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
Claims 1-20 are presented for examination.
This is a Non-Final Action.
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 11-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 11 recites "a computer-readable medium". The Specification recite a computer-readable medium can be in other forms (e.g., non-transitory computer-readable medium) (Paragraph [0157]) thus not limiting machine readable medium to only non-transitory mediums, which does not meet the requirement presented by 35 U.S.C. 101 See, e.g., In re Nuitjen, Docket no. 2006-1371 (Fed. Cir. Sept. 20, 2007) (slip. op. at 18) (“A transitory, propagating signal like Nuitjen’s is not a process, machine, manufacture, or composition of matter.’ … Thus, such a signal cannot be patentable subject matter.”)
Claims 12-16 are dependent upon claim 10, respectively, do not add anything to correct the deficiency and therefore are likewise rejected.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-23 of Patent No. US 11,657,056. Although the conflicting claims are not identical, they are not patentably distinct from each other because
Instant Application
US Patent: US 11,657,056
1, 11, 18
1, 2, 13, 19
2, 12, 19
1, 3, 14-15, 19, 20
3, 13, 20
4. 16-17, 21-22
4, 14
4, 6, 16-17
5, 15
1, 9, 13-17
6, 16
7, 18
7 and 17
9, 17
8
7
9
1, 10-12
10
8, 23
This is an obviousness-type double patenting rejection because the conflicting claims have in fact been patented.
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-17 of Patent No. US 12,287,794. Although the conflicting claims are not identical, they are not patentably distinct from each other because
Instant Application
US Patent: US 12,287,794
1, 11, 18
1, 9, 14
2, 12, 19
1, 2, 10, 15
3, 13, 20
3, 11, 15, 16
4, 14
3, 5, 11, 12
5, 15
1, 8, 11, 12
6, 16
6, 13
7, 17
8, 12
8
6
9
1
10
7, 17
This is an obviousness-type double patenting rejection because the conflicting claims have in fact been patented.
Claim Rejections - 35 U.S.C. §101
35 U.S.C. §101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 USC 101 as directed to an abstract idea without significantly more.
With respect to independent claims 1, 11 and 18, specifically claim 1 recites “generating, by a computing device, a set of serialized data values for an attribute of an event based at least in part on a first type of data compression performed on the attribute of the event; generating, by the computing device, a set of de-serialized data values for the attribute of the event based at least in part on the first type of data compression and the set of serialized data values;”. These limitations could be reasonably and practically performed by the human mind, the steps of data compression, serializing, de-serializing all fall under data transformations wherein serialization/deserialization is a form encoding/decoding which can be done via mathematical concepts based on observation/evaluations. Accordingly, the claim recites a mental process and mathematical relationships, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. At step 2A, prong two, claim(s) 1, 11 and 18, recites the additional elements of “computer readable storage medium…”, “computing devices”, “one or more processors”, “memory storing a plurality of instructions”, “executing…” “transmitting…”“executed by at least one data processor”; “obtain, from database, multiple videos…; “obtain a natural language query…”; and “present…” are elements merely invoking a generic computer environment (processor, database, memory) and basic data-gathering or outputting functions (MPEP 2106.05(f)) hence reciting insignificant extra solution activities.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, each step is done “by a computing device” but the claims do not, improve the computer itself, change how serialization is performed, create a new type of query or engine, solve a technical problem in a technical way. The claims merely represent mere instructions to a computer to generate data values (serialization), generate more data values (deserialization), run queries, and send results. No technological improvement to networking, distributed systems, query engines or compression techniques are recited. Therefore claim is directed to an abstract idea.
The claims, 1, 11 and 18, at step 2B do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained with respect to Step 2A Prong Two, the additional elements as recited in step 2A prong 2 recite conventional computer executing routine data-storage and retrieval operations. No elements individually or in combination adds “significantly more” than the abstract idea hence are no more than well-understood, routine and conventional computer functions that merely apply the abstract idea on a generic computer. When viewed as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and do not add significantly more than the abstract idea itself (MPEP 2106.05(d)). According, claim 1 is ineligible under 101.
Claims 2-10 are dependent claims and do not recite any additional elements that would amount to significantly more than the abstract idea. Specifically,
Claim 2. With respect to step 2A prong 1 “identifying the first type of data compression performed on a plurality of data values represented by the attribute of the event in the batch of events” recites abstract idea of mental steps (observation & evaluation), a person can classify information based on observations/evaluations. With respect to step 2A prong 2 “receiving a batch of events from an event stream;” recites additional elements of insignificant extra solution activity of generic data ingestion. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 3. With respect to step 2A prong 1 “identifying a second type of data compression performed on a plurality of data values represented by the attribute of the event, wherein the second type of data compression is different from the first type of data compression.” recites abstract idea of mental steps (observation & evaluation), a person can identify compression type based on observations/evaluations.
Claim 4. With respect to step 2A prong 2 “generating the set of serialized data values for the attribute based at least in part on the second type of data compression; and generating the set of de-serialized data values for the attribute based at least in part on the second type of data compression and the set of serialized data values.” recites additional elements of insignificant extra solution activity of mathematical transformation of data, serialization, deserialization, compression and decompression are listed in MPEP 2106.04 as mathematical operations; reciting generic computer execution with no improvement to how serialization is performed; simply instructs a computer to perform routine data transformations. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 5. With respect to step 2A prong 1 “determining that the attribute is of a first data type; and wherein: the first type of data compression is performed on a plurality of data values represented by the attribute based at least in part on determining that the attribute is of the first data type, wherein the first data type is a numeric data type.” recites abstract idea of mental steps (observation & evaluation), a person can classify information based on observations/evaluations.
Claim 6. With respect to step 2A prong 2 “wherein the first type of data compression is at least one of a base value compression, a precision reduction compression, or a precision reduction value index compression.” recites additional elements of insignificant extra solution activity mathematical encoding, the claim recites the idea itself, not any technological improvement in how compression is implemented. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 7. With respect to step 2A prong 1 “determining that the attribute is of a second data type; and wherein: a second type of data compression is performed on a plurality of data values represented by the attribute based at least in part on determining that the attribute is of the second data type, wherein the second data type is a non-numeric data type.” recites abstract idea of mental steps (observation & evaluation), a person can classify information (data type detection) and do mathematical transformations (compressions) based on observations and evaluations with no improvement to the computer functions.
Claim 8. With respect to step 2A prong 2 “wherein the second type of data compression is a value index compression technique.” recites additional elements of insignificant extra solution activity of mathematical concept (value-index compression is indexing with encoding with no technological improvement to the operation of computer. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 9. With respect to step 2A prong 1 “generating the set of serialized data values for the attribute based on the first type of data compression comprises: obtaining a minimum data value, a maximum data value, and a set of unique data values represented by a plurality of data values represented by the attribute; computing a number of bits to store the plurality of data values represented by the attribute; determining that a size of the set of unique data values is smaller than the plurality of data values; and responsive to the determining, performing the first type of data compression on the plurality data values represented by the attribute for the event to generate the set of serialized data values for the attribute” recites abstract idea of mathematical operations (i.e. mathematical functions (min, max), set operations (unique values), information theory (bit count), conditional logic). With respect to step 2A prong 2 the claim recites generic computer with no improvement to computer performance thus recites additional elements of insignificant extra solution activity of applying mathematical operations to a generic computer. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records.
Claim 10. With respect to step 2A prong 1 “identifying a set of one or more operations to be performed on the event in a batch of events based on the plurality of continuous queries; representing the set of one or more operations as a continuous query language (CQL) Resilient Distributed Dataset (RDD) Directed Acyclic Graph (DAG) of transformations; and executing the CQL RDD transformations against the set of de-serialized data values corresponding to the attribute to generate the plurality of output event streams.” recites abstract idea, mental steps (observation & evaluation), a person can identifying a set of batch events wherein the events represent operations for transformation, wherein a person could process the data utilizing the transformations to generate a output, this can be done as an academic exercise with help of paper and pencil.
Claims 11 and 18 are similar to claim 1 hence rejected similarly.
Claims 12 and 19 are similar to claim 2 hence rejected similarly.
Claims 13 and 20 are similar to claim 3 hence rejected similarly.
Claim 14 is similar to claim 4 hence rejected similarly.
Claim 15 is similar to claim 5 hence rejected similarly.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 11-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Deshmukh et al. (US 2014/0095535) further in view of Acker et al. (US 2016/0171067)
1. Deshmukh teaches, A method for processing a continuous data stream of events using a distributed event processing system (Abstract – techniques for managing continuous queries with archived relations… a query that includes at least a data stream… evaluated based at least in part on the data stream…, Deshmukh ), the method comprising:
executing, by the computing device, a plurality of continuous queries against the set of de-serialized data values corresponding to the attribute to generate a plurality of output event streams (Paragraph 5 – teaches the query may comprise a continuous query configured to process incoming real-time data… evaluating the query… based on the data stream, Fig 1: 156 – SQ Engine producing outputs – teaches executing continuous queries over event streams, Deshmukh ); and
transmitting, by the computing device, the plurality of output event streams to a user device (Fig 3, Paragraphs 159 – 162 – teaches processing the selecting events then outputting to event sinks including a cache Deshmukh).
Deshmukh does not explicitly recite,
generating, by a computing device, a set of serialized data values for an attribute of an event based at least in part on a first type of data compression performed on the attribute of the event ();
generating, by the computing device, a set of de-serialized data values for the attribute of the event based at least in part on the first type of data compression and the set of serialized data values;
However, Acker teaches,
generating, by a computing device, a set of serialized data values for an attribute of an event based at least in part on a first type of data compression performed on the attribute of the event (Paragraph 26-28 – teaches a fast serialization schemes can use the knowledge about data format, data content… minimal value, maximal value, value set or dictionary… fast serialization schemes may be used… to reduce the amount of data during the serialization/deserialization – thus teaching general serialized value and explicitly reducing size via compression (serialization schemes), Acker );
generating, by the computing device, a set of de-serialized data values for the attribute of the event based at least in part on the first type of data compression and the set of serialized data values (Paragraph 48 and Fig 1 – teaches deserialization engine 148 and abstract – teaches transferring the serialized data to the second database… the target system can read the transferred data from the transfer medium and deserialize the transferred data, Acker);
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to combine Deshmukh with Acker because Acker paragraph 27-29 teaches selective combinations of data compression and serialization to optimize data transfer rates, and Acker at paragraph 48 teaches data streaming compatible with Deshmukh’s continuous queries. A POSITA would incorporate Acker’s serialization/compression into Deshmukh’s event-stream system because Deshmukh handles high-volume streams, and Acker provides known methods for reducing message size to improve throughput.
2. The combination of Deshmukh and Acker teach, The method of claim 1, further comprising:
receiving a batch of events from an event stream (Paragraph 3 – teaches a number of modern applications generate data in the form of continuous data or event streams; Fig. 6: 604 – teaches initialize the query with historical data, step 606 – teaches evaluate the query based on the data stream and historical data – these steps show processing sets of events together (historical with incoming), Deshmukh); and
identifying the first type of data compression performed on a plurality of data values represented by the attribute of the event in the batch of events (Paragraph 26-28 – teaches fast serialization schemes can use the knowledge about data format, data content… minimal value, maximal value, value set or dictionary… During the analyzing process, it may be determined whether to use compression scheme, fast serialization schemes, or a combination therefore – further knowledge can be obtained during an analyzing process before serialization – thus teaching analyzing process that identifies which compression/serialization scheme applies based on attribute values, Acker).
3. The combination of Deshmukh and Acker teach, The method of claim 1, further comprising identifying a second type of data compression performed on a plurality of data values represented by the attribute of the event (Paragraph 28 – teaches fast serialization schemes can use knowledge about data format, data content… During the analyzing process, it may be determined whether to use compression scheme, fast serialization schemes or a combination thereof; Paragraphs 7-8 & 30-34 – teaches dynamic determination of different serialization schemes (repetition, replication, integer, character) – thus teaching multiple distinct compression/serialization schemes and identifying which one applies during analysis, Acker), wherein the second type of data compression is different from the first type of data compression (Paragraphs 7-8 & 33-35 – teaches determining the data serialization scheme… comprises at least one of repetition scheme or a replication scheme… determining the variable serialization scheme.. comprises an integer scheme or a character scheme – thus showing multiple different, alternative schemes, Acker).
4. The combination of Deshmukh and Acker teach, The method of claim 3, further comprising:
generating the set of serialized data values for the attribute based at least in part on the second type of data compression (Paragraphs 7-10 & 33-35 – teaches determining the data serialization scheme comprises at least one of a repetition scheme or a replication scheme; determining the variable serialization scheme comprises an integer scheme or a character scheme – the system selects different schemes for compression and serialization, Acker); and
generating the set of de-serialized data values for the attribute based at least in part on the second type of data compression and the set of serialized data values (Fig 1: 148 – teaches Deserialization engine and Abstract, Paragraph 1 – teaches the target system can deserialize the transfer data – thus teaching corresponding deserialization processes for repetition vs replication schemes (i.e. different decompression paths), the second type is simply the alternative serialization scheme, Acker).
5. The combination of Deshmukh and Acker teach, The method of claim 1 further comprising:
determining that the attribute is of a first data type (Paragraph 26-28 – teaches fast serialization schemes can use knowledge about data format… The knowledge of data format may include character, integer or other formats; Paragraph 8 – teaches if the variable type is integer, determining that variable serialization comprises an integer scheme, Acker); and wherein:
the first type of data compression is performed on a plurality of data values represented by the attribute based at least in part on determining that the attribute is of the first data type, wherein the first data type is a numeric data type (Paragraph 8 – teaches if the variable type is integer, determining that variable serialization comprises an integer scheme; and if the variable type is character, determining that the variable serialization comprises a character scheme; Paragraph 26-28 – teach selecting schemes based on data characteristics, Acker).
6. The combination of Deshmukh and Acker teach, The method of claim 5, wherein the first type of data compression is at least one of a base value compression (Paragraph 28-29 – teaches the knowledge of data content may include minimal value, maximal value, value set… In some cases, this knowledge can be obtained during an analyzing process… determining minimal value, maximal value for base-value encoding, Acher), a precision reduction compression, or a precision reduction value index compression.
Claims 11 and 18 are similar to claim 1 hence rejected similarly.
Claims 12 and 19 are similar to claim 2 hence rejected similarly.
Claims 13 and 20 are similar to claim 3 hence rejected similarly.
Claim 13 further includes “…in the batch of events”
Claim 14 is similar to claim 4 hence rejected similarly.
Claim 15 is similar to claim 5 hence rejected similarly.
Claim 16 is similar to claim 6 hence rejected similarly.
Claims 7, 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Deshmukh et al. (US 2014/0095535) in view of Acker et al. (US 2016/0171067) further in view of Al-Dossary et al. (US 2015/0094958)
All the limitations of claim 1 are taught above.
7. The combination of Deshmukh and Acker teach, The method of claim 1 further comprising:
determining that the attribute is of a second data type and wherein: wherein the second data type is a non-numeric data type (Paragraph 28 – teaches the Knowledge about data format may include character, integer or other formats, Paragraph 34 – teaches distinguishes characters from integer serialization schema – thus teaching identifying non-numeric types such as character fields during serialization analysis, Acker).
The combination of Deshmukh and Acker do not explicitly teach,
a second type of data compression is performed on a plurality of data values represented by the attribute based at least in part on determining that the attribute is of the second data type (non-numeric).
However, Al-Dossary teaches,
a second type of data compression is performed on a plurality of data values represented by the attribute based at least in part on determining that the attribute is of the second data type (non-numeric) (Abstract, Paragraphs 7-8 – teaches extended quantization… group reduction criteria… handling attributes that have non-numeric or categorical values through grouping and merging techniques – thus teaching quantization/grouping of categorical attribute values, Al-Dossary).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to modify the combination of Deshmukh and Acker with Al-Dossary because Deshmukh’s event stream systems must optimize both numeric and non-numeric attribute transport, Acker identifies the type and Al-Dossary supplies the appropriate compression for non-numeric data. Both techniques target bandwidth and memory reduction. A POSITA would combine Al-Dossary’s grouping/quantization compression with Deshmukh’s event processing to reduce payload size for text/categorical fields further enhancing Acker’s numeric compression and enhances performance in distributed CQ system.
8. The combination of Deshmukh, Acker and Al-Dossary teach, The method of claim 7, wherein the second type of data compression is a value index compression technique (Abstract, Paragraph 7 – 8 – extended quantization… merging attributes… reducing attribute values into representative groups… - thus teaching grouping categorical values into quantization groups which represented internally by group identifiers (indexes), Al-Dossary).
Claim 17 is similar to claim 7 hence rejected similarly.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Deshmukh et al. (US 2014/0095535) in view of Acker et al. (US 2016/0171067) further in view of Jerzak et al. (US 2015/0169786)
All the limitations of claim 1 is taught above.
9. The combination of Deshmukh and Acker teach, The method of claim 1, wherein generating the set of serialized data values for the attribute based on the first type of data compression comprises:
obtaining a minimum data value, a maximum data value, and a set of unique data values represented by a plurality of data values represented by the attribute (Paragraph 28 – teaches the knowledge about data content may include minimal value, maximal value, value set or dictionary – thus teaching obtaining min/max value and value set (unique values), Acker); and
responsive to the determining, performing the first type of data compression on the plurality data values represented by the attribute for the event to generate the set of serialized data values for the attribute (Paragraph 29 – teaches after describing a value-set condition, compression/serialization is performed according 0 thus teaching responsive to determine data characteristics, Acker).
The combination of Deshmukh and Acker not explicitly teach,
computing a number of bits to store the plurality of data values represented by the attribute; and
determining that a size of the set of unique data values is smaller than the plurality of data values.
However, Jerzak teaches,
computing a number of bits to store the plurality of data values represented by the attribute (Paragraph 29 – teaches hash-based strategy applies a hash function… The obtained value is subsequently subjected to modulo operations – thus teaching hash to modulo reduces bit-width by discarding high-order bits, implicitly computing the number of bits needed to represent the reduced index).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to modify the combination of Deshmukh and Acker with Jerzak because Deshmukh’s high throughput streaming environment motivates data-size reduction, Jerzak demonstrates the benefits of reducing large value domains into smaller index spaces. A POSITA would use Jerzak’s precision reduction (hash/modulo) to determine an appropriate bit-width for compressing numeric attributes with Deshmukh’s distributed event system.
The combination of Deshmukh, Acker and Jerzak do not explicitly recite, determining that a size of the set of unique data values is smaller than the plurality of data values. However Deshmukh processes high-volume streaming data where reducing payload size is crucial. Jerzak teaches domain reduction (hash to Modulo) to improve efficiency by mapping many values into fewer representative buckets. A POSITA would recognize that dictionary/value-index compression is beneficial only when duplicates exist; therefore, performing a unique-value-set comparison is routine, predictable heuristic for deciding whether compression with reduce size. Combining Deshmukh’s high-throughput environment with Jerzak’s domain-reduction insight naturally motivates such a test.
All the limitations of claim 1 are taught above.
10. The combination of Deshmukh and Acker do not explicitly teach,
identifying a set of one or more operations to be performed on the event in a batch of events based on the plurality of continuous queries;
representing the set of one or more operations as a continuous query language (CQL) Resilient Distributed Dataset (RDD) Directed Acyclic Graph (DAG) of transformations; and
executing the CQL RDD transformations against the set of de-serialized data values corresponding to the attribute to generate the plurality of output event streams.
However, Jerzak teaches,
identifying a set of one or more operations to be performed on the event in a batch of events based on the plurality of continuous queries (Paragraph 3 – Within an ESP system a continuous data stream (comprising multiple, consecutive data items) is pushed through a query. Results of the query are subsequently pushed out of the system. Queries in ESP system can be decomposed into a network of operators, each operator representing an atomic processing block. The operator network forms a DAG, Jerzak);
representing the set of one or more operations as a continuous query language (CQL) Resilient Distributed Dataset (RDD) Directed Acyclic Graph (DAG) of transformations (Paragraph 3 – teaches an event-stream continuous query (CQL), and Paragraph 17 With Fig 2 – teaches a parsed ESP query (e.g. parsed query 109) can be represented as a DAG, Jerzak); and
executing the CQL RDD transformations against the set of de-serialized data values corresponding to the attribute to generate the plurality of output event streams (Paragraphs 3, 21, 42-43, Fig 3 and Fig 6 – teaches at operation 612 a merge node is created in the DAG 300, the merge node consolidating data from the grouping and the duplicates of the grouping. At operation 614 the input query is resolved by processing data from one or more event streams 104A-104E using the DAG 300).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to modify the combination of Deshmukh and Acker with Jerzak because both references are complementary event stream processors, and their similarities and overlap are such that appearances of features shown in one would suggest the application of those features to the other to a POSITA and the elements can be combined according to known methods to yield predictable results, without any change in the element’s respective functions. One would have been motivated to modify Deshmukh with Jerzak to improve processing efficiency because Jerzak discloses methods “to create an optimal partitioned query 112 (an optimal DAG) to better utilize system resources” (Paragraph 15, Jerzak)
Conclusion
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/AMRESH SINGH/Primary Examiner, Art Unit 2159
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