DETAILED ACTION
This action is in response to the application filed 29 September 2023.
Claims 1–20 are pending. Claims 1, 8, and 15 are independent.
Claims 1–20 are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claim Rejections—35 U.S.C. § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1–20 are rejected under 35 U.S.C. § 103 as being unpatentable over Kasaragod et al. (US 2019/0036716 A1) [hereinafter Kasaragod] in view of Bathen et al. (US 2022/0405574 A1) [hereinafter Bathen].
Regarding independent claim 1, Kasaragod teaches [a] system for configuring user-defined recognition patterns at an edge device, the system comprising: a pattern recognition integrated circuit in the edge device, the pattern recognition integrated circuit implementing a machine learning pattern recognizer that generates an event recognition output in response to an input thereto […]; An edge device comprising a processor [integrated circuit] and a memory storing a local model (Kasaragod, ¶¶ 107, 153). The model may be a machine learning or AI model (Kasaragod, ¶ 30). The model generates predictions based on data from sensors, e.g., based on environmental conditions or events (Kasaragod, ¶¶ 32–34). The models on the devices, including the edge devices, are trained (Kasaragod, ¶¶ 105, 125, 135). The models include a number of parameters [weights] (Kasaragod, ¶¶ 3, 77). a remote pattern recognition training service in communication with a secondary user device receptive to a training input of the user-defined recognition patterns, […]; and The system may include a hub device [secondary user device] in communication with a provider network [remote pattern recognition training service] (Kasaragod, ¶ 35). The provider network may receive data from edge devices and generate updates to the local models based on the new data (Kasaragod, ¶ 28). an application interface connecting the pattern recognition integrated circuit to the secondary user device, […]. A network interface [application interface] communicatively couples the hub device to the edge devices via a local network (Kasaragod, ¶ 55). The edge devices have a memory for storing the local model (Kasaragod, ¶¶ 54, 55, 107, 108).
Kasaragod teaches models having parameters but does not expressly teach that the parameters are weights. However, Bathen teaches: based upon pre-trained machine learning weights stored in a memory of the pattern recognition integrated circuit A machine learning model training system for edge devices (Bathen, ¶¶ 25–27). The models are artificial neural networks comprising neurons and weights (Bathen, ¶ 82). The weights are stored as weight values in computer memory or are implemented as resistive processing units (Bathen, ¶ 86). the remote pattern recognition training service returning a set of training weights corresponding to the training input Training the models comprises updating the weights (Bathen, ¶ 83). the set of training weights returned to the secondary user device from the remote pattern recognition training service being transferable to the machine learning pattern recognizer for storage in the memory of the pattern recognition integrated circuit through the application interface The trained model may be transmitted to the edge device using, e.g., a network (Bathen, ¶ 79).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Kasaragod with those of Bathen. Doing so would have been a matter of applying a known technique [updating a model of an edge device by adjusting weights] to a known device ready for improvement [the model training system of Kasaragod] to yield predictable results [a model training system wherein weights of the model are updated via an intermediate device].
Regarding dependent claim 2, the rejection of claim 1 is incorporated and Kasaragod/Bathen further teaches: wherein the machine learning pattern recognizer is selected from a group consisting of: a multilayer perceptron (MCP), a convolutional neural network (CNN), and a recurrent neural network (RNN). The model may be a convolutional neural network or a recurrent neural network (Bathen, ¶¶ 87, 91).
Regarding dependent claim 3, the rejection of claim 1 is incorporated and Kasaragod/Bathen further teaches: wherein the training input is accompanied by an input type definition1. The training dataset may include a data type (Bathen, ¶ 34). A user may provide annotations for data used for training (Bathen, ¶ 56).
Regarding dependent claim 4, the rejection of claim 3 is incorporated and Kasaragod/Bathen further teaches: wherein the input type definition is selected through an application executing on the secondary user device and capturing the training input. The user can provide annotations for training data, e.g., by identifying objects within the data (Bathen, ¶ 56). The user may access the system through, e.g., a cloud portal (Bathen, ¶ 115).
Regarding dependent claim 5, the rejection of claim 1 is incorporated and Kasaragod/Bathen further teaches: wherein the machine learning pattern recognizer generates the event recognition output based upon an identification of an arbitrary user input to the edge device as matching a recognition pattern correlated to the set of training weights transferred from the secondary user device. The prediction may include, e.g., an identification based on data from the sensor, such as identification of a person2, animal, or object, based on use of an image classifier (Kasaragod, ¶¶ 33–34).
Regarding dependent claim 6, the rejection of claim 1 is incorporated and Kasaragod/Bathen further teaches: wherein the input is audio. The data collected from the sensor may be audio data from a microphone (Kasaragod, ¶ 169). The data type of the model may be audio (Bathen, ¶¶ 28, 40, 45, 53).
Regarding dependent claim 7, the rejection of claim 1 is incorporated and Kasaragod/Bathen further teaches: wherein the input is one or more images. The data collected from the sensor may be image data (Kasaragod, ¶ 102, 122, 169). The data type of the model may be images or video (Bathen, ¶¶ 27, 28, 40, 44, 45).
Regarding independent claim 8, this claim recites limitations similar to those of claim 1, and therefore is rejected for the same reasons.
Regarding dependent claim 9, this claim recites limitations similar to those of claim 2, and therefore is rejected for the same reasons.
Regarding dependent claim 10, this claim recites limitations similar to those of claim 3, and therefore is rejected for the same reasons.
Regarding dependent claim 11, this claim recites limitations similar to those of claim 4, and therefore is rejected for the same reasons.
Regarding dependent claim 12, this claim recites limitations similar to those of claim 5, and therefore is rejected for the same reasons.
Regarding dependent claim 13, this claim recites limitations similar to those of claim 6, and therefore is rejected for the same reasons.
Regarding dependent claim 14, this claim recites limitations similar to those of claim 7, and therefore is rejected for the same reasons.
Regarding independent claim 15, this claim recites limitations similar to those of claim 1, and therefore is rejected for the same reasons.
Regarding dependent claim 16, this claim recites limitations similar to those of claim 2, and therefore is rejected for the same reasons.
Regarding dependent claim 17, this claim recites limitations similar to those of claim 3, and therefore is rejected for the same reasons.
Regarding dependent claim 18, this claim recites limitations similar to those of claim 4, and therefore is rejected for the same reasons.
Regarding dependent claim 19, this claim recites limitations similar to those of claim 6, and therefore is rejected for the same reasons.
Regarding dependent claim 20, this claim recites limitations similar to those of claim 7, and therefore is rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
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/Tyler Schallhorn/Examiner, Art Unit 2144
/SHOURJO DASGUPTA/Primary Examiner, Art Unit 2144
1 It is unclear whether “input type” means, e.g., a label describing the input data, such as the “dog barking” or “baby crying” in Applicant’s paras. 48 and 49, or the type of data, such as “audio” or “video” in claims 6 and 7, therefore citations are provided for both interpretations.
2 Compare to Applicant’s paragraph 53: identification of a person in video data.
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