim 5 as outlined above, and further teaches:
The system of claim 5, wherein the connective word comprises an adverb that describes a sequence and a position of the connected skills. (Mairittha, Section 4.1, “For example, the user may use some common signal words with present continuous like now, at the moment, this week to current record activities, or mention the time when the action was completed with an adverb or adverbial phrase like yesterday and two hours ago to record past activities.”)
Claim 13 recites similar limitations as corresponding claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 13.
Claim 14 recites similar limitations as corresponding claim 5 and 6. Therefore, the same subject matter analysis that was utilized for claim 5 and 6, as described above, is equally applicable to claim 14.
Claim 19 recites similar limitations as corresponding claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 13.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AYUSH MALPEDDI whose telephone number is (571)270-0244. The examiner can normally be reached 7:30am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at 5712723768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AYUSH REDDY MALPEDDI/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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