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 Amendments
The action is responsive to the Applicant’s Amendment filed on 4/21/2026. Claims 1-4, 6-14, and 16-20 are pending in the application. Claims 1, 7-11, 16, 17, 19, and 20 are amended.
Response to Arguments
Applicant’s arguments with respect to the rejections previously made and the amended claims filed on 4/21/2026 have been fully considered but they are not persuasive. In view of the claim amendments, the rejections are being updated accordingly.
In regards to independent claim 1, Applicant argued that cited reference Pulle fails to teach the amended limitations of “wherein the tree structure of the patch includes tree structure data sufficient to navigate the common structure of the telemetry samples to locate a telemetry data field corresponding to each of the one or more value differences”.
However, Pulle teaches tree structure data sufficient to navigate to a data field in paragraphs [0016] and [0026]-[0027]. In addition, the newly added limitation is nonfunctional descriptive material describing information elements that are not functionally involved in the steps recited. None of the claimed steps are depending on any of the tree structure data being described. All steps in the claims (i.e. obtaining, invoking, and storing) would be performed the same to achieve a same outcome regardless of the tree structure data, with the intended use of being sufficient to navigate the common structure of the telemetry samples to locate a telemetry data field corresponding to each of the one or more value differences. Describing the data does not produce any practical outcome nor any useful application based on the claim language presented because none of the data is being used in any way that could impact the outcome of the claimed steps.
Thus, for at least the reasons as set forth above, it is submitted that the amended limitations of “wherein the tree structure of the patch includes tree structure data sufficient to navigate the common structure of the telemetry samples to locate a telemetry data field corresponding to each of the one or more value differences” are properly addressed by Pulle.
In regards to independent claim 11, the emphasized limitations that the Applicant argues in claims 11 are similar to the emphasized limitations of claim 1, which have been addressed above. See the response of claim 1 above for explanation.
Further, regarding the new limitations recited in claims 1, 7-11, 16, 17, 19, and 20, it is submitted that they are properly addressed.
Furthermore, it is also submitted that all limitations in pending claims, including those not specifically argued, are properly addressed. The reason is set forth in the rejections. See claim analysis below for detail.
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.
Claims 1-4, 6-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (US 20230079074 A1) in view of Sun et al. (US 20200366315 A1), Jas (US 20200295879 A1) and Pulle et al. (US 20240201967 A1).
Regarding Claim 1, Cella discloses a method of managing telemetry data ([0939]: In some embodiments, the data sources 8020 may include a set of edge devices 8042 that collect, receive and process data from… telemetry systems of machinery), comprising:
obtaining, from a component of an information handling system: telemetry samples including a first sample and one or more subsequent samples (Fig. 69; [0956]: In some embodiments, the digital twin generation system 8108 (in combination with the digital twin I/O system 8104) may obtain data streams from traditional data sources, such as… telemetry data sources… the data streams may include metadata streams that are associated with the nature of the data and data streams containing primary data (e.g., sensor data, sales data, survey data, and the like; [2948]: Additionally or alternatively, the redundant data may include… samples of data, etc.)); and
a component identifier (CID) wherein the CID is uniquely indicative of the component (Fig. 8; [0352]: systems 640 may include an entity discovery system 1900 for discovering one or more value chain network entities 652… This may include components or sub-systems… by an identifier that is assigned by and/or managed by the platform 604; [0956]: Layers for which metadata may be tracked and/or created may include, for example, metadata with respect to… component systems);
However, Cella does not explicitly teach “invoking a lossless compression protocol to store in a telemetry database, a record corresponding to each of the telemetry samples, wherein the storing includes: storing the first sample as an independent record; and storing a subsequent sample as a differential dependent record indicative of one or more value differences, comprising differences between values in the subsequent sample and corresponding values in a preceding sample.”
On the other hand, in the same field of endeavor, Sun teaches
invoking a lossless compression protocol to store in a telemetry database, a record corresponding to each of the telemetry samples ([0008]: Data compression techniques include Run Length Encoding (RLE), which is a form of lossless encoding; [0054]: A data compression technique such as RLE described above can be selected in advance such that a specific application program always invokes the same data compression technique; Fig. 2; The compression parameters file 2070 resulting from the training stage may be sent to a database server for inclusion into a system management software product),
Additionally, Jas teaches wherein said storing includes: storing the first sample as an independent record (Fig. 4; [Abstract]: A device may receive a first telemetry data entry associated with an attribute and store a record associated with the first telemetry data entry; [0010]: a first stream (e.g., an event stream) may include data entries representative of events involving an attribute); and
storing a subsequent sample as a differential dependent record indicative of one or more value differences, comprising differences between values in the subsequent sample and corresponding values in a preceding sample (Fig. 4; [Abstract]: The device may receive a second telemetry data entry associated with the attribute and may determine, from the mapping, that the second telemetry data entry is associated with a second context value that is different from the first context value; [0010]: a second stream (e.g., a context stream) may include data entries representative of changes to context (e.g., an identifier, a location, a characteristic, and/or the like) of the attribute); [0003]: store an event record associated with the event in an event record data structure).
Furthermore, Pulle teaches wherein each telemetry sample includes a structured plurality of key value pairs sharing a common structure (Figs. 4-5; [0024]-[0027]: telemetry logic 150 may sample various performance monitoring counters… Different implementations may use different types of searchable tree structures. For example, a trie (or “prefix tree”) is a search tree data structure that stores (key, value) pairs) and each record includes a data values component (DVC) determined, at least in part, by the telemetry sample (Fig. 1; [0024]-[0030]: In one implementation, telemetry logic 150 collects various forms of resource utilization data 155… Each key is the path to a value that is stored in a leaf node… sorted arrays are used to both preserve custom event records);
wherein the DVC for the dependent record comprises a patch, wherein the patch includes each of the value differences embedded within a tree structure indicative of a location of each of the value differences within the common structure of the telemetry samples (Fig. 5; [0047]-[0053]: At 502, a code path search tree structure is generated… At 503, temporal offsets are determined for aligning the stack trace data… In FIG. 4, for example, the stack trace data 475 is subdivided into a sequence of time deltas (t1, t2, t3, . . . tn). For a given significant stack trace path, a separate sample count is provided for each time delta, indicating the significance of this stack trace within the time delta), and
wherein the tree structure of the patch includes tree structure data sufficient to navigate the common structure of the telemetry samples to locate a telemetry data field corresponding to each of the one or more value differences ([0016]: In one implementation, the stack tracing data and associated profiling data is processed and stored in a search tree structure to provide a more efficient and manageable representation. The data from the search tree structure is then aligned and mapped to corresponding resource utilization data for analysis. For example, correlations are identified between code paths in the search tree structure and occurrences of resource utilization spikes; [0027]: Different implementations may use different types of searchable tree structures).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Cella to incorporate the teachings of Sun to invoke a lossless compression protocol, and Jas to store first and subsequent records to determine differences between values in the subsequent sample and corresponding values in a preceding sample, and Pulle to store the values in a tree structure.
The motivation for doing so would be to produce a perfect reproduction of the uncompressed data set, as recognized by Sun ([0004] of Sun: The compressed data set may be decoded to reproduce the uncompressed data set. If the decoding results in a perfect reproduction of the uncompressed data set, then the compression technique is said to be ‘lossless’), to determine if a subsequent record is inaccurate, as recognized by Jas ([0003] of Jas: perform, based on determining that the second timestamp is before the first timestamp, an action associated with indicating that the event record is inaccurate) and to store the data in a tree structure, as recognized by Pulle ([0016] of Pulle: [0016] In one implementation, the stack tracing data and associated profiling data is processed and stored in a search tree structure to provide a more efficient and manageable representation).
Regarding Claim 2, the combined teachings of Cella, Sun, Jas, and Pulle disclose the method of claim 1.
Jas further teaches wherein: each record includes: the CID; and a time stamp; (Fig. 1A; [0015]: As shown, the event stream includes a timestamp of the event Ts, an event indicator Es for the event, and an attribute C.sub.1 associated with the event… the attribute may refer to any other suitable type of information that may be included and/or shared across multiple telemetry data streams).
Regarding Claim 3, the combined teachings of Cella, Sun, Jas, and Pulle disclose the method of claim 1.
Cella further teaches wherein the DVC for the independent record comprises an encoded telemetry sample encoded with a predetermined encoding scheme ([0053]: The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to… encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products).
Regarding Claim 4, the combined teachings of Cella, Sun, Jas, and Pulle disclose the method of claim 3.
Cella further teaches wherein the predetermined encoding scheme comprises a key-length-type-value (KLTV) encoding scheme comprising: a key field comprising a number corresponding to a telemetry data field; a value field comprising a value for the telemetry data field value; a type field comprising a number indicative of a data type of the value field; and a length field indicative of a length of the value field ([1809-1810]: The machine learning models 11520 may be or may include an autoencoder… The machine learning models 11520 may be or may include one or more other forms of artificial neural networks such as, for example, deep Boltzmann machines; deep belief networks; stacked autoencoders; and the like).
Regarding Claim 6, the combined teachings of Cella, Sun, Jas, and Pulle disclose the method of claim 1.
Jas further teaches wherein storing the subsequent sample as a dependent record includes performing structured comparison operations comprising:
creating an array to hold structured data indicative of the value differences, wherein each element of the array corresponds to an array index value (AIV) and includes a linked list of telemetry data fields corresponding to the AIV ([0017]: As shown further shown in FIG. 1A, and by reference number 120, the telemetry data analyzer looks up the context for the attribute in the lookup table. The lookup table may be implemented and/or stored as any suitable data structure);
for each value of the AIV from 1 to N, expanding any links to identify all telemetry data endpoints for the telemetry data field corresponding to the AIV (Fig. 1B; [0028]-[0031]: Additionally, or alternatively, the telemetry data analyzer may append an error flag to the event record in the event record data structure to indicate that the context value of the event record is likely inaccurate); and
for telemetry data endpoints that differ between the subsequent sample and the preceding sample, saving the value from the subsequent sample in a key value pair for inclusion in a patch and include structured data for navigating the telemetry structure (Fig. 1B; ([0028]-[0031]: the telemetry data analyzer may generate an error record that identifies a time period between the timestamps to indicate that any context information (or value) that was associated with the attribute during the time period is likely inaccurate).
Regarding Claim 7, the combined teachings of Cella, Sun, Jas, and Pulle disclose the method of claim 2.
Cella further teaches further comprising performing decoding operations to recreate a telemetry data sample corresponding to a particular CID and timestamp from one or more records in the telemetry database sharing the CID ([0337]: These may include data integration capabilities, such as for… decoding, and otherwise processing data packets, signals, and other information as it exchanged among the layers and/or the applications 630).
Additionally, Jas teaches wherein the decoding operations include indexing the database with the CID and timestamp to identify a matching record (Fig. 2; [0036]: For example, telemetry data structure 230 may include a list, a table, an index, a database, a graph, and/or the like);
responsive to determining the matching record is an independent record, extracting the sample from the matching record ([0066]: As further shown in FIG. 5, process 500 may include receiving a context entry of a context stream, wherein the context entry indicates that the attribute is associated with a second context value that is different from the first context value (block 540)); and
responsive to determining the matching record is a dependent differential record: indexing the database with the CID to identify: a base record, comprising an independent record matching the CID; and one or more intermediate records comprising one or more dependent differential records matching the CID and having an earlier timestamp than the particular timestamp ([0067]-[0068]: As further shown in FIG. 5, process 500 may include identifying, based on the second context value being different from the first context value, a second timestamp… As further shown in FIG. 5, process 500 may include determining that the second timestamp is before the first timestamp (block 560));
merging the one or more tree structures of the one or more intermediate records to form a patch; patching the base record to obtain an independent record matching the CID and timestamp; and extracting the sample from the matching record ([0031]: Accordingly, computing resources and/or network resources associated with detecting, addressing, and/or mitigating such errors can be conserved using the telemetry data analyzer; [0069]: As further shown in FIG. 5, process 500 may include performing, based on determining that the second timestamp is before the first timestamp, an action associated with indicating that the event record is inaccurate (block 570)).
Regarding Claim 8, the combined teachings of Cella, Sun, Jas, and Pulle disclose the method of claim 1.
Cella further teaches wherein the CID is determined based on a concatenation of values for two or more identifying parameters and wherein the two or more identifying parameters include at least one of: an electronic piece part identifier (ePPID), a service tag, a serial number, a model name ([1680]: Each of the part may also be tokenized to capture information including purchase order identifier (orderID), instruction set identifier (fileID), manufacturing node (manufacturerID), 3D printer (printerID), part number (partID)… The parts can then be tracked using a physical tracker using a unique part number; [1688]: In embodiments, manufacturers and/or wholesalers may sign an instance of 3D printed instruction set, such as by applying a serial number to a piece of 3D printed instruction set before it is transmittable to a customer); and
wherein the CID comprises a hash value generated by hashing the concatenation in accordance with a hashing algorithm ([1654]-[1655]: In embodiments, the transaction data is validated by the nodes through a proof-of-work (POW) consensus algorithm and hashed into an ongoing chain of cryptographically approved blocks of transaction records… the nodes may be required to calculate a hash via a hash algorithm (e.g., SHA256) that satisfies certain conditions set by the system).
Regarding Claim 9, the combined teachings of Cella, Sun, Jas, and Pulle disclose the method of claim 1.
Cella further teaches wherein the method is performed in conjunction with preboot telemetry features for optimizing scarce persistent storage in a preboot environment ([1821]: The data storage and management system 11914 may include a memory subsystem for storage of instructions and data and a file storage subsystem providing persistent storage for program and data files; [1922]: In embodiments, data pipeline functions may include, among other things, optimizing use of preconfigured sensor and detection packages that combine sensor selection, sensing, information collection, preprocessing, routing, consolidation, processing, and the like [Intended use]).
Regarding Claim 10, the combined teachings of Cella, Sun, Jas, and Pulle disclose the method of claim 1.
Jas further teaches wherein the method is performed in conjunction with embedded controller (EC), runtime telemetry for optimizing scarce EC storage ([0527]: Processor 320 is implemented in hardware, firmware, and/or a combination of hardware and software. Processor 320 takes the form of… a microcontroller).
Additionally, Pulle teaches ([0015]: In some implementations, stack tracing is performed to collect code path samples of a container, virtual machine (VM), and/or application at runtime; [0027]-[0031]: A radix trie is a data structure that represents a space-optimized trie… to optimize size and query performance of the stack trace data).
Regarding Claim 11, Cella discloses an information handling system, comprising:
a central processing unit (CPU); and a memory including processor-executable instructions that, when executed by the CPU, cause the system to perform telemetry data management operations including ([2571]: FIG. 151 illustrates an example of a connected product 14110):
obtaining, from a component of an information handling system: telemetry samples including a first sample and one or more subsequent samples (Fig. 69; [0956]: In some embodiments, the digital twin generation system 8108 (in combination with the digital twin I/O system 8104) may obtain data streams from traditional data sources, such as… telemetry data sources… the data streams may include metadata streams that are associated with the nature of the data and data streams containing primary data (e.g., sensor data, sales data, survey data, and the like); [2948]: Additionally or alternatively, the redundant data may include… samples of data, etc.)); and
a component identifier (CID) wherein the CID is uniquely indicative of the component (Fig. 8; [0352]: systems 640 may include an entity discovery system 1900 for discovering one or more value chain network entities 652… This may include components or sub-systems… by an identifier that is assigned by and/or managed by the platform 604; [0956]: Layers for which metadata may be tracked and/or created may include, for example, metadata with respect to… component systems).
However, Cella does not explicitly teach “invoking a lossless compression protocol to store in a telemetry database, a record corresponding to each of the telemetry samples, wherein the storing includes: storing the first sample as an independent record; and storing a subsequent sample as a differential dependent record indicative of one or more value differences, comprising differences between values in the subsequent sample and corresponding values in a preceding sample, and wherein the tree structure of the patch includes tree structure data sufficient to navigate the common structure of the telemetry samples to locate a telemetry data field corresponding to each of the one or more value differences”
On the other hand, in the same field of endeavor, Sun teaches
invoking a lossless compression protocol to store in a telemetry database, a record corresponding to each of the telemetry samples ([0008]: Data compression techniques include Run Length Encoding (RLE), which is a form of lossless encoding; [0054]: A data compression technique such as RLE described above can be selected in advance such that a specific application program always invokes the same data compression technique; Fig. 2; The compression parameters file 2070 resulting from the training stage may be sent to a database server for inclusion into a system management software product),
Additionally, Jas teaches wherein the storing includes: storing the first sample as an independent record (Fig. 4; [Abstract]: A device may receive a first telemetry data entry associated with an attribute and store a record associated with the first telemetry data entry; [0010]: a first stream (e.g., an event stream) may include data entries representative of events involving an attribute); and
storing a subsequent sample as a differential dependent record indicative of one or more value differences, comprising differences between values in the subsequent sample and corresponding values in a preceding sample (Fig. 4; [Abstract]: The device may receive a second telemetry data entry associated with the attribute and may determine, from the mapping, that the second telemetry data entry is associated with a second context value that is different from the first context value; [0010]: a second stream (e.g., a context stream) may include data entries representative of changes to context (e.g., an identifier, a location, a characteristic, and/or the like) of the attribute); [0003]: store an event record associated with the event in an event record data structure).
Furthermore, Pulle teaches wherein each telemetry sample includes a structured plurality of key value pairs sharing a common structure (Figs. 4-5; [0024]-[0027]: telemetry logic 150 may sample various performance monitoring counters of the CPUs 140… Different implementations may use different types of searchable tree structures. For example, a trie (or “prefix tree”) is a search tree data structure that stores (key, value) pairs) and each record includes a data values component (DVC) determined, at least in part, by the telemetry sample (Fig. 1; [0024]-[0030]: In one implementation, telemetry logic 150 collects various forms of resource utilization data 155… Each key is the path to a value that is stored in a leaf node… sorted arrays are used to both preserve custom event records);
wherein the DVC for the dependent record comprises a patch, wherein the patch includes each of the value differences embedded within a tree structure indicative of a location of each of the value differences within the common structure of the telemetry samples (Fig. 5; [0047]-[0053]: At 502, a code path search tree structure is generated… At 503, temporal offsets are determined for aligning the stack trace data… In FIG. 4, for example, the stack trace data 475 is subdivided into a sequence of time deltas (t1, t2, t3, . . . tn). For a given significant stack trace path, a separate sample count is provided for each time delta, indicating the significance of this stack trace within the time delta), and
wherein the tree structure of the patch includes tree structure data sufficient to navigate the common structure of the telemetry samples to locate a telemetry data field corresponding to each of the one or more value differences ([0016]: In one implementation, the stack tracing data and associated profiling data is processed and stored in a search tree structure to provide a more efficient and manageable representation. The data from the search tree structure is then aligned and mapped to corresponding resource utilization data for analysis. For example, correlations are identified between code paths in the search tree structure and occurrences of resource utilization spikes; [0027]: Different implementations may use different types of searchable tree structures).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Cella to incorporate the teachings of Sun to invoke a lossless compression protocol, and Jas to store first and subsequent records to determine differences between values in the subsequent sample and corresponding values in a preceding sample, and Pulle to store the values in a tree structure.
The motivation for doing so would be to produce a perfect reproduction of the uncompressed data set, as recognized by Sun ([0004] of Sun: The compressed data set may be decoded to reproduce the uncompressed data set. If the decoding results in a perfect reproduction of the uncompressed data set, then the compression technique is said to be ‘lossless’), to determine if a subsequent record is inaccurate, as recognized by Jas ([0003] of Jas: perform, based on determining that the second timestamp is before the first timestamp, an action associated with indicating that the event record is inaccurate) and to store the data in a tree structure, as recognized by Pulle ([0016] of Pulle: [0016] In one implementation, the stack tracing data and associated profiling data is processed and stored in a search tree structure to provide a more efficient and manageable representation).
Regarding Claim 12, the combined teachings of Cella, Sun, Jas, and Pulle disclose the information handling system of claim 11.
Jas further teaches wherein: each record includes: the CID; a time stamp; and a data values component (DVC) determined, at least in part, by the telemetry sample (Fig. 1A; [0015]: As shown, the event stream includes a timestamp of the event Ts, an event indicator Es for the event, and an attribute C.sub.1 associated with the event… the attribute may refer to any other suitable type of information that may be included and/or shared across multiple telemetry data streams).
Regarding Claim 13, the combined teachings of Cella, Sun, Jas, and Pulle disclose the information handling system of claim 11.
Cella further teaches wherein the DVC for the independent record comprises an encoded telemetry sample encoded with a predetermined encoding scheme ([0053]: The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to… encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products).
Regarding Claim 14, the combined teachings of Cella, Sun, Jas, and Pulle disclose the information handling system of claim 13.
Cella further teaches wherein the predetermined encoding scheme comprises a key-length-type- value (KLTV) encoding scheme comprising: a key field comprising a number corresponding to a telemetry data field; a value field comprising a value for the telemetry data field value; a type field comprising a number indicative of a data type of the value field; and a length field indicative of a length of the value field ([0053]: The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to… encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products).
Regarding Claim 16, the combined teachings of Cella, Sun, Jas, and Pulle disclose the information handling system of claim 11.
Jas further teaches wherein storing the subsequent sample as a differential dependent record includes performing structured comparison operations comprising:
creating an array to hold structured data indicative of the value differences, wherein each element of the array corresponds to an array index value (AIV) and includes a linked list of telemetry data fields corresponding to the AIV ([0017]: As shown further shown in FIG. 1A, and by reference number 120, the telemetry data analyzer looks up the context for the attribute in the lookup table. The lookup table may be implemented and/or stored as any suitable data structure);
for each value of the AIV from 1 to N, expanding any links to identify all telemetry data endpoints for the telemetry data field corresponding to the AIV (Fig. 1B; [0028]-[0031]: Additionally, or alternatively, the telemetry data analyzer may append an error flag to the event record in the event record data structure to indicate that the context value of the event record is likely inaccurate); and
for telemetry data endpoints that differ between the subsequent sample and the preceding sample, saving the value from the subsequent sample in a key value pair for inclusion in a patch and including structured data for navigating the telemetry structure (Fig. 1B; ([0028]-[0031]: the telemetry data analyzer may generate an error record that identifies a time period between the timestamps to indicate that any context information (or value) that was associated with the attribute during the time period is likely inaccurate).
Regarding Claim 17, the combined teachings of Cella, Sun, Jas, and Pulle disclose the information handling system of claim 12.
Cella further teaches further comprising performing decoding operations to recreate a telemetry data sample corresponding to a particular CID and timestamp from one or more records in the telemetry database sharing the CID ([0337]: These may include data integration capabilities, such as for… decoding, and otherwise processing data packets, signals, and other information as it exchanged among the layers and/or the applications 630).
Additionally, Jas teaches wherein the decoding operations include indexing the database with the CID and timestamp to identify a matching record (Fig. 2; [0036]: For example, telemetry data structure 230 may include a list, a table, an index, a database, a graph, and/or the like);
responsive to determining the matching record is an independent record, extracting the sample from the matching record ([0066]: As further shown in FIG. 5, process 500 may include receiving a context entry of a context stream, wherein the context entry indicates that the attribute is associated with a second context value that is different from the first context value (block 540).); and
responsive to determining the matching record is a dependent differential record: indexing the database with the CID to identify: a base record, comprising an independent record matching the CID; and one or more intermediate records comprising one or more dependent differential records matching the CID and having an earlier timestamp than the particular timestamp ([0067]-[0068]: As further shown in FIG. 5, process 500 may include identifying, based on the second context value being different from the first context value, a second timestamp… As further shown in FIG. 5, process 500 may include determining that the second timestamp is before the first timestamp (block 560));
merging the one or more tree structures of the one or more intermediate records to form a patch; patching the base record to obtain an independent record matching the CID and timestamp; and extracting the sample from the matching record ([0031]: Accordingly, computing resources and/or network resources associated with detecting, addressing, and/or mitigating such errors can be conserved using the telemetry data analyzer; [0069]: As further shown in FIG. 5, process 500 may include performing, based on determining that the second timestamp is before the first timestamp, an action associated with indicating that the event record is inaccurate (block 570)).
Regarding Claim 18, the combined teachings of Cella, Sun, Jas, and Pulle disclose the information handling system of claim 11.
Cella further teaches wherein the CID is determined based on a concatenation of values for two or more identifying parameters ([1680]: Each of the part may also be tokenized to capture information including purchase order identifier (orderID), instruction set identifier (fileID), manufacturing node (manufacturerID), 3D printer (printerID), part number (partID)… The parts can then be tracked using a physical tracker using a unique part number; [1688]: In embodiments, manufacturers and/or wholesalers may sign an instance of 3D printed instruction set, such as by applying a serial number to a piece of 3D printed instruction set before it is transmittable to a customer).
Regarding Claim 19, the combined teachings of Cella, Sun, Jas, and Pulle disclose the information handling system of claim 11.
Cella further teaches wherein the telemetry data management operations are invoked in conjunction with preboot telemetry features for optimizing scarce persistent storage in a preboot environment ([1821]: The data storage and management system 11914 may include a memory subsystem for storage of instructions and data and a file storage subsystem providing persistent storage for program and data files; [1922]: In embodiments, data pipeline functions may include, among other things, optimizing use of preconfigured sensor and detection packages that combine sensor selection, sensing, information collection, preprocessing, routing, consolidation, processing, and the like [Intended use]).
Regarding Claim 20, the combined teachings of Cella, Sun, Jas, and Pulle disclose the information handling system of claim 11.
Jas further teaches wherein the telemetry data management operations are invoked in conjunction with embedded controller (EC), runtime telemetry for optimizing scarce EC storage ([0527]: Processor 320 is implemented in hardware, firmware, and/or a combination of hardware and software. Processor 320 takes the form of… a microcontroller [Intended use]).
Additionally, Pulle teaches ([0015]: In some implementations, stack tracing is performed to collect code path samples of a container, virtual machine (VM), and/or application at runtime; [0027]-[0031]: A radix trie is a data structure that represents a space-optimized trie… to optimize size and query performance of the stack trace data).
Conclusion
25. 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIRLEY D. HICKS whose telephone number is (571)272-3304. The examiner can normally be reached Mon - Fri 7:30 - 4:00.
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/S D H/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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