DETAILED ACTION
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 .
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 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims describe a computer-readable medium containing instructions. However, the specification does not clearly define the nature of the “computer-readable medium"; therefore, the “computer-readable medium” can be interpreted to include non-statutory media such as a wireless signal or carrier wave. The Examiner recommends replacing “computer-readable medium” with “non-transitory computer-readable medium”.
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, 7, 11, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rather et al. (US 2002/0138000 A1) in view of Amini et al. (US 2019/0289263 A1).
Regarding claim 1, Rather discloses a system comprising: an image generation module; (Paragraph 0034, correlator for producing images) receive, by a computing system (Figure 1, imaging system), wireless data comprising phase data and amplitude data over a time period; (Paragraphs 0033-0034, amplitude and phase data of an ultrasound signal) generate, by an image generation module of a computing system, a first image using at least one of the phase data or the amplitude data of the wireless data, wherein the first image represents an object during the time period (Paragraph 0034, the correlator uses the amplitude and phase data to produce a visual image representation of an object). Rather does not clearly disclose a machine learning module; one or more processors; and a non-transitory computer readable medium comprising instructions that, when executed by the one or more processors, cause the system to perform operations determine, by a machine learning module implemented on the computing system, a first current behavior of the object based on the first image; and transmit, by the computing system, a message to a user device, the message indicating the first current behavior of the object.
Amini discloses instructions stored in memory that can be executed by a processor for image processing (Paragraph 0110) including applying machine learning behavioral analytics to learn the behavior of objects in images and identify certain behaviors of objects (Paragraph 0095) and based on the behavior, a notification related to the behavior can be generated and transmitted to a user device (Paragraphs 0096-0099). Amini’s implementation of providing stored instructions for execution by a processor for image processing including machine learning behavioral analytics to identify in images, certain behaviors of objects that generated and transmit notifications to a user device based on the identified behaviors would have been recognized by one of ordinary skill in the art to be applicable to the generated images of objects using amplitude and phase data of Rather, and the results would have bene predictable in executing stored instructions by a processor to implement the generation of images of objects using amplitude and phase data that can be used by machine learning behavioral analytics to identify certain behaviors of the objects and generating and transmitting notifications to a user device based on the identified behaviors. Therefore, the claimed subject matter would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 2, Rather in view of Amini discloses one or more satellite nodes, each configured to transmit and/or collect wireless data; (Rather, paragraph 0033, receiver array that receives ultrasound waves) and a central node, the central node configured to receive the wireless data from the one or more satellite nodes, and wherein the machine learning module is implemented on the central node (Amini, paragraph 0073, a base station can perform the analytics on received images).
Regarding claim 3, Rather discloses wherein each of the one or more satellite nodes is configured to generate image data based on respective wireless data received at the one or more satellite nodes (Paragraphs 0033-0034, producing the image of the object based on the received data).
Regarding claim 5, Rather discloses wherein the machine learning module comprises at least one of a K-Nearest Neighbor model, a clustering model, and a computer vision model (Paragraph 0095, machine learning analytics for video images).
Regarding claims 7 and 18, similar reasoning as discussed in claim 1 is applied.
Regarding claim 11, Amini discloses determining, by the computing system, that the first current behavior is an unwanted behavior; (Paragraph 0096, determining the threat) and transmitting, by the computing system, an emergency message to an emergency service (Paragraphs 0096-0097, generation and transmission of a notification of the threat).
Regarding claim 17, Rather in view of Amini discloses providing, by the computing system, one or more data sets comprising phase data and/or amplitude data corresponding to one or more particular behaviors to the machine learning module; (Rather, paragraph 0034, producing images of the object using amplitude and phase data that can be used to identify behavior, Amini, paragraph 0095) and causing, by the computing system, one or more machine learning models of the machine learning module to be retrained using the one or more data sets (Amini, paragraph 0095, the machine-learning behavioral analytics can learn from the images it receives).
Allowable Subject Matter
Claims 4, 6, 8-10, 12-16, 19, and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 4, the prior art does not clearly disclose the system of claim 1, further comprising: one or more satellite nodes, each configured to transmit and/or collect wireless data, and wherein each of the satellite nodes comprises a respective image generator and respective machine learning module.
Regarding claim 6, the prior art does not clearly disclose the system of claim 1, wherein the image generation module utilizes continuous wavelet transformation.
Regarding claim 8, the prior art does not clearly disclose the method of claim 7 wherein the first current behavior of the object is at least one of one of sitting, standing, walking, running, moving, laying down.
Regarding claims 9 and 19, similar reasoning as discussed in claim 6 is applied.
Regarding claim 10, the prior art does not clearly disclose the method of claim 7, further comprising: receiving, by the computing system and from the user device, a request for the first current behavior of the object; and determining, by the computing system, a particular device of the computing system within a given proximity of the object.
Regarding claim 12, the prior art does not clearly disclose the method of claim 11 further comprising: determining, by the computing system, a location of the object based at least in part on a node of the computing system.
Regarding claim 13, the prior art does not clearly disclose the method of claim 7, further comprising: receiving, by the computing system, additional wireless data comprising phase data or amplitude data over the time period; generating, by the image generation module of the computing system, a second image using at least one of the phase data or the amplitude data of the additional wireless data, wherein the second image represents the object during the time period; determining, by the machine learning module implemented on the computing system, a second current behavior of the object based on the second image; and comparing, by the computing system, the first current behavior and the second current behavior to generate a confidence metric.
Regarding claim 15, the prior art does not clearly disclose the method of claim 7, further comprising: generating, by the image generation module of the computing system, a second image using unused data of at least one of the phase data or the amplitude data of the wireless data, wherein the second image represents the object during the time period; determining, by the machine learning module implemented on the computing system, a second current behavior of the object based on the second image; and comparing, by the computing system, the first current behavior and the second current behavior to generate a confidence metric.
Regarding claim 20, similar reasoning as discussed in claim 13 is applied.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Braun et al. (US 2024/0331869) discloses analysis of scalogram images for making predictions.
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/PHI HOANG/Primary Examiner, Art Unit 2619
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