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 .
DETAILED ACTION
This office action is in response to the claims filed on 03/22/2024.
Claims 1-7 are presented for examination.
Priority
The following claimed benefit is acknowledged: the instant application, filed 03/22/2024 claims priority from foreign application PCT/JP2021/035045, filed 09/24/2021.
Claim Interpretation
6. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
7. The claims 1-5 in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skills in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non- structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
8. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are :
-“ first analysis unit” in claim 1,
“second analysis unit” in claims 1, 3, 4
“selection unit” in claims 1, 3, 5
“rule creation unit” in claims 1, 3
-“summary sentence generating unit”, output unit” in claims 2
-“first acquisition unit”, “second acquisition unit”, “storage unit” in claim 4.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-
AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C.
112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/those being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recite sufficient structure to perform the claimed function so as to avoid it/them being interpreted under
35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AlA), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
9. Claims 1-5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AlA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AlA the applicant regards as the invention.
The examiner considers Claims 1-5 invoking 112(f) having “generic placeholders” performing functional limitations (“calculates first feature amounts, calculates a second feature amount, selects a possible message, creates an identification rule, generates a summary, a third feature amount, reselects the possible message, creates a new identification rule, acquires a plurality of event messages, stores event messages, selects the possible message”).
However the claims are rejected under 112 second paragraph as being indefinite because the specification fails to clearly specifies the corresponding structure to the claimed function where the identification of the corresponding structure is required (37 CFR 1.105). NO where in specification describe the structure of the unit. The specification only describes performances of “first analysis unit”, “second analysis unit”, “selection unit”, “rule creation unit”, “summary sentence generating unit”, output unit”, “first acquisition unit”, “second acquisition unit”, “storage unit”. However, the specification are not described what are the structure of “first analysis unit”, “second analysis unit”, “selection unit”, “rule creation unit”, “summary sentence generating unit”, output unit”, “first acquisition unit”, “second acquisition unit”, “storage unit”.
Claim limitations -“ first analysis unit” in claim 1, “second analysis unit” in claims 1, 3, 4, “selection unit” in claims 1, 3, 5, “rule creation unit” in claims 1, 3
“summary sentence generating unit”, output unit” in claims 2, “first acquisition unit”, “second acquisition unit”, “storage unit” in claim 4, invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the functions:
calculates first feature amounts,
calculates a second feature amount,
selects a possible message,
creates an identification rule,
generates a summary,
calculate a third feature amount,
reselects the possible message,
creates a new identification rule,
acquires a plurality of event messages,
stores event messages,
selects the possible message”
Therefore, claims 1-5 are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skills in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§608.01(o) and 2181.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
In the instant case, the claims are directed to apparatus (claims 1-5), method (claim 6) and non-transitory computer-readable storage medium (claim7). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A analysis:
Based on the claims being determined to be within of the four categories (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically the abstract idea of “Mental Processes/Concepts performed in the human mind (including an observation, evaluation, judgment, opinion)”.
The claim 1 recites:
Step 2A: prong 1 analysis:
-“calculates first feature amounts indicating features of event messages” this is a mental process, the human mind can calculate the first feature amounts of the messages, for example the human can determine that a particular feature is importance more than other feature in the message, (observation/Evaluation).
-“that calculates a second feature amount indicating a feature of a text including information indicating an intention of a user regarding identification of a failure in the target system” this is a mental process, the human mind can calculate the second feature amounts of the messages, for example the human can determine that a particular feature is importance more than other feature in the message, (observation/Evaluation).
“selects a possible message corresponding to the intention of the user from the event messages on a basis of a similarity between each of the first feature amounts and the second feature amount;”. This is a mental process, the human mind can compare how similar the first and second feature amounts are in order to determine the intention of the user to select a possible message for the user.
-“ creates an identification rule for identifying a failure in the target system from the event messages on a basis of the possible message and the text.” This is a mental process, the human mind can create/design the rule/policy based on the message and the text, for example, the human can design the particular condition to determine if a received message is scammed, observation/Evaluation).
Step 2A: Prong 2 analysis:
-“a first analysis unit”, “a second analysis unit”, “ a selection unit”, “a rule creation unit” The additional limitations are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B analysis:
-“a first analysis unit”, “a second analysis unit”, “ a selection unit”, “a rule creation unit” The additional limitations are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
The claim 2 recites:
Step 2A: prong 1 analysis:
-“ that generates a summary sentence of the event messages on a basis of the first feature amounts;” this is a mental process, the human mind can rewrite the summary sentence of message based on the feature, for example, the human can write the summary of the sentence based on the particular feature, (observation/Evaluation).
Step 2A: Prong 2 analysis:
-“ a summary sentence generation unit” The additional limitations are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
-“ an output unit that outputs the summary sentence and the identification rule for presentation to the user“ These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data outputing. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data outputing to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
Step 2B analysis:
“ a summary sentence generation unit” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself.
-“ an output unit that outputs the summary sentence and the identification rule for presentation to the user“ These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data ouputting. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of outputting data to a judicial exception do not amount to significantly more than the judicial exception itself.
The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The claim 3 recites:
Step 2A: prong 1 analysis:
-“wherein in a case where the text including the information indicating the intention of the user is corrected”,
-“ calculates a third feature amount indicating a feature of the corrected text” this is a mental process, the human can calculate the third feature of the text, as the human can determine which feature is importance in the text, (observation/Evaluation).
“reselects the possible message from the event messages on a basis of a similarity between each of the first feature amounts and the third feature amount” this is a mental process, the human can calculate the third feature of the text, as the human can decide when the intention of the user is corrected and compare the first and third feature amounts to select a different message.
-“ creates a new identification rule on a basis of the reselected possible message and the corrected text” this is a mental process, the human can create new rule on the selected message and the text, for example, the human can design the rule to identify the scammed based on the message and text, (observation/Evaluation).
-“and updates the identification rule created before the correction of the text with the new identification rule.” this is a mental process, the human can create/update the new rule on the selected message and the text, for example, the human can design the rule to identify the scammed based on the message and text, (observation/Evaluation).
Step 2A: Prong 2 analysis:
-“ the second analysis unit”, “the selection unit further” and the rule creation unit further” The additional limitations are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B analysis:
-“ the second analysis unit”, “the selection unit further” and the rule creation unit further” The additional limitations are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
The claim 4 recites:
Step 2A: Prong 2 analysis:
-“ a first acquisition unit”, “a second acquisition unit that” The additional limitations are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
- “ that acquires a plurality of event messages from a storage unit that stores event messages output from a device or an application included in the target system, and passes the plurality of event messages to the first analysis unit;”, “acquires as the text, a natural language input by the user and passes the text to the second analysis unit.” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
Step 2B analysis:
-“ a first acquisition unit”, “a second acquisition unit that” The additional limitations are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
that acquires a plurality of event messages from a storage unit that stores event messages output from a device or an application included in the target system, and passes the plurality of event messages to the first analysis unit;”, “acquires as the text, a natural language input by the user and passes the text to the second analysis unit.” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself.
The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The claim 5 recites:
Step 2A: prong 1 analysis:
-“ selects the possible message by calculating a cosine similarity between each of the first feature amounts and the second feature amount and extracting an event message having a first feature amount having a highest similarity to the second feature amount.” This is a mathematical concept (the cosine similarity) and a mental process. The human can extract the event message based on the similarity of the first feature and second feature, (observation/Evaluation).
Step 2A: Prong 2 analysis:
-“ the selection unit” The additional limitations are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B analysis:
-“ the selection unit” The additional limitations are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
The claim 6 is rejected for the same the reason as the claim 1 since these claims recite the same limitation.
The claim 7 is rejected for the same the reason as the claim 1 since these claims recite the same limitation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 6, 7 are rejected under 35 U.S.C. 103 as being unpatentable over Morimura et al. (Pub. No. US 20110209010– hereinafter, Morimura) in view of Joy et al . (Pub. No. US 20140280766– hereinafter, Joy) and further in view of Lo et al. ``(Pub. No. US 20200379776 – hereinafter, lo).
Regarding claim 1, Morimura teaches a rule creation apparatus comprising (Morimura, [Par.0003], “Further, in Non-Patent Literature 2, there is disclosed a technique, which uses an expert system-based inference engine to rapidly determine the root cause of a failure by making rules for pairing up root causes that are inferred from a combination of the event information by this event correlation technique and events at the time of a failure.”. Examiner’s note, the rule is made based on the combination of the event information and fail of event at the time). and a rule creation unit that creates an identification rule for identifying a failure in the target system from the event messages on a basis of the possible message and the text (Morimura, [Par.0003], “Further, in Non-Patent Literature 2, there is disclosed a technique, which uses an expert system-based inference engine to rapidly determine the root cause of a failure by making rules for pairing up root causes that are inferred from a combination of the event information by this event correlation technique and events at the time of a failure.”. Examiner’s note, the rule is made based on the combination of the event information and fail of event at the time).
However, Morimura does not teaches a first analysis unit that calculates first feature amounts indicating features of event messages acquired from a target system, a second analysis unit that calculates a second feature amount indicating a feature of a text including information indicating an intention of a user regarding identification of a failure in the target system, selection unit that selects a possible message corresponding to the intention of the user from the event messages on a basis of a similarity between each of the first feature amounts and the second feature amount.
On the other hand, Joy teaches a first analysis unit that calculates first feature amounts indicating features of event messages acquired from a target system (Joy, [Par.0010], “In a different example, a system for event state management in stream processing includes an input event managing unit, a batch event storage, an event batch write managing unit, and an event batch read managing unit. The input event managing unit is configured to create a hatch of events from a plurality of input events. The batch is associated with a state and is to be processed in one or more stages. The hatch event storage is configured to store the batch of events.” And [Par.0046], “Referring back to FIG. 6, the input event managing unit 602 in this example may also keep track of the state associated with each batch that has been created and is to be processed in each stage of stream processing. As mentioned before, the state may be any piece of information that needs to be saved in persistent store, e.g., the batch state storage 610 in this example, aggregated over time, and referred back later during the course of stream processing. FIG. 8 is an exemplary transition diagram of batch state in a source stage according to an embodiment of the present teaching, in this example, the state may be maintained in the batch state storage 610, e.g., ZooKeeper, separate from the batch event storage 608, e.g., HBase, to allow better coordination among multiple tasks.” Examiner’s note, the input manage unit configure to create the batch for event (name of the event) that is corresponding to the first analysis unit calculate the feature amount indicate the event message).
a second analysis unit that calculates a second feature amount indicating a feature of a text including information indicating an intention of a user regarding identification of a failure in the target system Joy, [Par.0046, 0051], “[0046], Referring back to FIG. 6... FIG. 8 is an exemplary transition diagram of batch state in a source stage according to an embodiment of the present teaching, in this example, the state may be maintained in the batch state storage 610, e.g., ZooKeeper, separate from the batch event storage 608, e.g., HBase, to allow better coordination among multiple tasks. ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. A new batch is started in the "started" state. Each event is fetched from an external source, tagged with the current batch-id, persisted in HBase and then acknowledged to the external source immediately. After that, the event is emitted to the next processing stage for execution.“[0051], In one example, the sub-state for all processing stages within a topology may be maintained as a ZooKeeper state hierarchy as shown in FIG. 10. Under the state hierarchy maintained for a topology, each spout or bolt maintains the state for its currently active batches as well as a list of completed batches. Each active batch keeps track of the current retry attempt being made by the tasks within that processing stage. Once all the tasks of a stage finish a batch attempt, that batch is promoted to "completed" state for that stage. Once all the stages complete a batch, that batch is promoted to "committed" state. Any batch failure before commit may reset its state to "failed" and create a fresh retry attempt to be initiated from the spout. In FIG. 10, the state hierarchy shows five batches having batch id 18, 19, 20, 21, and 22, and three stages: spout 1, bolt 1, and bolt 2. The state and/or sub-states associated with each batch are recorded. For example, the state of batch 18 is recorded as the latest committed hatch because all the stages have completed processing this batch. For those batches that have not been committed yet, sub-states associated with each batch are recorded on a per stage basis. For example, batches 19 and 21 have a sub-state of "complete" in spout 1 as spout 1 has finished processing those batches. Batch 22 has the sub-state "started" in spout 1 as spout 1 has just started retrieving a fresh batch of input events. Batch 20, instead, has a "replay" sub-state in spout 1, and its number of attempts to replay may be also recorded. Optionally, the physical nodes of a cluster that process batch 20 may be also recorded. Similarly, the sub-states of each uncommitted batch in each processing stage, e.g., bolt 1 and bolt 2, are also recorded in this example. For example, batch 20 has a sub-state of "failed" in bolt 1, indicating a processing failure without being replayed yet. As described above, all the data information related to state/sub-states of each batch may be dynamically updated and recorded in the form of, for example, tables shown in FIG. 9, in a persistent storage, e.g., the batch event storage 608, for future reference.” Examiner’s note, Zookeeper keep track or determine the stage of each batch event, whether the batch event is completed or fail.”).
Morimura and Joy are analogous in arts because they have the same field of endeavor of analyze the feature based on the received event or message.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the rule creation apparatus, as taught by Morimura, to include the a first analysis unit that calculates first feature amounts indicating features of event messages acquired from a target system, a second analysis unit that calculates a second feature amount indicating a feature of a text including information indicating an intention of a user regarding identification of a failure in the target system, as taught by Joy. The modification would have been obvious because one of the ordinary skills in art would be motivated to helps to reduce the overhead of tracking them individually and maintaining transaction semantics at each stage, (Joy, [Par.0056], “FIG. 12 is an exemplary sequence diagram illustrating a process of event state management in a normal scenario according to an embodiment of the present teaching in operation, at step 1202, a source stage (HTSpout) gets a first event to be processed from an external source (SSource). At step 1204, the source stage (HTSpout) creates a new batch of events and generates a batch id for it. The state associated with the newly created batch is then updated to "started" and recorded in a persistent storage (ZooKeeper at step 1206. The received event is persisted in another persistent storage (HBASE) at step 1208. The source stage (HTSpout) then acknowledges the receiving of the event with the external source (SSource) at step 1210. The event is then emitted to the processing stages (TransactionalBolts) with its batch id at step 1212. Steps 1202-1212 are repeated over time. Based on the predetermined duration of each batch, at step 1214, once the source stage (HSpout) determines that the received events have reached the batch boundary, it sends a batch finish signal to the processing stages (TransactionalBolts) at step 1216. It is understood that events may be still processed individually as they arrive at the processing stages (TransactionalBolts), but batching helps to reduce the overhead of tracking them individually and maintaining transaction semantics at each stage.”).
However, neither Morimura nor joy teaches selection unit that selects a possible message corresponding to the intention of the user from the event messages on a basis of a similarity between each of the first feature amounts and the second feature amount,
On the other hand, Lo teaches selection unit that selects a possible message corresponding to the intention of the user from the event messages on a basis of a similarity between each of the first feature amounts and the second feature amount (Lo, [Par.0006], “The method further includes computing, by the event management system, a similarity score between the second event and each first event of the plurality of first events, the similarity score computed using i) the respective parameter values used to generate the second event and generate the first event and ii) the registration data of the second event and the historical registration data of the first event. The method further includes selecting, by the event management system, for the second event, a subset of the plurality of first events based on the similarity score exceeding a threshold similarity score value, and generating, by the event management system, for the second event, a projected number of entities based on i) the registration data of the second event and ii) the historical registration data of one or more first events included in the selected subset.);
Morimura, Joy and Lo are analogous in arts because they have the same field of endeavor of analyze the feature based on the received event or message.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the rule creation apparatus, as taught by Morimura, to include the selection unit that selects a possible message corresponding to the intention of the user from the event messages on a basis of a similarity between each of the first feature amounts and the second feature amount, as taught by Lo. The modification would have been obvious because one of the ordinary skills in art would be motivated to helps to improve the computer resource allocation, (Lo, [Par.0006], “According to one aspect, methods and systems for improving computer resource allocation by prioritization of computer executable events based on dynamically changing entity data. The method includes maintaining, by an event management system including one or more processors, for each first event of a plurality of first events managed by the event management system, historical registration data. The historical registration data identifies, for each time of a plurality of times, a number of entities at the time. Each first event has respective parameter values of one or more parameters used to generate the first event. The method further includes identifying, by the event management system, for a second event that has an open registration status, respective parameter values of one or more parameters used to generate the second event, and registration data identifying, for each time of a plurality of times since registration for the second event was opened, a current number of entities at the time.”).
Regarding claim 2, Morimura teaches the rule creation apparatus according to claim 1, further comprising: a summary sentence generation unit that generates a summary sentence of the event messages on a basis of the first feature amounts (Morimura, [Par.0075-0085], “[0075], FIG. 27 shows the event management information 106. The event management information comprises the following information. [0079] Event ID: This is information for identifying the event management information. [0080] Event detection time: Denotes the time at which the event was detected…[0085]Furthermore, the event management information 106 is created by the processing of the management program 105 in accordance with receiving event-related information (hereinafter, called event reception information), which the monitoring target information processing apparatus creates in response to an event detection and sends to the management system.” Examiner’s note, the event management information is generated by the management program (summary sentence generation unit )based on the event ID of the received event. ;
and an output unit that outputs the summary sentence (Morimura, [Fig. 27, par.0002], “Because event correlation technique uses correlation with the event information notified at the time of the failure to infer the root cause, this technique has been used in diagnosing failures in network system for a long time.” And [Par.0085], “Furthermore, the event management information 106 is created by the processing of the management program 105 in accordance with receiving event-related information (hereinafter, called event reception information), which the monitoring target information processing apparatus creates in response to an event detection and sends to the management system.” Examiner’s note, the monitoring target information processing apparatus display/output the event management information (sentence summary).)
and the identification rule for presentation to the user (Morimura, [Par.0099-0106, 0130, Fig.3], “In a case where the above topology is assumed, a specification to the effect "file server and client computer using file server-exported file system" is made in the condition part as the topology condition. The following is further specified in the condition part as the condition set. [0100] As condition R1-21, the status of the file server-exported file system becomes error. [0101] As condition R1-22, the status of the client machine network drive becomes error. [0102] Furthermore, in addition to the error status comprising the fact that at the least either the apparatus or the component is unable to be used, this error status may also comprise a general error status. For the other cases explained hereinbelow as well, unless otherwise stated, it will be supposed that the error status has the above-described meaning.[0103] The conclusion part specifies the following for this condition set. [0104] The cause location information is the "file server"-exported file system. [0105] The detailed message is "cause is failure of file server-exported file system". [0106] The following is specified in the analysis rule information 104-R2 of FIG. 3 as an example of an analysis rule for handling a failure in the server file system. [0107] In rule identification information R2-1, since a topology condition of "none" is set in the condition part as the application-destination topology, "R2" is applied at the least to all the management-targeted file servers. [0108] The condition that makes up the condition part specifies the status of the "computer file system" as error. Then, the following is specified in conclusion part R2-3 as the conclusion corresponding to this condition set. [0109] The cause location information is the "computer file system". [0110] The detailed message is "cause is failure of computer file system". And [Par.0130], “[0130] Message: A message for displaying the cause apparatus and the root cause component obtained by applying the analysis rule information identified by the applied rule ID to either the monitoring target information processing apparatus or a component of this apparatus specified by the applied component ID.” Examiner’s note, the root cause is displayed.).
The claim 6 is rejected for the same reason as the claim 1, since these claims recites the same limitations.
The claim 7 is rejected for the same reason as the claim 1, since these claims recites the same limitations.
Claim(s)3 are/is rejected under 35 U.S.C. 103 as being unpatentable over Morimura et al. (Pub. No. US 20110209010– hereinafter, Morimura) in view of Joy et al . (Pub. No. US 20140280766– hereinafter, Joy) and further in view of Lo et al. (Pub. No. US 20200379776 – hereinafter, lo) and further in view of Lo et al. ``(Pub. No. US 20200379776 – hereinafter, lo) and further in view of Brandow et al. (Patent. No. US 6064957 – hereinafter, Brandow).
Regarding claim 3, Morimura teaches the rule creation apparatus according to claim 1, however, Morimura does not teach wherein in a case where the text including the information indicating the intention of the user is corrected, the second analysis unit calculates a third feature amount indicating a feature of the corrected text, the selection unit further reselects the possible message from the event messages on a basis of a similarity between each of the first feature amounts and the third feature amount, and the rule creation unit further creates a new identification rule on a basis of the reselected possible message and the corrected text, and updates the identification rule created before the correction of the text with the new identification rule.
On the other hand, LO teaches the selection unit further reselects the possible message from the event messages on a basis of a similarity between each of the first feature amounts and the third feature amount (Lo, [Par.0006], “The method further includes computing, by the event management system, a similarity score between the second event and each first event of the plurality of first events, the similarity score computed using i) the respective parameter values used to generate the second event and generate the first event and ii) the registration data of the second event and the historical registration data of the first event. The method further includes selecting, by the event management system, for the second event, a subset of the plurality of first events based on the similarity score exceeding a threshold similarity score value, and generating, by the event management system, for the second event, a projected number of entities based on i) the registration data of the second event and ii) the historical registration data of one or more first events included in the selected subset.);
Morimura, Joy and Lo are analogous in arts because they have the same field of endeavor of analyze the feature based on the received event or message.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the rule creation apparatus, as taught by Morimura, to include the selection unit further reselects the possible message from the event messages on a basis of a similarity between each of the first feature amounts and the third feature amount as taught by Lo. The modification would have been obvious because one of the ordinary skills in art would be motivated to helps to improve the computer resource allocation, (Lo, [Par.0006], “According to one aspect, methods and systems for improving computer resource allocation by prioritization of computer executable events based on dynamically changing entity data. The method includes maintaining, by an event management system including one or more processors, for each first event of a plurality of first events managed by the event management system, historical registration data. The historical registration data identifies, for each time of a plurality of times, a number of entities at the time. Each first event has respective parameter values of one or more parameters used to generate the first event. The method further includes identifying, by the event management system, for a second event that has an open registration status, respective parameter values of one or more parameters used to generate the second event, and registration data identifying, for each time of a plurality of times since registration for the second event was opened, a current number of entities at the time.”).
However, neither Morimura nor Lo teaches wherein in a case where the text including the information indicating the intention of the user is corrected, the second analysis unit calculates a third feature amount indicating a feature of the corrected text , and the rule creation unit further creates a new identification rule on a basis of the reselected possible message and the corrected text, and updates the identification rule created before the correction of the text with the new identification rule.
On the other hand, Brandow teaches wherein in a case where the text including the information indicating the intention of the user is corrected (Brandow, [Col. 2, lines 41-54], “FIG. 1 shows a block diagram of a training unit 10 that is used in this invention to develop a set of correction rules. In the training unit 10, text data generated from a speech recognition system is collected and inputted to a text aligner 12. In this invention, the speech recognition text data can be generated from any type of speech recognition system such as an isolated-word speech recognition system or a continuous speech recognition system. A corresponding true transcription of the speech recognition text data is also collected and inputted to the text aligner 12. The corresponding true transcription of text data is a manually verified transcription of the exact voice input that was inputted to the speech recognition system.”
the second analysis unit calculates a third feature amount indicating a feature of the corrected text (Brandow, [Col. 4, lines 63-68 and Col.5, lines 1-23], , “The revised rules generated by the rule generator 18 are stored in a correction rules database 28. The rules in the correction rules database 28 are then used by a text corrector 30. In particular, the text corrector 30 applies the revised rules to the speech recognition text data and corrects the text according to the rules. The text aligner 12 aligns the corrected text with the corresponding true transcription of text data. Eventually, more rules are developed by the rule generator 18 and subsequently validated or invalidated by observing their applicability across the corpus of text data. Again the invalidated rules are revised and then applied to the speech recognition text.” Examiner’s note, text aligner aligns the position of the corrected text.).
and the rule creation unit further creates a new identification rule on a basis of the reselected possible message and the corrected text (Brandow, [Col. 5, lines 33-37 ], “FIG. 2 shows a flow chart summarizing the steps used to obtain the correction rules. The processing steps begin at 32 where the text data generated from a speech recognition system is collected. Next, the corresponding true transcription of the text data is collected at 34. The text corrector 30 corrects the speech recognition text data according to any rules developed by the rule generator 18 at 36. Initially, there are no rules and the speech recognition text data passes to the text aligner 12. Then the text aligner 12 aligns the text data with the corresponding true transcription of text data at 38. All of the misaligned sections are sent to the statistical collector 14 which counts the number of times that the misaligned sections have occurred in the training sample of parallel text at 40. The misaligned sections along with the number of times that the misalignment has occurred in the training sample of parallel text are then stored in the trainable database 16 at 42. The rule generator 18 uses the perceived differences in alignment between the speech recognition text data and the corresponding true transcription of text data to derive a set of context-free rules at 44. The applicability of the preliminary replacement rules are observed across the training collection at 46. If all of the rules are determined to be valid at 48 or no further progress can be made at 50, then the rules are ready to be put into the production phase at 52.” Examiner’s note, the rule generator continuously define the rule based on the text data and the true transcription of text data) ,
and updates the identification rule created before the correction of the text with the new identification rule (Col.5, lines 45-59], “After the final correction rules have been derived then the rules are ready to be used in the production phase. FIG. 3 shows a block diagram of a production unit 56 that is used in this invention to apply the correction rules. In the production unit 56, text data is generated from a speech recognition system 58. Again, the speech recognition system can be either an isolated-word speech recognition system or a continuous speech recognition system. The speech recognition text data is outputted from the speech recognition system 58 and inputted to a correction module 60 which contains the set of correction rules that were developed in the training phase. The correction module 60 then applies the correction rules to the speech recognition text data and accordingly corrects the text data. The corrected text is then outputted from the correction module 60.”)..
Morimura, Joy, Lo and Brandoware analogous in arts because they have the same field of endeavor of analyze the feature based on the received event or message.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the rule creation apparatus, as taught by Morimura, to include the wherein in a case where the text including the information indicating the intention of the user is corrected, the second analysis unit calculates a third feature amount indicating a feature of the corrected text , and the rule creation unit further creates a new identification rule on a basis of the reselected possible message and the corrected text, and updates the identification rule created before the correction of the text with the new identification rule, as taught by Brandow. The modification would have been obvious because one of the ordinary skills in art would be motivated to helps to generate the new rule, [Col. 5 ], “FIG. 2 shows a flow chart summarizing the steps used to obtain the correction rules. The processing steps begin at 32 where the text data generated from a speech recognition system is collected. Next, the corresponding true transcription of the text data is collected at 34. The text corrector 30 corrects the speech recognition text data according to any rules developed by the rule generator 18 at 36. Initially, there are no rules and the speech recognition text data passes to the text aligner 12. Then the text aligner 12 aligns the text data with the corresponding true transcription of text data at 38. All of the misaligned sections are sent to the statistical collector 14 which counts the number of times that the misaligned sections have occurred in the training sample of parallel text at 40. The misaligned sections along with the number of times that the misalignment has occurred in the training sample of parallel text are then stored in the trainable database 16 at 42. The rule generator 18 uses the perceived differences in alignment between the speech recognition text data and the corresponding true transcription of text data to derive a set of context-free rules at 44. The applicability of the preliminary replacement rules are observed across the training collection at 46. If all of the rules are determined to be valid at 48 or no further progress can be made at 50, then the rules are ready to be put into the production phase at 52.” Examiner’s note, the rule generator continuously define the rule based on the text data and the true transcription of text data) ,
Claim(s) 4 are/is rejected under 35 U.S.C. 103 as being unpatentable over Morimura et al. (Pub. No. US 20110209010– hereinafter, Morimura) in view of Joy et al . (Pub. No. US 20140280766– hereinafter, Joy) and further in view of Lo et al. (Pub. No. US 20200379776 – hereinafter, lo) and further in view of Lo et al. (Pub. No. US 20200379776 – hereinafter, lo) and further in view of Donlan et al. (Pub. No. US 20040088734 – hereinafter,Donlan).
Regarding claim 4, Morimura teaches the rule creation apparatus according to any one of claim 1, further comprising: a first acquisition unit that acquires a plurality of event messages from a storage unit that stores event messages output from a device or an application included in the target system (Morimura, [Par.0078], “0078] FIG. 27 shows the event management information 106. The event management information comprises the following information. [0079] Event ID: This is information for identifying the event management information. [0080] Event detection time: Denotes the time at which the event was detected. Furthermore, there are cases where event detection is in the monitoring target information processing apparatus, and other cases where it is in the management system, and a value based on a timer respectively managed by either the computer or the apparatus is set in this time.” And [Par.0134], “(Step C) In a case where more than one event was selected in Step B, the management program 105 creates analysis result information 102 based on the selection result of Step B. Furthermore, the value of each item of the analysis result information 102 is created as follows. [0135] Received event list: Stores a source apparatus ID, a source component ID and a status for each event selected in Step B. Furthermore, the received event list may also store the event ID of the event management information together with the above-mentioned value as the information identifying the event. [0136] Applied rule ID: Stores rule identification information stored in the analysis rule information used in the Step A selection,”).
However, Morimura does not teach and passes the plurality of event messages to the first analysis unit and a second acquisition unit that acquires, as the text, a natural language input by the user and passes the text to the second analysis unit.
On the other hand, Donlan teaches and passes the plurality of event messages to the first analysis unit (Donlan, [Par.0068], “Upon receipt of the message, the manager 104 matches selected fields in the response against corresponding fields in the message 512 and, if a match is identified, forwards an update subscriber message 528 to data storage 228 via interface 226. In one configuration, the manager 104 returns a "success" message (not shown) to the customer service center 122 and/or billing system 114.” Examiner’s note, message is returned/passed to the customer service (first analysis unit).) ;
and a second acquisition unit that acquires, as the text, a natural language input by the user and passes the text to the second analysis unit (Donlan, [Apr.0059-0060], “[0060] If the transaction is valid and the criteria has a match, the provisioning manager 104 next forwards a modify subscriber message 412 to the pre-registration server 117. The modify subscriber message 412 includes the subscriber identification data. After verifying the contents of the messages, the server 117 updates its data base (not shown) in response to the messages, and forwards a modify subscriber and household message 416 containing the subscriber identification information to the corresponding service server infrastructure 118 (FIG. 1) serving the subscriber. The infrastructure 118 verifies the contents of the message, and, if properly verified, a server in the infrastructure 118 further initiates transactions with external service providers (e-mail server 138, address book server 142, calendar server 146, etc.) to delete or enable access to the appropriate services by users associated with the subscriber's account. “Examiner’s note, the pre-registration server receives the modify subscriber message and forward to the server infrastructure 118 (second analysis unit).).
Morimura, Joy, Lo and Donlan are analogous in arts because they have the same field of endeavor of analyze the feature based on the received event or message.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the the rule creation apparatus according to any one of claim 1, further comprising: a first acquisition unit that acquires a plurality of event messages from a storage unit that stores event messages output from a device or an application included in the target system, as taught by Morimura, to include the and passes the plurality of event messages to the first analysis unit and a second acquisition unit that acquires, as the text, a natural language input by the user and passes the text to the second analysis unit, as taught by Donlan. The modification would have been obvious because one of the ordinary skills in art would be motivated to update the database on the valid transaction, (Donlan, [par.0059-0060], “An update subscriber response message 408 is returned to the billing system 114, providing pass information or, when the transaction is invalid or the selected criteria does not have a match, fail information.[0060] If the transaction is valid and the criteria has a match, the provisioning manager 104 next forwards a modify subscriber message 412 to the pre-registration server 117. The modify subscriber message 412 includes the subscriber identification data. After verifying the contents of the messages, the server 117 updates its data base (not shown) in response to the messages, and forwards a modify subscriber and household message 416 containing the subscriber identification information to the corresponding service server infrastructure 118 (FIG. 1) serving the subscriber.”).
Claim(s) 5 are/is rejected under 35 U.S.C. 103 as being unpatentable over Morimura et al. (Pub. No. US 20110209010– hereinafter, Morimura) in view of Joy et al . (Pub. No. US 20140280766– hereinafter, Joy) and further in view of Lo et al. (Pub. No. US 20200379776 – hereinafter, lo) and further in view of Adwards et al. (Pub. No. US 20210350346 – hereinafter, Adwards).
Regarding claim 5, Morimura teaches the rule creation apparatus according to claim 1, but it dos not teach wherein the selection unit selects the possible message by calculating a cosine similarity between each of the first feature amounts and the second feature amount, and extracting an event message having a first feature amount having a highest similarity to the second feature amount,
On the other hand, Lo teaches and extracting an event message having a first feature amount having a highest similarity to the second feature amount (LO, [Par.0008], “According to another aspect, a system for prioritization of computer executable events based on dynamically changing entity data is described herein. The system includes a processor and a memory. The memory includes historical registration data for each first event of a plurality of first events, the historical registration data identifying, for each time of a plurality of times, a number of entities at the time, each first event including respective parameter values of one or more parameters used to generate the first event, and computer-readable instructions stored in the memory. The computer-readable instructions, when executed by the processor, cause the processor to identify, for a second event that has an open registration status, respective parameter values of one or more parameters used to generate the second event and registration data identifying, for each time of a plurality of times since registration for the second event was opened, a current number of entities at the time, compute, a similarity score between the second event and each first event of the plurality of first events, the similarity score computed using i) the identified respective parameter values used to generate the second event and generate the first event and ii) the identified registration data of the second event and the historical registration data of the first event, select, for the second event, a subset of the plurality of first events based on the similarity score for each of the events of the subset exceeding a threshold similarity score value, generate, for the second event, a projected number of entities based on i) the registration data of the second event and ii) the historical registration data of one or more first events included in the selected subset, and determine a ranking of the second event relative to one or more third events that have an open registration status.” Examiner’s note, the event is selected based on the similarity score exceed the threshold/ highest similarity score.).
Morimura, Joy and Lo are analogous in arts because they have the same field of endeavor of analyze the feature based on the received event or message.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the rule creation apparatus, as taught by Morimura, to include the and extracting an event message having a first feature amount having a highest similarity to the second feature amount, as taught by Lo. The modification would have been obvious because one of the ordinary skills in art would be motivated to helps to improve the computer resource allocation, (Lo, [Par.0006], “According to one aspect, methods and systems for improving computer resource allocation by prioritization of computer executable events based on dynamically changing entity data. The method includes maintaining, by an event management system including one or more processors, for each first event of a plurality of first events managed by the event management system, historical registration data. The historical registration data identifies, for each time of a plurality of times, a number of entities at the time. Each first event has respective parameter values of one or more parameters used to generate the first event. The method further includes identifying, by the event management system, for a second event that has an open registration status, respective parameter values of one or more parameters used to generate the second event, and registration data identifying, for each time of a plurality of times since registration for the second event was opened, a current number of entities at the time.”).
However, neither Morimura nor Lo teaches wherein the selection unit selects the possible message by calculating a cosine similarity between each of the first feature amounts and the second feature amount,
On the other hand, Edwards teaches wherein the selection unit selects the possible message by calculating a cosine similarity between each of the first feature amounts and the second feature amount (Edwards, [Par.0024], “In some embodiments, the prior response data and the detected response may be compared to determine a similarity score. The similarity score may indicate how similar the detected response is to each of the prior responses to the previous audio messages. For example, a feature vector representing the facial expression of the user in response to the output audio message may be compared to feature vectors representing facial expressions of other users (as well as the user) expressed in prior responses to previous audio messages. As another example, a feature vector representing the captured audio of the user in response to the output audio message may be compared to feature vectors representing the spoken replies from the other users (as well as the user) to previous audio messages. In some embodiments, the similarity may be determined by computing a cosine distance, a Euclidean distance, or other feature space similarity metric, between each pair of feature vectors.”).
Morimura, Joy, Lo and Edwards are analogous in arts because they have the same field of endeavor of analyze the feature based on the received event or message.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the rule creation apparatus, as taught by Morimura, to include wherein the selection unit selects the possible message by calculating a cosine similarity between each of the first feature amounts and the second feature amount, as taught by Edwards. The modification would have been obvious because one of the ordinary skills in art would be motivated to helps to determine the similarity score between the two features, (Edwards, [Par.0024], “In some embodiments, the prior response data and the detected response may be compared to determine a similarity score. The similarity score may indicate how similar the detected response is to each of the prior responses to the previous audio messages. For example, a feature vector representing the facial expression of the user in response to the output audio message may be compared to feature vectors representing facial expressions of other users (as well as the user) expressed in prior responses to previous audio messages. As another example, a feature vector representing the captured audio of the user in response to the output audio message may be compared to feature vectors representing the spoken replies from the other users (as well as the user) to previous audio messages. In some embodiments, the similarity may be determined by computing a cosine distance, a Euclidean distance, or other feature space similarity metric, between each pair of feature vectors. In some embodiments, the feature vectors for both the spoken reply and the facial expression may be compared individually or in combination, as detailed below. Based on the similarities scores, a determination may be made as to whether any of the similarity scores satisfy a similarity score threshold. For example, the similarity score threshold may correspond to the Euclidean distance between two feature vectors being greater than a threshold value (e.g., f.sub.1.Math.f.sub.2≥T). If so, an account associated with the prior response that “matched” the feature vector of the captured response may be selected, and secure services available for that account may be determined.”).
Conclusion
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/E.T./Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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