DETAILED ACTION
Response to Amendment
1. This office action is in response to applicant’s communication filed on 07/23/2026 in response to PTO Office Action mailed 04/28/2026. The Applicant’s remarks and amendments to the claims and/or the specification were considered with the results as follows.
2. In response to the last Office Action, no claims are added, amended or canceled. As a result, claims 1-16 are pending in this office action.
3. The 35 USC 101 rejections have been withdrawn upon re-evaluation.
4. The double patenting rejections have been withdrawn due to terminal disclaimer filed on 07/21/2026.
Response to Arguments
5. Applicant's arguments with respect to 35 USC 103 have been fully considered but are not persuasive and the details are as follows:
Applicant’s argument with respect to claim 1 stated as “Wroblewski is directed to relational database query optimization using statistical relationships among stored data…Arelakis does not teach allocate a compression or decompression system for each data set across the plurality of computing devices and to process the plurality of data sets through each data sets corresponding compression or decompression system. These limitations require distribution of the compression or decompression work itself across multiple computing devices…Arelakis ‘s HyComp compression is a distinct unit located with that subsystem and is not distributed across P1 through Pn. Arelakis nowhere allocates compression to, or performs compression on, different computing devices. The architecture is directed to selecting a single compression scheme for a single data block within a single system to improve cache memory, or link compression, not distributing compression takes across a plurality of computing devices”.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., distribute a single compression operation among multiple computing devices, distributing the compression workload, load balancing, parallel processing, or executing portions of a data block on different processors) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Claim 1 merely recites allocate a compression or decompression system for each data set across the plurality of computing devices and process the plurality of data sets through each data sets corresponding compression or decompression system. The cited reference Arelakis expressly discloses a hybrid data compression architecture comprising multiple computing resources, including multiple processing units P1…Pn, caches, memory subsystems and link subsystems within a memory hierarchy (See para. [0020]-para.[0021], para. [0055], para. [0135] and Figures 1 and 18). The hybrid data compression architecture of Arelakis evaluates incoming data blocks, determines an appropriate compression and decompression method, and allocates each data set to its corresponding compression/decompression method in a cache subsystem, a memory subsystem and/or data transferring subsystem in a computer and/or a data computer system (See para. [0024] and para. [0070]). In addition, the rejection does not rely on the predictor alone as corresponding to the claimed virtual management layer. Rather, the rejection relies on the collective operation of Arelakis’s hybrid data compression architecture, including the virtual management functionality that evaluates incoming determines an appropriate compression/decompression method, allocates the selected compression/decompression subsystem and coordinates processing across the disclosed computing resources (See para. [0020]-para. [0024], para. [0055], para. [0070] and para. [0135], Figures 1 and 18). Accordingly, Arelakis, as relied upon in the rejection, teaches or at least suggests the argued imitations of claim 1.
Applicant further argues “claim 1 requires the computing devices to maintain the identified relationships between data sets after compression or decompression has taken place…the stored dependency is itself a compression technique used to avoid storing redundant data, it is not the preservation of an association between different data sets so that those data sets remain associated after they have been compressed or decompressed…Wroblewski discloses no post-processing step that regroups associated data set after compression or decompression”.
In response to applicant's argument, the Examiner disagrees because Applicant argues that claim 1 requires a post-processing step that regroups associated data sets after compression or decompression. However, claim 1 contains no limitation that requires regrouping of associated data sets after processing. Rather, claim 1 merely recites maintaining the identified relationships between data sets after compression or decompression has taken place. Claim 1 does not define the nature of the “identified relationships” nor prescribe any way such relationships are maintained. Claim 1 also does not require regrouping associated data sets after compression or decompression. The cited reference Arelakis teaches maintaining associations after compression by generating metadata associated with the compressed data block, storing the generated metadata together with the compressed data block (See Arelakis, para. [0052], para. [0074], Figures 9, 14 and 15). Thus, the association between the compressed data and the information identifying the selected compression/decompression scheme is preserved after compression or decompression. Further, the Wroblewski reference teaches identifying relationships among data units through dependency information (See Wroblewski, para. [0006] and para. [0035]) and maintaining descriptions of those dependencies after encoding or compression has taken place (See Wroblewski, para. [0082]). Contrary to Applicant’s assertion, claim 1 does not exclude dependency relationships from the scope of the claimed “identified relationships”. Accordingly, Wroblewski’s dependency relationships reasonably correspond to the claimed identified relationships.
Applicant further argues “The office action lacks a reasoned explanation as to why a skilled artisan would have combined them in a specific manner recited…and the office does not explain how the single-block hybrid compressor of Arelakis and the database query optimizer of Wroblewski would be modified to produce it”.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the rejection relies on Wroblewski for its teachings on organizing data elements into data units and identifying and maintaining relationships among data units, while Arelakis is relied upon for its hybrid data compression architecture that evaluates incoming data, selects an appropriate compression/decompression method, and allocates the selected compression/decompression subsystem. A person of ordinary skill in the art would have recognized that incorporating Wroblewski’s data organization techniques into Arelakis’s hybrid compression architecture would have predictably improved the organization and management of related data while reducing storage requirements, as taught by Wroblewski (See Wroblewski, para. [0005]).
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, 3, 5, 9, 11 and 13 are rejected under 35 U.S.C. 103 as unpatentable by Arelakis et al. (WO 2016/186563 A1), hereinafter Arelakis and in view of Wroblewski et al. (US 2008/0071748 A1), hereinafter Wroblewski.
Referring to claims 1 and 9, Arelakis discloses a system for multi-type data compression or decompression with a virtual management layer (See para. [0020], para. [0021], para. [0055] a hybrid data compression system for accessing and processing data chunks of sizes depended on data types using compression or decompression schemes),
comprising: a plurality of computing devices, each comprising at least a memory and a processor (See para [0005], para. [0006], para. [0055] and para. [0135] and Figures 1 & 18, a hybrid compression system which integrates a plurality of compression and decompression devices into a cache, memory or link subsystem of a computer system comprises one or more several processing units PI…Pn connected to a memory hierarchy);
a plurality of programming instructions stored in the memory and operable on the processor of each computing device, wherein the plurality of programming instructions, when operating on the processors (See para. [0005]- para. [0007], para. [0055] and para. [0135], Figures 1 & 18, each device is a cache, a memory or a link subsystem of the computer system which is configured to perform respective data compression/decompression methods by respective computer program products comprising code instructions), cause the computing devices to:
organize a plurality of incoming data types into a plurality of data sets in a virtual
management layer (See para. [0024] and para. [0070] the hybrid data compression system assesses and arranges each data block in one of a plurality of certain data type(s)); […]
allocate a compression or decompression system for each data set across the plurality of computing devices (See para. [0024] and para. [0070], the hybrid data compression system selects or allocates data compression methods and devices that are estimated to be the best data compression scheme among two or more compression schemes using a dominating data type in a data block);
process the plurality of data sets through each data set's corresponding compression or
decompression system: and maintain […the metadata between datasets] after compression or decompression has taken place (See para. [0052], para. [0074], Figures 9, 14 and 15, the hybrid data compression system processes a plurality of data blocks in a Null block compression device and/or a Huffman-based compression device, also note in para. [0108], the hybrid data compression system is configured to generate [e.g., 1816; 2516; 3016] metadata [e.g., 1824; 2524; 3024] associated with the compressed data block [e.g., 1818; 2518] and serving to identify the data compression scheme of the selected estimated best suited data compressor. The hybrid data compression device is configured to store the generated metadata in a data storage [e.g., 1820; 2520] together with the compressed data block [e.g., 1818; 2518], the data storage being accessible to a data decompression device [e.g., 1830; 2530]. Alternatively, the hybrid data compression device may be configured to transmit the generated metadata [e.g., 3024] over a link [e.g., 3020] together with the compressed data block [e.g., 3018] to a data decompression device [e.g., 3030]).
Arelakis does not explicitly identify relationships between different data sets and maintain the identified relationships between data sets after compression or decompression has taken place.
Wroblewski discloses identify relationships between different data sets (See para. [0006] and para. [0035], identifying relationships between two or more data units in order to group data elements in data packs); maintain the identified relationships between data sets after compression or decompression has taken place (See para. [0082], the functional dependencies are stored and encoded in data packs).
Therefore, it would have been obvious to a person of ordinary skill in computer art to modify the system of Arelakis to identify relationships between different data sets, as taught by Wroblewski. Skilled artisan would have been motivated to group a plurality of data elements of a same data type into at least one data unit to reduce the overall size of the stored information (See Wroblewski, para. [0005]). In addition, all of the references (Arelakis and Wroblewski) teach features that are directed to analogous art, and they are directed to the same field of endeavor, such as data decompression and compression (See Wroblewski, para. [0004]). This close relation between both references highly suggests an expectation of success.
As to claims 3 and 11, Arelakis in view of Wroblewski discloses wherein the virtual management layer categorizes each set of incoming data into a particular type and selects a compression or decompression technique to maximize efficiency for that particular data type (See Wroblewski, para. [0201], grouping data elements in different data types and selecting appropriate encoding or decoding schemes to reduce memory requirements by exploiting relationships and redundances that are not apparent at the level of individual data elements).
Therefore, it would have been obvious to a person of ordinary skill in computer art to modify the system of Arelakis to categorize each set of incoming data into a particular type and to select a compression or decompression technique to maximize efficiency for that particular data type, as taught by Wroblewski. Skilled artisan would have been motivated to group a plurality of data elements of a same data type into at least one data unit to reduce the overall size of the stored information (See Wroblewski, para. [0005]). In addition, all of the references (Arelakis and Wroblewski) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data decompression and compression (See Wroblewski, para. [0004]). This close relation between both references highly suggests an expectation of success.
As to claims 5 and 13, Arelakis discloses performing parallel processing of different data sets on different computing devices simultaneously (See para. [0086], processing of data segments in parallel).
Claims 2, 4, 6, 7, 10, 12, 14 and 15 are rejected under 35 U.S.C. 103 as unpatentable by Arelakis et al. (WO 2016/186563 A1) and in view of Wroblewski and further in view of Bowman (WO 2021/101798 A1).
As to claims 2 and 10, Arelakis in view of Wroblewski discloses wherein identifying relationships between different data sets (See Wroblewski, para. [0006] and para. [0035], identifying relationships between two or more data units to group data elements in data packs).
Arelakis does not explicitly disclose flagging associated data sets as similar before they enter the compression or decompression process.
Bowman discloses flagging associated data sets as similar before they enter the compression or decompression process (See para. [0047] and para. [0274], a set of flag bits are generated to enable various characteristics of a plurality of data sets or columns, the flag bit value indicates whether or not all of the data values within a column are the same before entering per-column data compression).
Therefore, it would have been obvious to a person of ordinary skill in the computer art to modify the system of Arelakis to flag or mark similar data sets before entering compression or decompression, as taught by Bowman. Skilled artisans would have been motivated to group similar data sets together to facilitate data compression in groups (See Bowman, para. [0274]). In addition, all of the references (Arelakis, Wroblewski and Bowman) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data decompression and compression. This close relation between all the references highly suggests an expectation of success.
As to claims 4 and 12, Arelakis does not explicitly disclose dynamically allocate processing tasks among the plurality of computing devices based on their current workload and capacity.
Bowman discloses dynamically allocate processing tasks among the plurality of computing devices based on current processing capacity and workload for each computing device (See para. [0005], the system automatically analyzes a level of availability of storage space within a node device to determine whether to dynamically adjust the quantity of data buffers of the buffer queue).
Therefore, it would have been obvious to a person of ordinary skill in the computer art to modify the system of Arelakis to allocate processing tasks among the plurality of computing devices based on their current workload and capacity, as taught by Bowman. Skilled artisans would have been motivated to allocate resources of multiple devices dynamically to improve processing and retrieval speed in a distributed storage system (See Bowman, para. [0002]). In addition, all of the references (Wroblewski, Arelakis and Bowman) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data decompression and compression. This close relation between all the references highly suggests an expectation of success.
As to claims 6 and 14, Arelakis discloses wherein maintaining the identified relationships comprises merging data that has been marked as associated back together after compression or decompression has taken place, regardless of which computing devices performed the compression or decompression (See para. [0098], para. [0099], para. [0140], maintain the identified relationships [paths] that have been labelled with conditions related to other columns, the graph node contains a base node that provides information on associated column without any additional condition).
Therefore, it would have been obvious to a person of ordinary skill in the computer art to modify the system of Arelakis to maintain the identified relationships comprises merging data that has been marked, as taught by Wroblewski. Skilled artisan would have been motivated to group a plurality of data elements of a same data type into at least one data unit to reduce the overall size of the stored information (See Wroblewski, para. [0005]). In addition, all of the references (Arelakis, Bowman and Wroblewski) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data decompression and compression (See Wroblewski, para. [0004]). This close relation between all the references highly suggests an expectation of success.
As to claims 7 and 15, Arelakis does not explicitly disclose a load balancing module configured to distribute processing tasks among the plurality of computing devices based on their current workload and capacity.
Bowman discloses a load balancing module configured to distribute processing tasks among the plurality of computing devices based on their current workload and capacity.
(See para. [0005], the system automatically analyzes a level of availability of storage space within a node device to determine whether to dynamically adjust the quantity of data buffers of the buffer queue).
Therefore, it would have been obvious to a person of ordinary skill in the computer art to modify the system of Arelakis to allocate processing tasks among the plurality of computing devices based on their current workload and capacity, as taught by Bowman. Skilled artisans would have been motivated to allocate resources of multiple devices dynamically to improve processing and retrieval speed in a distributed storage system (See Bowman, para. [0002]). In addition, all of the references (Wroblewski, Arelakis and Bowman) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data decompression and compression. This close relation between all the references highly suggests an expectation of success.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as unpatentable by Arelakis et al. (WO 2016/186563 A1) and in view of Wroblewski and further in view of Kalevo (US 2019/0182484 A1).
As to claims 8 and 16, Arelakis discloses for text data types, the selected compression technique comprises one or more Huffman Coding, Arithmetic Coding, or Run-Length Encoding (See para, [0016], Huffman or Arithmetic Coding).
Areklakis does not explicitly disclose wherein:
for image data types, the selected compression technique comprises one or more of Discrete
Cosine Transforms, Wavelet Transforms, or Deflate Algorithms; and for audio data types, the selected compression technique comprises one or more Modified Discrete Cosine Transforms or Linear Prediction; and for video data types, the selected compression technique comprises one or more of H.264 or H.265 encoding.
Kalevo discloses for image data types, the selected compression technique comprises one or more of Discrete Cosine Transforms, Wavelet Transforms, or Deflate Algorithms; and for audio data types, the selected compression technique comprises one or more of Modified Discrete Cosine Transforms or Linear Prediction; and for video data types, the selected compression technique comprises one or more of H.264 or H.265 encoding (See para. [0004], para. [0005]).
Therefore, it would have been obvious to a person of ordinary skill in the computer art to modify the system of Arelakis to select appropriate compression technique for different data types including audio and video, as taught by Kalevo. Skilled artisans would have been motivated to compress data as much as possible and with as high quality as possible to enable cost and energy savings, together with good user experience (See Kalevo, para. [0002]). In addition, all the references (Wroblewski, Arelakis and Kalevo) teach features that are directed to analogous art, and they are directed to the same field of endeavor, such as data decompression and compression. This close relation between all the references highly suggests an expectation of success.
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Matsushita et al. (US 2016/0196075 A1) discloses a storage apparatus includes a semiconductor storage device, and a storage controller coupled to the semiconductor storage device, and which stores data to a logical storage area provided by the semiconductor storage device. The semiconductor storage device includes one or more non-volatile semiconductor storage media, and a medium controller coupled to the semiconductor storage media. The medium controller compresses data stored in the logical storage area and stores the compressed data in the semiconductor storage medium. The size of a logical address space of the logical storage area is larger than the total of the sizes of physical address spaces of the semiconductor storage media.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUK TING CHOI whose telephone number is (571)270-1637. The examiner can normally be reached Monday-Friday 9am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, AMY NG can be reached on 5712701698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YUK TING CHOI/Primary Examiner, Art Unit 2164
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