DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 03/02/2026 has been entered. Claims 1, 17, and 20 have been amended. Claims 1-3 and 5-20 remain pending in the application.
Response to Arguments
Claim Rejections - 35 USC § 103
Regarding claim 1, applicant argues that “ Swan describes searching via index structures keyed to text segments, prefixes, suffixes, and associated postings lists, not storing each client's observability event sequence as a flattened alternating byte sequence of time-range-indicator bytes and hashed event-value bytes, and then running a direct byte-wise comparison over that flattened sequence. Swan's packed arrays and hash tables are used for indexing and de-duplication and for supporting lexicon and postings operations, not for a direct byte-wise pattern match of an entire normalized event stream against a query sequence mapped into the same alternating normalized form. In practical terms, Applicant's amended claim specifies a specific storage representation that is intentionally structured to be searched using a low-level byte-wise comparison between two alternating sequences (stream vs. mapped query). Swan instead builds and queries index structures that require lexicon lookup and postings traversal. Those different architectures have different functional consequences. Applicant's approach enables sequence-pattern detection by direct memory comparison on a flattened normalized stream. Swan's approach enables ad hoc keyword- and time-oriented searching using search-engine indexing constructs. As such, Swan cannot be fairly characterized as teaching, suggesting, or rendering obvious, among other things: storing, by the device, the observability data for the sequenced events as corresponding hashed event values and corresponding associated time range indicators, wherein the stored observability data comprises a normalized event stream formed as an alternating sequence of time range indicators and hashed event values for the sequenced events and/or searching, by the device, the stored observability data by performing a direct byte-wise comparison operation between the normalized event stream and a query mapped to a corresponding alternating sequence of normalized time range indicators and normalized hashed event values, as presently claimed.
Further Applicant argues that “Di Pietro fails to contemplate storing observability data in a flattened normalized event stream formed as an alternating sequence of time range indicators and hashed event values, nor does Di Pietro contemplate performing a direct byte-wise comparison between such a stored normalized stream and a query mapped to the same alternating normalized form. Di Pietro's focus is model deployment and monitoring in a cloud/on-prem architecture. Its periodic communication of "raw data" is for model performance monitoring, not for building a compact correlation-ready stream that is searchable via direct byte-wise comparisons. In short, Di Pietro is about monitoring and analysis workflows and does not disclose the specific normalized stream storage structure and direct byte-wise comparison search operation now claimed.”
Furthermore, Applicant argues that “Dini does not contemplate storing observability event sequences as a normalized event stream formed as an alternating sequence of time range indicators and hashed event values. Dini's discussion concerns computation of scalar metrics characterizing network state-change behavior, and then storing or transmitting those metrics and related timestamp lists. See, e.g., Dini's description that "each distinct instability order value has its own timestamp list." This is materially different from Applicant's claimed approach, which is centered on generating a compact byte-addressable normalized representation of a sequenced event stream and then performing direct byte-wise comparison of that stream against a similarly normalized query sequence for pattern detection. Stated differently, even if Dini's log function is used somewhere in a system, Dini still does not teach the amended storage format (alternating normalized indicator/value sequence) or the amended search operation (direct byte-wise comparison between the stored normalized event stream and a mapped query sequence). “
In response, Examiner relies on a new combination of references.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5-6, and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Swan (US 9,514,175 B2) in view of Di Pietro (US 20210306224 A1) in view of Smith (US 7,661,121 B2) in further view of French (US20240313959A1)
Regarding claim 1, Swan discloses: A method, comprising: obtaining, by a device, observability data for sequenced events in a computing network (Swan, column 3, line 66- Time series data streams (corresponding to “sequenced events”) are received. One example of time series data streams includes server logs and other types of machine data (i.e., data generated by machines); column 4, line 1- The time series data streams are time stamped to create time stamped events; column 9, line 2- the event 315 is taken as input to a time stamp extraction step 330 where the time stamp (corresponding to “observability data”) from the raw event data is extracted; column 5, line 47- raw logs 205 from multiple web servers, application servers)
in a raw format; (Swan, column 5, line 32- so the TSSE typically will collect or be fed raw time series data that are close to their native form; column 5, line 44- The time stamp process 210 turns raw time series data 205 into time stamped events 215 to be fed to the indexing process 220. Following our information processing example, raw logs 205 from multiple web servers, application servers and databases might be processed by the time stamp process 210 to identify individual events 215 within the various log formats and properly extract time and other event data; column 6, line 48- learning is separated into different domains based on the source of MD 205. Domains can be general system types, such as log files, message bus traffic, and network management data, or specific types, such as output of a given application or technology-Sendmail logging data, Oracle database audit data, and J2EE messaging.)
normalizing, by the device, each event of the sequenced events into a hashed event value; (Swan, time series data is organized into discrete events with normalized time stamps and the events are indexed by time and keyword, abstract; column 9, line 53- Events indexed by the TSSE…line 54- hashing the components of the index over a set of buckets organized by time,…, line 64- a bucketing policy may specify that the buckets for events from earlier than today are three hour buckets, but that the buckets for events occurring during the last 24 hours are hashed by the hour; column 4, line 21- The time stamped events may also be segmented; column 12, line 16- the packed array of segments and its associated hash table;)
associating, by the device, observability data corresponding to each event to a particular time range indicator of a plurality of time range indicators representative of respective event completion time ranges (Swan, column 4, line 15- time bucketed indices(corresponding to “time range indicator”) are created by assigning the time stamped events to time buckets according to their time stamps (corresponding to “observability data”); column 10, line 2- a bucket might cover the period 01-15-2005 12:00:00 to 01-15-2005 14:59:59, (corresponding to “event completion time ranges”)…line 18, we use half-open intervals, defined by a start time and an end time… so that events occurring on bucket boundaries are uniquely assigned to a bucket. Following our example in the information processing environment, a database server event with the time stamp of 0l-15-2005 12:00 01 might be assigned to the above-mentioned bucket.)
storing, by the device, the observability data for the sequenced events as corresponding hashed event values and corresponding associated time range indicators, (Swan, fig. 2, item 225, “Time-based Indexes”; fig. 6, item 565, “Bucketed Indexes”; column 10, line 13- in indexing an event by time is to identify the appropriate bucket for the event based on the event's time stamp and the index's bucketing policy; column 9, line 33- The time stamp extraction module 330 automatically stores the time stamp of every hundredth event ( or some other configurable period) from each time series data stream in order to facilitate time stamp interpolation 340; column 10, line 4- buckets are instantiated using a lazy allocation policy (i.e., as late as possible) in primary memory (i.e., RAM). In memory buckets have a maximum capacity and, when they reach their limit, they will be committed to disk and replaced by a new bucket;).
However Swan does not clearly disclose:
from a plurality of agents that send the observability data in a raw format and that are part of an observability intelligence platform; wherein the stored observability data comprises a normalized event stream formed as an alternating sequence of time range indicators and hashed event values for the sequenced events; and searching, by the device, the stored observability data by performing a direct byte-wise comparison operation between the normalized event stream and a query mapped to a corresponding alternating sequence of normalized time range indicators and normalized hashed event values.
However Di Pietro discloses:
from a plurality of agents that send the observability data in a raw format and that are part of an observability intelligence platform; (Di Pietro [0016] The recalibrated model may use previously collected data, newly received raw data, or raw data that is received periodically from the on-premise cloud agent during operation of the on-premise cloud agent; [0017] The present technology allows a machine learning model to be configured on a cloud-based AI engine and be deployed to an on-premise cloud agent. )
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Swan with the teaching of Di Pietro to allow for reduced communications of data from the network devices to the cloud AI engine, (Di Pietro, [0017]).
However Swan in view of Di Pietro does not clearly disclose:
wherein the stored observability data comprises a normalized event stream formed as an alternating sequence of time range indicators and hashed event values for the sequenced events; and searching, by the device, the stored observability data by performing a direct byte-wise comparison operation between the normalized event stream and a query mapped to a corresponding alternating sequence of normalized time range indicators and normalized hashed event values.
However Smith discloses:
wherein the stored observability data comprises a normalized event stream formed as an alternating sequence of time range indicators and hashed event values for the sequenced events; (Smith, Fig. 3B, e.g. Table 320; Fig. 6, e.g. table 606, Hash value, time and table 608, Event 1, time, Event 2, time; column 12, e.g. line 21- Table 320 includes a Hash Value column, a Time column and a Time Offset column…line 26- The time value may be relative to the entire multimedia content stream; column 15, line 49- the hash sequence 3D59, 2Z55, A6E3, and 9Y95 includes metadata which describes an event EVENT 1. EVENT 1 may be an event within the multimedia content stream that occurs at a particular time; column 20, line 14- Table 606 holds the hash sequence of 15 3D59, 2Z55, A6E3, and 9Y95, as well as the time and time offset values for each hash value; column 21, line 2- EVENTS 1 and 2 within the multimedia content stream)
and searching, by the device, the stored observability data by performing a comparison operation between the normalized event stream and a query mapped to a corresponding alternating sequence of normalized time range indicators and normalized hashed event values. (Smith, columns 16-18; column 16, e.g. line 16, performing the hash comparisons; column 17, e.g. line 6- While generating hash values, Parsing Module 210 sends the hash values and time stamps associated with each hash value to the Pattern Recognition State Machine 212, which is used to compare and match the hash values to any of the hash value sequence data downloaded, stored, or received by the DVR 102 from Server 106A or any other source; column 18, line 53- Further, while matching hash values to each hash value in the sequence of 55 hash values, State Machine 500 may also compare the time offset between hash value generated by Parsing Module 210 and the hash value within the hash sequence. If both the hash values match and the time offset values of both hash values).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Swan in view of Di Pietro with the teaching of Smith of hash-based sequence matching and timing techniques in order to improve the accuracy and efficiency of detecting and synchronizing sequences with a data stream and also segments can be recognized with great accuracy using the timestamps of hash values (Smith, column 25, lines 1-7) and also the efficiency is further advanced and can quickly rule out a large amount of hash sequences for comparison without occupying many system resources, (Smith, column 18, lines 8-13)
However Swan in view of Di Pietro in view of Smith does not clearly disclose:
by performing a direct byte-wise comparison operation
However, French discloses:
by performing a direct byte-wise comparison operation (French, [0041]performing a bytewise comparison between the modified ciphertext and the salted search term to determine a level of similarity between the salted search and the modified ciphertext; [0085] The first random salt is extended to the length of the plaintext to be encrypted, for example by repeated hashing where the salt block length is the hash digest length. The length of salt block as determined by the hash digest length may then be applied to the plaintext and key stream in order to generate respective blocks of plaintext and key stream with the same length as the blocks of salt.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Swan in view of Di Pietro in view of Smith with the teaching of French to provide an efficient and secure method for searching non-deterministically encrypted data, (French, [0042]) and also because the bytewise comparison is effective in determining whether the search term matches the plaintext, (French, [0044])
Claims 17 and 20 correspond to claim 1, and are rejected accordingly.
Regarding claim 2, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Claim 2 further recites: wherein the observability data for the sequenced events is represented by an array of hashed event values and time range indicators. (Swan, column 11, line 50- a hot index 555 contains a packed array of segments, a packed array of event addresses and their associated time stamps, and a postings list that associates segments with their time stamped event addresses. For performance reasons, the packed arrays can have hash tables associated with them to provide for quick removal of duplicates; column 4, line 1- The time series data streams are time stamped to create time stamped events. The time stamped events are time indexed to create time bucketed indices)
Regarding claim 5, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Claim 5 further recites: generating a response to a query regarding the observability data by specifying a list of clients associated with one or more particular queried events and/or one or more particular queried event completion time ranges. (Swan, column 14, line 43-Time buckets are queried in the order that is most advantageous to pruning given the sort order for the results. For example, if search results are sorted in reverse chronological order, then the sub-search for the most recent time bucket will be issued first; column 15, line 8- Results can be presented by aggregating and summarizing search results based on discrete time ranges or based on statistical calculations. For example, the example TSSL can specify to see results for only a particular time frame and/or to see results presented by seconds, minutes, hours, days, weeks or months. In this way the search window can be limited to a timeframe and the results can be constructed for optimal viewing based on the density of the expected result set returned from a search. The search "192.168.169.100 hoursago::24 page:: seconds", will return time series events including the keyword "192.168.169.100" that occurred within the last 24 hours and will summarize the display results by seconds.)
Regarding claim 6, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Claim 6 further recites: generating a response to a query regarding the observability data by creating a visual representation of clients associated with a particular queried event, wherein the visual representation of each client further represents a respective event completion time range corresponding to that client. (Swan, column 15, line12- For example, the example TSSL can specify to see results for only a particular time frame and/or to see results presented by seconds, minutes, hours, days, weeks or months. In this way the search window can be limited to a timeframe and the results can be constructed for optimal viewing based on the density of the expected result set returned from a search. The search "192.168.169.100 hours ago::24 page::seconds", will return time series events including the keyword "192.168.169.100" that occurred within the last 24 hours and will summarize the display results by seconds…line 36- In addition to time-based presentation 710, an example TSSE preferably is able to present additional aggregation and summarization of results by metadata characteristics 720, such as, data source, data source type, event type, or originating host machine. In this way, results can be not only organized by time, but also refined by metadata aggregation and summarization; column 6, line 3- feeds the results 275 to an API or user interface for presentation.)
Regarding claim 9, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Claim 9 further recites: wherein the observability data for the sequenced events is obtained in a non-normalized format. (Swan, column 5, line 44- The time stamp process 210 turns raw time series data (corresponding to “non-normalized format “)205 into time stamped events 215 to be fed to the indexing process 220.)
Regarding claim 10, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Claim 10 further recites: storing the observability data for the sequenced events indexed by their corresponding hashed event values and their corresponding associated time range indicators. (Swan, fig. 2, item 225, “Time-based Indexes”; fig. 6, item 565, “Bucketed Indexes”; column 9, line 33- The time stamp extraction module 330 automatically stores the time stamp of every hundredth event ( or some other configurable period) from each time series data stream in order to facilitate time stamp interpolation 340; column 10, line 13- in indexing an event by time is to identify the appropriate bucket for the event based on the event's time stamp and the index's bucketing policy; column 11, line 56- When incoming events are being indexed, each segment of the event is tested for duplication using the segment array and its associated hash; column 9, line 54- hashing the components of the index over a set of buckets organized by time,…, line 64- a bucketing policy may specify that the buckets for events from earlier than today are three hour buckets, but that the buckets for events occurring during the last 24 hours are hashed by the hour; column 10, line 4- buckets are instantiated using a lazy allocation policy (i.e., as late as possible) in primary memory (i.e., RAM). In memory buckets have a maximum capacity and, when they reach their limit, they will be committed to disk and replaced by a new bucket;)
Claim 19 corresponds to claim 10, and is rejected accordingly.
Regarding claim 11, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Claim 11 further recites: wherein each event of the sequenced events is in a representative format selected from a group consisting of: a string; an identifier; a type; an enumeration; a composed textual line; a file; and a log entry. (Swan, column 1, line 26- Time series data are sequences of time stamped records occurring in one or more usually continuous streams, representing some type of activity made up of discrete events. Examples include information processing logs, market transactions, and sensor data from real-time monitors (supply chains, military operation networks, or security systems; column 3, line 24- Other software that is focused on time series, e.g., log event analyzers; column 5, line 47- raw logs 205 from multiple web servers, application servers and databases might be processed by the time stamp process 210 to identify individual events 215 within the various log formats and properly extract time and other event data; column 10, line 29- a substring of the incoming event text; column 16, line 3- the varying types of time series data and events (e.g., single line events a few bytes in size, to multiple line events several megabytes in size; column 9, line 53- Events indexed by the TSSE…line 54- hashing the components of the index over a set of buckets organized by time,…, line 64- a bucketing policy may specify that the buckets for events from earlier than today are three hour buckets, but that the buckets for events occurring during the last 24 hours are hashed by the hour;)
Regarding claim 12, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Claim 12 further recites: wherein the respective event completion time ranges are manually configured. (Swan, column 9, line 33- The time stamp extraction module 330 automatically stores the time stamp of every hundredth event ( or some other configurable period) from each time series data stream in order to facilitate time stamp interpolation 340.)
Regarding claim 13, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Claim 13 further recites: wherein the respective event completion time ranges are determined based on dividing the observability data into a number of ranges. (Swan, column 4, line 15- time bucketed indices are created by assigning the time stamped events to time buckets according to their time stamps (corresponding to “observability data”); column 10, line 2- a bucket might cover the period 01-15-2005 12:00:00 to 01-15-2005 14:59:59.)
Regarding claim 14, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Claim 14 further recites: wherein storing the observability data for the sequenced events further comprises: appending an owner of an event stream to the observability data being stored. (Swan, column 4, line 9- the events may be classified by domain and then time stamped according to their domain; column 6, line 64- the source signature 412 for an Apache web server log might be programmatically assigned the label "205", or a user can assign the label "ApacheServer Log".)
Regarding claim 15, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Claim 15 further recites: wherein the device is an observability agent sending the observability data for the sequenced events stored as corresponding hashed event values and corresponding associated time range indicators to a central server. (Swan, column 5, line 32- most sources of time series data are not designed for sophisticated processing of the data, so the TSSE typically will collect or be fed raw time series data that are close to their native form…one copy of the TSSE can be run on a single central computer or multiple copies can be configured in a peer-to-peer set-up with each copy working on the same time series data streams or different time series data streams; column 5, line 42- The time stamp process 210 turns raw time series data 205 into time stamped events 215 to be fed to the indexing process 220. Following our information processing example, raw logs 205 from multiple web servers, application servers and databases might be processed by the time stamp process 210 to identify individual events 215 within the various log formats and properly extract time and other event data. The event data 215 is used by the index process 220 to build time bucketed indices 225 of the events.)
Regarding claim 16, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Claim 16 further recites: wherein the device is a central server. (Swan, column 5, line 32- most sources of time series data are not designed for sophisticated processing of the data, so the TSSE typically will collect or be fed raw time series data that are close to their native form…one copy of the TSSE can be run on a single central computer or multiple copies can be configured in a peer-to-peer set-up with each copy working on the same time series data streams or different time series data streams; column 5, line 42- The time stamp process 210 turns raw time series data 205 into time stamped events 215 to be fed to the indexing process 220. Following our information processing example, raw logs 205 from multiple web servers, application servers and databases might be processed by the time stamp process 210 to identify individual events 215 within the various log formats and properly extract time and other event data. The event data 215 is used by the index process 220 to build time bucketed indices 225 of the events.)
Regarding claim 18, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 17 as outlined above. Claim 18 further recites: generating a response to a query regarding the observability data including one or more of: a list of clients associated with one or more particular queried events and/or one or more particular queried event completion time ranges; (Swan, column 14, line 43-Time buckets are queried in the order that is most advantageous to pruning given the sort order for the results. For example, if search results are sorted in reverse chronological order, then the sub-search for the most recent time bucket will be issued first; column 15, line 8- Results can be presented by aggregating and summarizing search results based on discrete time ranges or based on statistical calculations. For example, the example TSSL can specify to see results for only a particular time frame and/or to see results presented by seconds, minutes, hours, days, weeks or months. In this way the search window can be limited to a timeframe and the results can be constructed for optimal viewing based on the density of the expected result set returned from a search. The search "192.168.169.100 hoursago::24 page:: seconds", will return time series events including the keyword "192.168.169.100" that occurred within the last 24 hours and will summarize the display results by seconds.)
and a visual representation of clients associated with a particular queried event, wherein the visual representation of each client further represents a respective event completion time range corresponding to that client. (Swan, column 15, line12- For example, the example TSSL can specify to see results for only a particular time frame and/or to see results presented by seconds, minutes, hours, days, weeks or months. In this way the search window can be limited to a timeframe and the results can be constructed for optimal viewing based on the density of the expected result set returned from a search. The search "192.168.169.100 hours ago::24 page::seconds", will return time series events including the keyword "192.168.169.100" that occurred within the last 24 hours and will summarize the display results by seconds…line 36- In addition to time-based presentation 710, an example TSSE preferably is able to present additional aggregation and summarization of results by metadata characteristics 720, such as, data source, data source type, event type, or originating host machine. In this way, results can be not only organized by time, but also refined by metadata aggregation and summarization; column 6, line 3- feeds the results 275 to an API or user interface for presentation.)
Claims 3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Swan (US 9,514,175 B2) in view of Di Pietro (US 20210306224 A1) in view of Smith (US 7,661,121 B2) in further view of French (US20240313959A1) in further view of DUPONT (US 2020/0279003 Al)
Regarding claim 3, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Claim 3 further recites: wherein the observability data for the sequenced events stored as corresponding hashed event values and corresponding associated time range indicators (Swan, fig. 2, item 225, “Time-based Indexes”; fig. 6, item 565, “Bucketed Indexes”; column 10, line 13- in indexing an event by time is to identify the appropriate bucket for the event based on the event's time stamp and the index's bucketing policy; column 9, line 33- The time stamp extraction module 330 automatically stores the time stamp of every hundredth event ( or some other configurable period) from each time series data stream in order to facilitate time stamp interpolation 340; column 10, line 4- buckets are instantiated using a lazy allocation policy (i.e., as late as possible) in primary memory (i.e., RAM). In memory buckets have a maximum capacity and, when they reach their limit, they will be committed to disk and replaced by a new bucket;)
However Swan in view of Di Pietro in view of Smith in further view of French does not clearly disclose:
are stored as a three-byte pair
However DUPONT discloses:
are stored as a three-byte pair
(DUPONT, [0005], line 5- Each pattern from the multiple patterns is hashed, via the processor, into a hash value (e.g., a two-byte hash value), to produce multiple hash values. The multiple hash values are stored in a hash table. Each record from multiple records of the hash table includes a hash value from the multiple hash values and an associated position value. )
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Swan in view of Di Pietro in view of Smith in further view of French with the teaching of DUPONT to encoding information using fewer bits than the original representation, (DUPONT, [0003]).
Regarding claim 7, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Swan in view of Di Pietro in view of Smith in further view of French does not clearly disclose:
wherein the hashed event value includes a two-byte representation of a hash of an event and a corresponding set size value.
However DUPONT discloses:
wherein the hashed event value includes a two-byte representation of a hash of an event and a corresponding set size value.
(DUPONT, [0005], line 5- Each pattern from the multiple patterns is hashed, via the processor, into a hash value (e.g., a two-byte hash value), to produce multiple hash values. The multiple hash values are stored in a hash table. Each record from multiple records of the hash table includes a hash value from the multiple hash values and an associated position value. )
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Swan in view of Di Pietro in view of Smith in further view of French with the teaching of DUPONT to encoding information using fewer bits than the original representation, (DUPONT, [0003]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Swan (US 9,514,175 B2) in view of Di Pietro (US 20210306224 A1) in view of Smith (US 7,661,121 B2) in further view of French (US20240313959A1) in further view of Lipfert (US 2019/0306670 Al)
Regarding claim 8, Swan in view of Di Pietro in view of Smith in further view of French discloses all of the features with respect to claim 1 as outlined above. Swan in view of Di Pietro in view of Smith in further view of French does not clearly disclose: wherein the plurality of time range indicators representative of respective event completion time ranges comprises one-byte representations.
However Lipfert discloses:
wherein the plurality of time range indicators representative of respective event completion time ranges comprises one-byte representations. (Lipfert, [0102] The length indicator 560 of the EDCA parameter element is 1 byte; [0082] a length indicator byte 560 that indicates how long the EDCA parameter element is.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Swan in view of Di Pietro in view of Smith in further view of French with the teaching of Lipfert to provide the structure of the parameter elements, which has been standardized in IEEE 802.11, (Lipfert, [0082]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Faezeh Forouharnejad whose telephone number is (571)270-7416. The examiner can normally be reached on generally Monday through Friday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shah Sanjiv can be reached on (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/F.F. /
Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166