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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed applications, Application No. 16/747,359 and 62/794,922 fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application: Independent Claims 1, 8, and 15 each recite the limitation a sequence of logic gates specifically for the first data block on which the collection of neural networks is trained. The only mentions of logic gates in the disclosure of the parent non-provisional application are in [0030], which states that “Each data block may be replaced by a cybernetic engram … including a collection of neural networks and logic gates configured as primitive functions” and [0071], with a similar statement. The only mention of training a neural network related to a data block occur in [0113], which makes no mention of logic gates nor to a collection of neural networks, nor to a sequence.
If applicant believes that the claims do have proper support in the parent application, it is requested for that support to be clearly pointed out in the next response.
Claim Interpretation
The specification reads: [0071] … a pool of logic gates … The pool of gates 200 may include primitive functions, for example read, write, copy, go to, if, for, while, print, etc. … to implement a transformation.
Objection to the Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
Independent Claims 1, 8, and 15 each recite the limitation a sequence of logic gates specifically for the first data block on which the collection of neural networks is trained. For the same reasons given for a lack of support in the priority application, these limitations also appear to lack written description support in the present specification.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent Claims 1, 8, and 15 each recite the limitation a sequence of the logic gates. There is insufficient antecedent basis for this limitation in the claim, as logic gates were never previously recited. For the purpose of examination, the claim will be interpreted as if they had read a sequence of logic gates.
Further, the independent claims are indefinite because it is unclear whether the phrase on which the neural network is claimed modifies the sequence of logic gates or whether it modifies the first data block. If the phrase modifies the sequence of logic gates, the limitation appears to lack further support in the specification (logic gates upon which the collection of neural networks does not appear in the specification). If the phrase modifies the first data block, then the limitation also lacks proper antecedent basis in the claims, as no first data block upon which the collection of neural networks is trained has previously been recited.
For the purpose of examination, the limitation the collection of neural networks including a sequence of the logic gates specifically for the first data block on which the collection of neural networks is trained will be interpreted to require: a data block; a sequence of logic gates related to the data block; and training of neural networks using either the data block or the sequence of logic gates.
Dependent claims are rejected for inheriting the indefiniteness of a parent claim.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dirac, US PG Pub 2015/0379072.
Regarding Claim 1, Dirac teaches a method of implementing a collection of neural networks by at least one processor of a computing device (Dirac, Fig. 1, “MLS artifact repository” is a collection of neural networks and other machine learning service algorithms such as recipes, see Fig. 11 & [0047], “a model that has already been created and stored in an MLS artifact repository” & [0053], “a wide variety of machine learning algorithms may be supported, including for example … neural network algorithms” & Fig. 33, “Processor,” see [0039]), comprising: receiving an operation request for a data block (Dirac, Abstract, “A record extraction request for a data set is received at a machine learning service. A plan to perform one or more chunk-level operations (such as sampling, shuffling, splitting, …) on chunks of the data set is generated”); and executing a first function of the collection of neural networks for the first data block in response to the operation request of the first data block (Dirac, Abstract, “A set of data transfers that results in a particular chunk being stored in a particular server’s memory is initiated to implement the first chunk-level operation of the sequence”), the collection of neural networks including a sequence of logic gates specifically for the first data block on which the collection of neural networks is trained (Dirac, Fig. 1, “MLS artifact repository”/collection of neural networks contains “recipes”/sequence of logic gates for operating on the data blocks, see Fig. 18 & [0022], “a machine learning service that supports re-usable recipes for data set transformation” for [0042], “a relatively straightforward recipe language may be supported, allowing MLS users to indicate various feature processing steps that they wish to have applied on datasets” for training the neural networks, see Fig. 26 & [0043], “recipes (e.g. descriptors of feature processing transformations to be applied to input data for training models)” & [0118], “operations may sometimes have to be performed in a sequence on an input data set”).
Regarding Claim 2, Dirac teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Dirac further teaches wherein executing the first function of the collection of neural networks for the first data block comprises verifying at least one of the first data block (Dirac, [0244], “verify that the particular data set meets a run-time acceptance criterion of the recipe”), a user requesting the operation request (Dirac, [0088], “verifying that the quota or quotas of the client on whose behalf the job is to be run have not been exhausted” ) or the operation request (Dirac, [0215], “verifying prior to scheduling the particular operation, that a resource quota of the client has not been exhausted”).
Regarding Claim 3, Dirac teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Dirac further teaches wherein executing the first function of the collection of neural networks for the first data block comprises authenticating at least one of the first data block, a user requesting the operation request (Dirac, [0136], “Authorization/ authentication metadata to be used to be able to obtain read access to the data set may be provided by the client”), or the operation request (Dirac, [0059], “Authorization and authentication of client request may be performed”).
Regarding Claim 4, Dirac teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Dirac further teaches wherein executing the first function of the collection of neural networks for the first data block comprises authorizing at least one of a user requesting the operation request for the first data block (Dirac, [0059], “Authorization and authentication of client request may be performed”) or a process requesting the operation request for the first data block (Dirac, [0136], “Authorization/ authentication metadata to be used to be able to obtain read access to the data set may be provided by the client”).
Regarding Claim 5, Dirac teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Dirac further teaches wherein receiving the operation request for the first data block comprises receiving an operation request for a data including a plurality of data blocks including the first data block and a second data block; and the method further comprises executing a second function of the collection of neural networks for the second data block in response to the operation request for the data, the collection of neural networks including a sequence of the logic gates specifically for the second data block on which the collection of neural networks is trained (Dirac, Abstract, “A plan to perform one or more operations on chunks of the data set” where the recopies selected for transforming the data and training the machine learning models are selected specifically for the second data block as well as specifically for the first data block).
Regarding Claim 6, Dirac teaches the method of Claim 5 (and thus the rejection of Claim 5 is incorporated). Dirac further teaches wherein the first function of the collection of neural networks for the first data block and the second function of the collection of neural networks for the second data block are a same function (Dirac, [0130], “60% of each chunk’s observation records may be sampled in a single intra-chunk sampling step” denotes that every chunk has a same operation performed on it, also see Fig. 18 where each chunk has the same recipes performed on it).
Regarding Claim 7, Dirac teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Dirac further teaches wherein the operation request for the first data block includes one of reading the first data block, writing the first data block (Dirac, Abstract, “A set of data transfers that results in a particular chunk being stored in a particular server’s memory is initiated in response to the first chunk-level operation of the sequence”), copying the first data block (Dirac, Abstract, “A set of data transfers that results in a particular chunk being stored in a particular server’s memory is initiated in response to the first chunk-level operation of the sequence”), or transforming the first data block (Dirac, Abstract, “one or more chunk-level operations (such as sampling, shuffling, splitting, or partitioning for parallel computation) on chunks of the data set”) and the method further comprising executing a second function of the collection of neural networks for the first data block for implementing the operation request for the first data block (Dirac, Abstract, “A second operation such as another filtering operation or a feature processing operation is performed on a result set of the first chunk-level operation”).
Claims 8-14 recite a computing device, comprising at least one processor configured with processor-executable instructions for performing precisely the methods of Claims 1-7, respectively. As Dirac performs their method on a cloud computing platform with multiple processors (Dirac, Fig. 33), Claims 8-14 are rejected for reasons set forth in the rejections of Claims 1-7, respectively.
Similarly, Claims 15-20 recite a non-transitory processor-readable medium having stored thereon processor-executable instructions configured to cause at least one processor to perform precisely the methods of Claims 1-6, respectively. As Dirac teaches such a medium (Dirac, [0175], “a computer-accessible medium configured to store program instructions … for implementing embodiments of the corresponding methods and apparatus”), Claims 15-20 are rejected for reasons set forth in the rejections of Claims 1-6, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: Documents cited by the examiner in the parent application, and thus by the applicant in the Information Disclosure Statement dated 8/2/2024, are each relevant to the specification. Particularly, Goyal et al, “DeepZip: lossless data compression using recurrent neural networks” is relevant to the concept of reproducing a data block using a trained neural network, prominent in the disclosure but not mentioned in the current claims.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN M SMITH whose telephone number is (469)295-9104. The examiner can normally be reached Monday - Friday, 8:00am - 4pm Pacific.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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