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 .
DETAILED ACTION
Claims 1-20 are pending.
This action is response to the application filed on January 02, 2025.
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation of United States Patent Application No. 18/312,419, filed on May 4, 2023, which is a continuation of United States Patent Application No. 17/068,248, filed on Oct. 12, 2020, issued as US Pat. No. 11,645,571 on May 9, 2023, which is a continuation of United States Patent Application No. 16/048,388, filed on July 30, 2018, issued as US Pat. No. 10,817,259 on Oct. 27, 2020, which claims the benefit of priority of U.S. Provisional Patent Application No. 62/539,334, filed on July 31, 2017, U.S. Provisional Patent
Application No. 62/562,398, filed on Sep. 23, 2017, U.S. Provisional Patent Application No. 62/562,401, filed on Sep. 23, 2017, U.S. Provisional Patent Application No. 62/581,744, filed on Nov. 5, 2017, and U.S. Provisional Patent Application No. 62/610,290, filed on Dec. 26, 2017. The entire contents of all of the above-identified applications are herein incorporated by reference.
Information Disclosure Statement
Information Disclosure Statement(s) (PTO/SB/08a and/or PTO/SB/08b) Paper No(s)/Mail Date: Receipt Date 01/02/2025.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As per claim 1:
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites for employing inference models, the system comprising: at least one processor configured to: receive information associated with available processing resources associated with at least one device configured to utilize inference models; select at least one inference model based on the information associated with the available processing resources; and cause the at least one device to utilize the selected at least one inference model.
Step 1: Statutory Category:
Yes, the claim recites a system.
Step 2A – Prong 1: Judicial Exception Recited:
The limitations of the claim recite system for employing inference models, the system comprising: at least one processor configured to: receive information associated with available processing resources associated with at least one device configured to utilize inference models; select at least one inference model based on the information associated with the available processing resources; and cause the at least one device to utilize the selected at least one inference model.
“receive”, “select’, and “cause” may be performed by processes which may be practically performed in the human mind using observation, evaluation, judgment, and opinion. Such mental observations or evaluations fall within the “mental processes” grouping of abstract ideas.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. As per MPEP 2106,04(a)(2) III “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011)”. Accordingly, the claim recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Application
This judicial exception is not integrated into a practical application. The additional elements of “cause the at least one device to utilize the selected at least one inference model” in the claim limitations do not improve the functioning of a computer, or an improvement to other technology and is merely using a computer as a tool to perform the concept. Accordingly, there are no additional elements in the claim limitations that integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Step 2B: Claims provide an Inventive Concept (significantly more than the judicial exception).
The claims 1, 18 and 20 do not have additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements that are sufficient to amount to significantly more than the judicial exception. The claims 1, 18 and 20 are not patent eligible.
As per dependent claims 2-17 and 19 depend directly or indirectly on the independent claim 1 and these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
The dependent claims 2-17 and 19 states additional steps “2. The system of claim 1, receive an update to the information associated with the available processing resources; update the selected at least one inference model based on the update to the information associated with the available processing resources to obtain at least one updated inference model; cause the at least one device to utilize the at least one updated inference model. 3. The system of claim 1, wherein the at least one processor is further configured to: receive an update to the information associated with the available processing resources; determine that the update to the information associated with the available processing resources is below a selected threshold; and based on said determination, withhold causing the at least one device to utilize the at least one updated inference model. 4. The system of claim 1, wherein the information associated with the available processing resources is information associated with available memory size. 5. The system of claim 1, wherein the information associated with the available processing resources is information associated with available processing units capabilities. 6. The system of claim 1, wherein the information associated with the available processing resources is information associated with available computer network resources. 7. The system of claim 1, wherein the information associated with the available processing resources is information associated with a number of artificial neurons evaluations per a time unit, and the selection of the at least one inference model is based on a number of artificial neurons in the at least one inference model. 8. The system of claim 1, wherein the information associated with the available processing resources is information associated with a distribution of available processing resources, and the 1 selection of the at least one inference model is based on a ratio of cases the at least one inference model can be evaluated within a selected time duration according to the distribution. 9. The system of claim 1, wherein utilizing the selected at least one inference model comprises training a machine learning algorithm using training examples to obtain at least part of the selected at least one inference model. 10. The system of claim 1, wherein utilizing the selected at least one inference model comprises applying input data to the selected at least one inference model to obtain at least one inferred value. 11. The system of claim 1, wherein the selection of the at least one inference model is based on a number of artificial neurons in the at least one inference model. 12. The system of claim 1, wherein the selection of the at least one inference model is based on a ratio of cases the at least one inference model can be evaluated within a selected time duration according to the distribution. 13. The system of claim 1, wherein the utilizing the selected at least one inference model comprises generating the selected at least one inference model. 14. The system of claim 1, wherein the utilizing the selected at least one inference model comprises selecting at least part of the selected at least one inference model of a plurality of alternative inference models. 15. The system of claim 1, wherein the utilizing the selected at least one inference model comprises selecting training examples based on the information associated with the available processing resources, and training a machine learning algorithm using the training examples to obtain at least part of the selected at least one inference model. 16. The system of claim 1, wherein the utilizing the selected at least one inference model comprises applying input data to the selected at least one inference model to obtain at least one inferred value, and the input data comprises image data captured by the at least one device. 17. The system of claim 1, wherein the causing the at least one device to utilize the selected at least one inference model comprises transmitting information associated with the selected at least one inference model to the at least one device.
Therefore, the claims 1-20 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Claim Interpretation; Broadest Reasonable Interpretation
CLAIMS MUST BE GIVEN THEIR BROADEST REASONABLE INTERPRETATION IN LIGHT OF THE SPECIFICATION
During patent examination, the pending claims must be "given their broadest reasonable interpretation consistent with the specification." The Federal Circuit’s en banc decision in Phillips v. AWH Corp., 415 F.3d 1303, 1316, 75 USPQ2d 1321, 1329 (Fed. Cir. 2005) expressly recognized that the USPTO employs the "broadest reasonable interpretation" standard: The Patent and Trademark Office ("PTO") determines the scope of claims in patent applications not solely on the basis of the claim language, but upon giving claims their broadest reasonable construction "in light of the specification as it would be interpreted by one of ordinary skill in the art.".
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 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 JULIAN et al (US 20150324686 A1).
With respect to claims 1, 18 and 20, JULIAN et al teaches
receive information associated with available processing resources associated with at least one device configured to utilize inference models (FIG. 1, FIG. 6-7, [0130] FIG. 10, input data computed based on the input data 1002 to provide distributed learned features 1004 as DNN feature model learning using inference model. [0038] level 102 may receive an input signal 108 that may be generated. The signal 108 may represent an input current of the level 102. [0090] the processing unit 704 to receive data from a server. [0011] to receive data from a server inference model to compute an inference model. to compute one or more model parameter updates based on the inference);
select at least one inference model based on the information associated with the available processing resources ([0011] based on a shared inference model to compute an inference model to compute one or more model parameter updates based on the inference. to transmit data based on the model parameter update(s) to the server. [0117] user device maintains one model, to a model update (e.g., W.sub.1)); and
cause the at least one device to utilize the selected at least one inference model ([0006] receiving model updates from one or more users. updated model based on a previous model and the model updates. transmitting data related to a subset of the updated model to user(s) based on the updated model. [0082] shared inference model, generating a model including one or more model parameters based on the received data, computing an inference model, computing one or more model parameter updates based on the inference, and/or transmitting data based on the model parameter update(s) to the server).
With respect to claim 2, JULIAN et al teaches receive an update to the information associated with the available processing resources; update the selected at least one inference model based on the update to the information associated with the available processing resources to obtain at least one updated inference model; cause the at least one device to utilize the at least one updated inference model ([0006] The method also includes computing an updated model based on a previous model and the model updates to transmitting data related to a subset of the updated model to user(s) based on the updated model. [0007] to compute an updated model based on a previous model and the model updates. to transmit data related to a subset of the updated model to user(s) based on the updated model).
With respect to claim 3, JULIAN et al teaches receive an update to the information associated with the available processing resources; determine that the update to the information associated with the available processing resources is below a selected threshold; and based on said determination, withhold causing the at least one device to utilize the at least one updated inference model ([0041] within a certain time period to depolarize the membrane potential above a threshold, an action potential occurs in the neuron to prevent the membrane potential from reaching a threshold).
With respect to claim 4, JULIAN et al teaches information associated with the available processing resources is information associated with available memory size ([0021] neural network where a memory may be interfaced with individual distributed. [0144] the number of models maintained is exemplary, and any number of models may be maintained according to resource availability).
With respect to claim 5, JULIAN et al teaches the information associated with the available processing resources is information associated with available processing units capabilities ([0144] the number of models maintained is exemplary, and any number of models may be maintained according to resource availability).
With respect to claim 6, JULIAN et al teaches the information associated with the available processing resources is information associated with available computer network resources ([0144] the number of models maintained is exemplary, and any number of models may be maintained according to resource availability. [0005] artificial neural network, which may comprise an interconnected group of artificial neurons, is a computational device or represents a method to be performed by a computational device. Artificial neural networks may have corresponding structure and/or function in biological neural network).
With respect to claim 7, JULIAN et al teaches the information associated with the available processing resources is information associated with a number of artificial neurons evaluations per a time unit, and the selection of the at least one inference model is based on a number of artificial neurons in the at least one inference model ([0005] An artificial neural network, which may comprise an interconnected group of artificial neurons (i.e., neuron models), is a computational device or represents a method to be performed by a computational device. Artificial neural networks may have corresponding structure and/or function in biological neural network).
With respect to claim 8, JULIAN et al teaches the information associated with the available processing resources is information associated with a distribution of available processing resources, and the selection of the at least one inference model is based on a ratio of cases the at least one inference model can be evaluated within a selected time duration according to the distribution ([0021] neural network where a memory may be interfaced with individual distributed. [0144] the number of models maintained is exemplary, and any number of models may be maintained according to resource availability).
With respect to claim 9, JULIAN et al teaches training a machine learning algorithm using training examples to obtain at least part of the selected at least one inference model ([0042] The neural system 100 may be utilized in a large range of applications, such as machine learning).
With respect to claim 10, JULIAN et al teaches applying input data to the selected at least one inference model to obtain at least one inferred value (FIG. 1, an input current of the level 102. [0090] the processing unit 704 to receive data from a server. [0011] to receive data from a server based on inference model to compute an inference model. to compute one or more model parameter updates based on the inference).
With respect to claim 11, JULIAN et al teaches one inference model is based on a number of artificial neurons in the at least one inference model ([0005] An artificial neural network, which may comprise an interconnected group of artificial neurons is a computational device or represents a method to be performed by a computational device. Artificial neural networks may have corresponding structure and/or function in biological neural network).
With respect to claim 12, JULIAN et al teaches inference model can be evaluated within a selected time duration according to the distribution ([0130] FIG. 10, the devices provide input data 1002, input data computed based on the input data 1002 to provide distributed learned features 1004 as feature layers of the DNN feature model learning).
With respect to claim 13, JULIAN et al teaches generating the selected at least one inference model ([0011] inference model to compute an inference based on the model to compute one or more model parameter updates based on the inference).
With respect to claim 14, JULIAN et al teaches selecting at least part of the selected at least one inference model of a plurality of alternative inference models ([0011] inference model to compute an inference based on the model to compute one or more model parameter updates based on the inference).
With respect to claim 15, JULIAN et al teaches selecting training examples based on the information associated with the available processing resources, and training a machine learning algorithm using the training examples to obtain at least part of the selected at least one inference model ([0047] machine learning, [0097] model for distributing a common feature model to users, training on top of the common feature model).
With respect to claim 16, JULIAN et al teaches applying input data to the selected at least one inference model to obtain at least one inferred value, and image data captured by the at least one device ([0042] The neural system 100 may be utilized in a large range of applications, such as image and pattern recognition, machine learning).
With respect to claim 17, JULIAN et al teaches transmitting information associated with the selected at least one inference model to the at least one device ([0007] learning a model to receive model updates from one or more users to transmit data related to a subset of the updated model to user(s) based on the updated model).
With respect to claim 19, JULIAN et al teaches receiving an update to the information associated with the available processing resources; update the selected at least one inference model based on the update to the information associated with the available processing resources to obtain at least one updated inference model ([0006] receiving model updates from one or more users. updated model based on a previous model and the model updates. transmitting data related to a subset of the updated model to user(s) based on the updated model. [0082] shared inference model, generating a model including one or more model parameters based on the received data, computing an inference model, computing one or more model parameter updates based on the inference, and/or transmitting data based on the model parameter update(s) to the server); determining whether the update to the information associated with the available processing resources is below a selected threshold; when the update to the information associated with the available processing resources is above a selected threshold, causing the at least one device to utilize the at least one updated inference model; and when the update to the information associated with the available processing resources is below the selected threshold, withholding causing the at least one device to utilize the at least one updated inference model ([0006] receiving model updates from one or more users. updated model based on a previous model and the model updates. transmitting data related to a subset of the updated model to user(s) based on the updated model. [0082] shared inference model, generating a model including one or more model parameters based on the received data, computing an inference model, computing one or more model parameter updates based on the inference, and/or transmitting data based on the model parameter update(s) to the server. [0124] inference model monitored to determine sparsity of the number of elements greater than a threshold. [0141] when model updates (e.g., W.sub.1 or W.sub.2) updates received after a threshold number of updates).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISAAC M WOO whose telephone number is (571)272-4043. The examiner can normally be reached 9:00 to 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ISAAC M WOO/Primary Examiner, Art Unit 2163