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 .
Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claim(s) 1-8 is/are method type claim. Claim(s) 9-16 is/are system type claim(s). Claim(s) 17-20 is/are product type claim(s). Therefore, claims 1-20 is/are directed to either a process, machine, manufacture or composition of matter.
Independent claim(s):
Step 2A Prong 1:
Regarding claim(s) 1, 9 and 17, this/these claim(s) recite(s)
analyzing... outputs ...by: performing an inference ... based on a deviation among the outputs, and determining, based on the inference ..., a label for the set of datapoint inputs.
The above limitations appear to be practically implementable in the human mind and is understood to be a recitation of a mental process (user can mentally analyze inputs, make an inference based on deviation in information and determine a label based on the inference).
Step 2A Prong 2:
Regarding claim(s) 1, 9 and 17, this judicial exception is not integrated into a practical application.
Additional elements:
Regarding claim(s) 9 and 17, this/these claim(s) recite(s) integrated circuitry, device and memory to perform the step of determining (mere instructions stored in a generic memory component to apply the exception using a generic computer component (integrated circuit) of a generic computer device).
Regarding claim(s) 1, 9 and 17, this/these claim(s) further recite(s)
executing, ...a set of neural network models, the execution of each of the set of neural network models being based on an input, each datapoint input having a different applied weight value... outputs from the set of neural network models (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a set of neural network models with applied weights to generate an output),
input comprising a received set of datapoint inputs, each datapoint input being an unlabeled equivalent of other datapoint inputs in the set of datapoint inputs (These limitations appear to be directed to the specification of data to be used by the models, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h)),
inference computation (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a inference computation),
storing, by the device, the labeled set of datapoint inputs in a database (Adding insignificant extra-solution activity (storing information) to the judicial exception - see MPEP 2106.05(g)).
The additional element(s) as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are directed to an abstract idea.
Step 2B:
Regarding claim(s) 1, 9 and 17, this/these claim(s) do/does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Regarding claim(s) 9 and 17, this/these claim(s) recite(s) integrated circuitry, device and memory to perform the step of determining (mere instructions stored in a generic memory component to apply the exception using a generic computer component (integrated circuit) of a generic computer device).
Regarding claim(s) 1, 9 and 17, this/these claim(s) further recite(s)
executing, ...a set of neural network models, the execution of each of the set of neural network models being based on an input, each datapoint input having a different applied weight value... outputs from the set of neural network models (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a set of neural network models with applied weights to generate an output),
input comprising a received set of datapoint inputs, each datapoint input being an unlabeled equivalent of other datapoint inputs in the set of datapoint inputs (These limitations appear to be directed to the specification of data to be used by the models, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h)),
inference computation (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a inference computation),
storing, by the device, the labeled set of datapoint inputs in a database (Adding insignificant extra-solution activity (storing information) to the judicial exception - see MPEP 2106.05(g). Furthermore, MPEP 2106.05(d)(II) indicate that merely “Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer).
The additional element(s) as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are not patent eligible.
Step 2A Prong 1, Dependent claims:
Regarding claim(s) 2, 10, 18, this/these claim(s) recite(s) wherein the label of the set of datapoint inputs comprises an indication that the deviation of the outputs is within a threshold value, and
Regarding claim(s) 5, 13, 20, this/these claim(s) recite(s) merging, ... the outputs from the set of neural network models, wherein the analysis of the outputs is based on the merged outputs.
The above limitations appear to be recitation of a mental process-user can mentally assign labels based on deviation and threshold, merge information and analyze merger information.
Step 2A Prong 2, Dependent claims:
Regarding claim(s) 3, 11, this/these claim(s) recite(s) training the set of neural network models based on the labeled set of datapoints (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a set of neural network models with previously determined data).
Regarding claim(s) 6, 14, this/these claim(s) recite(s) wherein the set of neural network models are part of a systolic array,
Regarding claim(s) 7, 15, this/these claim(s) recite(s) wherein each of the neural network models comprise a shape, wherein the shape is similar for each neural network model.
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of model architecture in terms of systolic array and applying weights.
Regarding claim(s) 8and 16, this/these claim(s) recite(s)
querying data samples..., wherein the received set of datapoint inputs corresponds to a result of the query (insignificant extra solution activity of requesting information and mere data gathering),
stored within the database (mere data stored in a generic computer component (database) to apply the exception).
Step 2B, Dependent claims:
Regarding claim(s) 3, 11, this/these claim(s) recite(s) training the set of neural network models based on the labeled set of datapoints (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a set of neural network models with previously determined data).
Regarding claim(s) 6, 14, this/these claim(s) recite(s) wherein the set of neural network models are part of a systolic array,
Regarding claim(s) 7, 15, this/these claim(s) recite(s) wherein each of the neural network models comprise a shape, wherein the shape is similar for each neural network model.
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of model architecture in terms of systolic array and applying weights.
Regarding claim(s) 8and 16, this/these claim(s) recite(s)
querying data samples..., wherein the received set of datapoint inputs corresponds to a result of the query (insignificant extra solution activity of requesting information and mere data gathering, this insignificant extra solution activity is well understood routine and conventional activity, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362),
stored within the database (mere data stored in a generic computer component (database) to apply the exception).
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.
Claim(s) 1, 9, and 17 recite(s) “a received set of datapoint inputs, each datapoint input being an unlabeled equivalent of other datapoint inputs in the set of datapoint inputs”. It is unclear what is considered “equivalent” in the context of datapoint inputs, rendering the claim(s) indefinite.
Claim(s) 2, 10 and 18 recite(s) “deviation of the outputs is within a threshold value”. It is unclear what constitutes being “within” and threshold value, as the term “within” is associated with a range rather than a value, rendering the claim(s) indefinite.
For examination purposes the examiner has interpreted “deviation of the outputs is within a threshold value” to be “deviation of the outputs is equal to or below a threshold value”.
Claim(s) 4, 12 and 19 recite(s) “wherein the labeled set of datapoint inputs requires further retraining”. It is unclear what constitutes training or retraining data, rather than training a neural network, rendering the claim(s) indefinite.
Claim(s) 7 and 15 recite(s) “wherein the shape is similar for each neural network model”. The term “similar” in claims 7 and 15, is a relative term which renders the claim indefinite. The term “similar” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim(s) 13 recite(s) “the physical processor”. There is lack of antecedent basis for this limitation in these claim(s), rendering the claim(s) indefinite.
Claim(s) 2-8, 10-16 and 18-20, do not contain claim limitations that cure the indefiniteness of claim(s) 1, 9, and 17, respectively, and therefore are also indefinite under 35 U.S.C. 112(b).
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 (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 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) 1-3, 5, 8-11,13,16-18, 20, is/are rejected under 35 U.S.C. 103 as being unpatentable over Snow (US 20220391765 A1), in view of Datt (US 20240134846 A1) and Lehman (US 20240046152 A1).
Regarding claim 1, Snow teaches a method comprising (Snow [23, 71-73] method performed by system processor executing instructions stored in memory):
executing, by a device, a set of neural network models, the execution of each of the set of neural network models being based on an input comprising a received set of datapoint inputs, each datapoint input being an unlabeled equivalent of other datapoint inputs in the set of datapoint inputs ... (Snow [4, 71] method may be executed by a device, to determine labels for data sampling in active labeling, Snow [22, 42, 45] sub-models may each be neural networks and may form an ensemble model, sub-models may each process dataset and provide respective output);
analyzing, by the device, outputs from the set of neural network models by: performing an inference computation based on a deviation among the outputs (Snow [42] sub-model outputs may be analyzed to determine metrics of outputs as a whole, metrics may include inferring confidence based on standard deviation), and
determining, based on the inference computation, a label for the set of datapoint inputs (Snow [43] pseudo label or ground-truth label is added to datapoint based on based on metrics meeting or not meeting threshold values).
Snow does not specifically teach each datapoint input having a different applied weight value; storing, by the device, ... labeled set of datapoint inputs in a database.
However Datt teaches each datapoint input having a different applied weight value (Datt [43] different datapoint inputs from different sources may have different applied weights to account for confidence in a data source, Datt [125, 151, 156] models in ensemble model provide outputs for dataset, dataset labels may be stored in a database, allows past performance to be compared with new performance).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Datt of each datapoint input having a different applied weight value, into the invention suggested by Snow; since both inventions are directed towards using ensemble models to generate outputs for a set of datapoint inputs, and incorporating the teaching of Datt into the invention suggested by Snow would provide the added advantage of using different weights to account for confidence in a data source and past performance to be compared with new performance, and the combination would perform with a reasonable expectation of success (Datt [43, 156]).
Snow and Datt does not specifically teach storing, by the device, the labeled set of datapoint inputs in a database.
However Lehman teaches storing, by the device, the labeled set of datapoint inputs (Lehman [103, 108, 125] inference labels for dataset may be stored, storing inference labels allows labels to be used for (re-)training).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Lehman of storing, by the device, the labeled set of datapoint inputs in a database, into the invention suggested by Snow and Datt; since both inventions are directed towards active learning and adding inference labels to a set of datapoint inputs, and incorporating the teaching of Lehman into the invention suggested by Snow and Datt would provide the added advantage of allowing labels to be used for (re-)training, and the combination would perform with a reasonable expectation of success (Lehman [103, 108, 151]).
Regarding claim 2, Snow, Datt and Lehman teach the invention as claimed in claim 1 above.
Snow further teaches wherein the label of the set of datapoint inputs comprises an indication that the deviation of the outputs is within a threshold value (Snow [11, 33, 43, 44, 52] use of pseudo label indicates that the deviation (confidence) measure is less than threshold)
Regarding claim 3, Snow, Datt and Lehman teach the invention as claimed in claim 2 above.
Snow further teaches training the set of neural network models based on the labeled set of datapoints (Snow [46] labelled dataset may be used to train the sub-models).
Regarding claim 5, Snow, Datt and Lehman teach the invention as claimed in claim 1 above.
Snow further teaches merging, by the device, the outputs from the set of neural network models, wherein the analysis of the outputs is based on the merged outputs (Snow [42] analysis may be based on distribution of outputs of sub-models).
Regarding claim 8, Snow, Datt and Lehman teach the invention as claimed in claim 1 above. Snow does not specifically teach querying data samples stored within the database, wherein the received set of datapoint inputs corresponds to a result of the query
However Datt teaches querying data samples stored within the database, wherein the received set of datapoint inputs corresponds to a result of the query (Datt [62, 123, 128] input dataset may be received by querying a database).
Claim 9 is directed towards a system executing instructions similar in scope to the instructions performed by the method of claim 1 and is rejected under the same rationale. Lehman further teaches a system comprising: at least one integrated circuit configured to (Lehman [195-201] method may be executed using system integrated circuits executing instructions).
Claim(s) 10, 11, 13, 16, is/are dependent on claim 9 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 2, 3, 5, 8, respectively, and is/are rejected under the same rationale.
Claim 17 is directed towards a medium storing instructions similar in scope to the instructions performed by the method of claim 1, and is rejected under the same rationale.
Snow further teaches non-transitory computer-readable medium comprising one or more computer-executable instructions that, when executed by at least one processor of a computing device, performs a method (Snow [23, 71-73] method performed by system processor executing instructions stored in memory).
Claim(s) 18 and 20 is/are dependent on claim 17 above, is/are directed towards a medium storing instructions similar in scope to the instructions performed by the method of claim(s) 2 and 5 respectively, and is/are rejected under the same rationale.
Claim(s) 6, 7, 14, 15, is/are rejected under 35 U.S.C. 103 as being unpatentable over Snow (US 20220391765 A1) in view of Datt (US 20240134846 A1) and Lehman (US 20240046152 A1), and further in view of Kim (US 20240078418 A1).
Regarding claim 6, Snow, Datt and Lehman teach the invention as claimed in claim 1 above. Snow does not specifically teach wherein the set of neural network models are part of a systolic array
However Kim teaches wherein the set of neural network models are part of a systolic array (Kim [86] ensemble model, Kim [246, 250, 251] models can use systolic arrays which allows parallel execution of for loops).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Kim of wherein the set of neural network models are part of a systolic array, into the invention suggested by Snow, Datt and Lehman; since both inventions are directed towards using ensemble models, and incorporating the teaching of Kim into the invention suggested by Snow, Datt and Lehman would provide the added advantage of allowing parallel execution of for loops, and the combination would perform with a reasonable expectation of success (Kim [86, 246, 250, 251]).
Regarding claim 7, Snow, Datt and Lehman teach the invention as claimed in claim 1 above. Snow does not specifically teach wherein each of the neural network models comprise a shape.
However Kim teaches wherein each of the neural network models comprise a shape, ... (Kim [86] ensemble model, Kim [129, 245] models may have shapes of an input feature map for layers, based on input data).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Kim of wherein each of the neural network models comprise a shape, wherein the shape is similar for each neural network model, into the invention suggested by Snow, Datt and Lehman; since both inventions are directed towards using ensemble models, and incorporating the teaching of Kim into the invention suggested by Snow, Datt and Lehman would provide the added advantage of allowing input feature map for each layer to be determined based on the input, and the combination would perform with a reasonable expectation of success
(Kim [86, 129, 245]).
Claim(s) 14, 15 is/are dependent on claim 9 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 6, 7, respectively, and is/are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Han (US 20210034971 A1) discloses inferring a class label for a global dataset.
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SANCHITA ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146
Prosecution Insights