DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to pending claims 1-20 filed 7/9/2026.
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.
Claim(s) 1-6, 9-12, 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Chattopadhyay (US 10817794 B1) in view of Niu (US 20180121120 A1).
For claim 1, Chattopadhyay discloses: a method of offloading knowledge base creation, comprising (fig.2 gives overview of a method for creating model-based knowledge base creation):
receiving a data stream in a memory of the storage device(fig.1A-B c.8¶2-3: fig.1A contemplates receiving data streams from remote devices for processing, with fig.1B contemplating receiving at storage devices);
identify a sequence of patterns in the data stream based on a time dimension of the data stream (c.10 ¶1: acquiring input data streams, i.e., patterns, to derive causal dependance, hence, temporally; see also c.10 ¶3: temporal series; see also examples in seismic prediction (c.16 last ¶), stock market, neural connectivity (c.17 ¶1-3), hence, temporal signals), wherein the time dimension reflects a chronological ordering of data items in the data stream (ibid);
recognizing a plurality of information contexts corresponding to the sequence of patterns based on analyzing neighboring patterns of any particular pattern in the sequence of patterns (fig.3A, c.11:last ¶ discloses the construction of state transition models PFSA from input streams, see also c.12¶4-10: determination of context sequences, hence, recognizing information contexts based on analyzing neighboring a patterns of a particular context pattern in the sequence of patterns); and
determining causal relations among the sequence of patterns based on detecting repetitions of any pair of information contexts among the plurality of information contexts, wherein the causal relations comprise a plurality of reason-consequence pairs (c.12¶4-10: a PFSA is constructed, this PFSA being a diagram of causal relationships between patten sequences and being constructed based on observations of patterns in order to construct transition probabilities between pairs of contexts, the causal relations being a plurality of causal or reason-consequence pairs);
storing, in a knowledge base maintained in the memory of the storage device, reason-consequence pairs corresponding to the detected repetitions of pairs of information contexts (fig.2, c.9 last¶-c.10 ¶1: generating prediction model, such as the PFSA’s shown in figs.3-6, to generate predictions, hence, storing a model for generating inference, the PFSA comprising a directed graph comprising reason-consequence pairs corresponding to detected repetitions of patterns in the information contexts, the storing taking place in the memory of storage device of fig.1B); and
generating using the knowledge base maintained in the memory of the storage device, predictions of future states of the data stream based on at least a portion of the causal relations (fig.2, c.10 ¶1: generating predictions based on inferred PFSAs; see also c.13:25-35).
Chattopadhyay does not disclose: wherein the offloading is into a storageand
The receiving including partitioning the data stream into a plurality of portions, wherein each of the plurality of portions corresponds to one of a plurality of data processing units (DPUs) located in the storage device;
wherein the identifying is via processing each of the plurality of portions in parallel and independently by a corresponding one of the DPU’s without initiation by a CPU external to the storage device to;
wherein the recognizing, determining, generating are by the DPUs;
wherein the generating takes place without transferring the data stream to the CPU.
Niu discloses: wherein the offloading is into a storage(fig.6, 0082-86 contemplates ad hoc processing of jobs via DPUs, such an ad hoc construction hence reducing CPU overhead, such as in a hybrid network (0084-85)) and
partitioning the data stream into a plurality of portions, wherein each of the plurality of portions corresponds to one of a plurality of data processing units (DPUs) located in the storage device (fig.6:600-604, 0082-83: ad hoc partitioning into portions on a storage device, see fig.3);
wherein the identifying is via processing each of the plurality of portions in parallel and independently by a corresponding one of the DPU’s without initiation by a CPU external to the storage device to (fig.6:600-604, 0082-85: ad hoc control of jobs without CPU initiation, hence, combination with the tasks of Chattopadhyay yielding performing of identifying in such a manner; see also 0045, 0066 describing DPU parallelization, hence, processing);
wherein the recognizing, determining, generating are by the DPUs (ibid); wherein the generating takes place without transferring the data stream to the CPU (ibid).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Chattopadhyay by incorporating the DPU cluster hardware of Niu. Both concern the art of machine learning, and the incorporation would have, according to Niu, provide greater processing capacity via reconfigurable DPU pools (0085).
For claim 2, Chattopadhyay modified by Niu discloses the method of claim 1, as described above. Chattopadhyay modified by Niu further discloses: detecting, by at least one of the DPUs (Niu fig.6), all instances of a particular information context's relations with remaining information contexts among the plurality of information contexts (Chattopadhyay c.12¶4-10 contemplates construction of a PFSA to model the entire ergodic system); and detecting, by the at least one of the DPUs, whether any detected relations are reproducible (ibid: contemplates determining causation based on repeated observations, with fig.2 contemplating prediction, c.16 ¶5 (“Model Validation”) contemplating validation of reproducibility).
For claim 3, Chattopadhyay modified by Niu discloses the method of claim 2, as described above. Chattopadhyay modified by Niu further discloses: generalizing reproducible relations to the causal relations (Chattopadhyay c.12¶4-10: generalizing reproducible information probabilistically to PFSA causal relations); and storing the causal relations into a knowledge base in the storage device (fig.2: models are stored for prediction, such has via hardware of fig.1A-B, with Niu fig.6 disclosing the use of DPU storage devices).
For claim 4, Chattopadhyay modified by Niu discloses the method of claim 1, as described above. Chattopadhyay modified by Niu further discloses: creating a time map of causality by connecting a subset of the plurality of reason-consequence pairs into a sequence based on the time dimension of the data stream (c.12¶4-10: the PFSAs constitute time maps of causality of directional sequences).
For claim 5, Chattopadhyay modified by Niu discloses the method of claim 4, as described above. Chattopadhyay modified by Niu further discloses: detecting whether the sequence of reason-consequence pairs is reproducible based at least in part on a predetermined similarity threshold (Chattopadhyay c.12¶4-10 discloses construction of PFSA which is a directed graph comprising linked reason-consequence pairs, each corresponding to a particular string or context string, the algorithm determining a set of links between states based on a clustering distance threshold, see c.12 step (3), hence, the reproducibility of the various reason-consequence pair states, whether they are in fact in the PFSA, being based on a cluster similarity threshold); and generalizing a reproducible sequence of reason-consequence pairs and storing the generalized sequence of reason-consequence pairs into a knowledge base (fig.2: creating model, see also c.12 step (5)) in the storage device (Chattopadhyay fig.1B, Niu fig.1-2).
For claim 6, Chattopadhyay modified by Niu discloses the method of claim 1, as described above. Chattopadhyay modified by Niu further discloses: wherein the generating the predictions of future states of the data stream based on at least the portion of the causal relations comprises generating the predictions indicative of the future states of the data stream based on a knowledge base created in the storage device (Chattopadhyay fig.2, c.10¶1, c.13:25-35): predictions are generate based on the PFSA causality model, with Niu figs.1-2 disclosing storage device).
Claims 9-12, 15-18 recite systems and computer media corresponding to the above methods and are hence likewise rejected. Furthermore, Chattopadhyay discloses:
for claim 9: a system, comprising:
at least one processor (fig.1B:101); and
at least one memory comprising computer-readable instructions that upon execution by the at least one processor cause the computing device to perform operations (fig.1B:102); for claim 15: a non-transitory computer-readable storage medium, storing computer-readable instructions that upon execution by a processor cause the processor to implement operations (fig.1B:101-102, c.9¶4).
Claim(s) 7-8, 13-14, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chattopadhyay (US 10817794 B1) in view of Niu (US 20180121120 A1) in view of Sato (US 20180189654 A1).
For claim 7, Chattopadhyay modified by Niu discloses the method of claim 6, as described above. Chattopadhyay modified by Niu does not discloses: determining whether the predictions are correct based on comparing the predictions with new future states associated with the data stream; and
storing at least a subset of the predictions to the knowledge base in response to determining that the at least a subset of the predictions are correct.
Sato discloses: determining whether the predictions are correct based on comparing the predictions with new future states associated with the data stream (fig.3, 0028-31: collecting new data and calculating updated accuracy data of predictors); and
storing at least a subset of the predictions to the knowledge base in response to determining that the at least a subset of the predictions are correct (fig.3:124, 0032; fig.4:103, 0040: predictions corresponding to the correct predictions are ranked and sorted for storage).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Chattopadhyay modified by Niu by incorporating the prediction updating and storage technique of Sato. Both concern the art of predictive machine learning, and the incorporation would have, according to Sato, raise predictive accuracy and robustness via multiple models (0067-68).
For claim 8, Chattopadhyay modified by Niu modified by Sato discloses the method of claim 7, as described above. Chattopadhyay modified by Niu further discloses: generating hypotheses associated with the data stream based on the knowledge base (fig.12, c.10¶1, c.16 last ¶-c.17¶4: generating predictive hypotheses based on knowledge base).
Claims 13-14, 19-20 recite systems and computer media corresponding to the above methods and are hence likewise rejected. Furthermore, Chattopadhyay discloses:
Response to Arguments
In the remarks dated 7/9/2026, Applicant argued:
The 101 rejections are not applicable.
Examiner agrees and the rejections are withdrawn.
2. Chattopadhyay’s disclosure of PFSA / XPFSA does not disclose reason-consequence pairs.
Examiner respectfully disagrees. A POSITA would recognize that state and transition diagrams comprise pairs of connected states. Furthermore, the reliance on such models for reasoning indicates storing, such as in the storage device of Niu. Further clarification is needed to exclude Chattopadhyay.
3. Niu does not disclose the amended matter directed to portioning a data stream.
Examiner respectfully disagrees. The ad-hoc DPU system of Niu fig.6, in disclosing the partitioning of jobs, when combined with Chattopadhyay, would yield a technique where jobs associated with incoming data streams would be partitioned, hence, the partitioning of data streams, such as over the course of operation.
4. Niu does not disclose processing in parallel the portioned portions of the data stream.
Examiner respectfully disagrees. Parallelization is claimed at a high level, hence, the cited potions of Niu disclosing various parallelization aspects of the DPU would disclose the parallelization of the portioned data sets, independently by each of the DPU processors in an ad hoc way. Further clarification of the parallelization aspects would be needed to overcome Niu.
5. Niu does not disclose storing of a knowledge base.
Examiner respectfully disagrees. Implementation of the technique of Chattopadhyay on the storage device of Niu would necessarily involve storing of the PFSA model on the storage system of Niu, such as for further inference.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Dubeyko (US 20200151020 A1) discloses data processing architecture based on DPUs. Desai (US 20170147931 A1) discloses cloud-based root cause inference.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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/LIANG LI/
Primary examiner AU 2143
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