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 .
Claims 1-20 have been examined. Claims 1-8 and 20 are rejected and claims 9-19 stand allowed.
Information Disclosure Statement
The information disclosure statement (IDS) submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement filed on February 19th, 2025, is being considered by the examiner.
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.
Claim 4 is 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. Specifically, the claim references a “proper” subset of sensor data. The term “proper” is unclear, as implies a distinction from an “improper” subset that is not defined. The specification does not provide guidance as it only defines the term using the same language as the claims (pg. 3 & 4). For the purpose of this office action, it is interpreted to only be a “subset” of the sensor data. If the term is intended to provide additional limiting features, it should be further defined in the claim or an alternative term used in its place.
Allowable Subject Matter
Claims 9-19 are allowed.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8 and 20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Kong et al. (U.S. PGPub 2021/0174095).
As per claims 1 and 20, Kong teaches a method comprising:
determining, using first sensor data obtained by a sensor at a property, (Kong, see fig. 1 and paragraphs 0039-0042 including a variety of video, occupancy, IR/motion, and other sensors; The sensors communicate with each other and/or servers, paragraphs 0047, 0050, and fig. 2A architecture where the sensors may operate in a mesh structure where the server processing takes place on the devices, paragraph 0059, 0061)
that a candidate target object satisfies a similarity threshold for each of two or more predetermined objects; (Kong, see fig. 3A, 3B, e.g., database 3169 where there are a plurality of candidate target objects that can be determined by the confidence level/similarity threshold; For example, candidate target object person M is detected via a wide variety of sensor data in paragraph 0133 and data 3184-M. Person M can be matched using a subset of the data to one-to-many correlation with predetermined object persons by reaching a similarity threshold per paragraph 0135; see also recognizing module 3158 of fig. 5)
in response to determining, using the first sensor data obtained by the sensor at the property, that the candidate target object satisfies the similarity threshold for each of the two or more predetermined objects, accessing second sensor data of the candidate target object; (Kong, see paragraphs 0133-1039, in determining that the target is a person m that could match existing multiple score, a confidence analysis can re-evaluate the target with the second sensor data of multiple sensors to reach confirmation of the target; see also recognizing module 3158 of fig. 5)
determining, using at least the second sensor data of the candidate target object, that the candidate target object is a target object from the two or more predetermined objects; (Kong, see paragraphs 0133-1039, 017-0172, reaching a final confirmation of the target object)
in response to determining that the candidate target object is the target object, selecting, from a plurality of automated actions each for a corresponding one of the two or more predetermined objects, an automated action; and performing the automated action for the target object (Kong, paragraphs 0173-0175, 0191, fig. 5 module 3154, a variety of automated actions are selected based on the target determination, which differ based on which one or more predetermined objects were relevant, then the action is performed, such as greeting or interacting with a visitor, triggering other sensors/responses, etc.).
As per claim 2, Kong teaches the method of claim 1, comprising:
detecting an action performed by the candidate target object, wherein determining, using the first sensor data, that the candidate target object satisfies the similarity threshold for each of two or more predetermined objects is responsive to detecting the action performed by the candidate target object (Kong, see wide variety of characterization data 3184-m, as a non-limiting example paragraphs 0214-0215 a visitor behavior action and context determining the specific class and information on the target visitor).
As per claim 3, Kong teaches the method of claim 1, wherein:
the first sensor data has a first resolution; (Kong, see fig. 1 and paragraphs 0039-0042 including a variety of video, occupancy, IR/motion, and other sensors; The sensors communicate with each other and/or servers, paragraphs 0047, 0050, and fig. 2A architecture where the sensors may operate in a mesh structure where the server processing takes place on the devices, paragraph 0059, 0061, 0193)
accessing the second sensor data of the candidate target object comprises accessing the second sensor data that has a second resolution that is different than the first resolution of the first sensor data; and (Kong, see paragraph 0068, where for example video devices can have “multiple streams, of respective resolutions and/or frame rates” including primary resolution and secondary streams. Each device can have multiple resolution levels while also having multiple video devices in the network with multiple respective streams. The stream may be processed locally, sent in entirety, stored locally, and/or otherwise cropped or modified (paragraphs 0069, 0070)
determining that the candidate target object is the target object from the two or more predetermined objects uses at least the second sensor data that has the second resolution that is different than the first resolution of the first sensor data (Kong, see paragraphs 0133-1039, 017-0172, reaching a final confirmation of the target object, where multiple sensor resolution streams, crops, and formats are all factored in).
As per claim 4, Kong teaches the method of claim 1, wherein:
the first sensor data comprises the second sensor data and third sensor data;
accessing the second sensor data of the candidate target object comprises: selecting, as the second sensor data, a proper subset of the first sensor data; and determining to skip selecting the third sensor data; and determining that the candidate target object is the target object from the two or more predetermined objects uses the second sensor data that is the proper subset of the first sensor data and does not use the third sensor data (Kong, see paragraphs 0068-0070, where a first sensor data (video source) can include 1-n streams (2nd, 3rd, etc.); a subset of the first data sensor, via a cropped image, is determined to be used to select the target object from the predetermined objects, via paragraph 0222, and the complete first sensor, third sensor data, and other data is in term skipped and not used).
As per claim 5, Kong teaches the method of claim 1, wherein:
determining that the candidate target object satisfies the similarity threshold for each of the two or more predetermined objects uses a first detection model; and determining that the candidate target object is the target object from the two or more predetermined objects uses a second, different detection model that is a different model from the first detection model (Kong, see, e.g., the process of paragraphs 0232-0238 observation window example. The first model can detect a visitor by a visual detection model to classify to a selection of predetermined object visitors, for example facial recognition. Then a context model, that measures different types of objects than facial recognition, is used to finish the determination of the target object).
As per claim 6, Kong teaches the method of claim 5, wherein
the first detection model is trained to differentiate different types of objects than the second, different detection model (Kong, see, e.g., the process of paragraphs 0232-0238 observation window example. The first model can detect a visitor by a visual detection model to classify to a selection of predetermined object visitors, for example facial recognition. Then a context model, that measures different types of objects than facial recognition, is used to finish the determination of the target object).
As per claim 7, Kong teaches the method of claim 1, wherein performing the automated action for the target object comprises:
determining that a behavior of the target object satisfies a similarity criterion for a predetermined behavior for the target object; and in response to determining that the behavior of the target object satisfies a similarity criterion for the predetermined behavior, causing performance of a training action corresponding to the predetermined behavior on the target object (Kong, see, e.g., paragraphs 0287-0291 where after behavior of the target object is determined to be similar, a training action in the form of a response/interaction with the target is selected and performed on the target).
As per claim 8, Kong teaches the method of claim 1, wherein determining that the candidate target object is the target object from the two or more predetermined objects comprises:
determining, using at least the second sensor data of the candidate target object, whether the candidate target object is the target object from the two or more predetermined objects; and determining that the candidate target object is the target object from the two or more predetermined objects (Kong, see paragraphs 0133-1039, 017-0172, reaching a final confirmation of the target object).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. PGPub 2023/0225290, which describes a method of behavioral analysis using automated sensors and behavior detection to perform automated actions in response;
U.S. Patent 11,783,689, which describes a method of monitoring multiple sensor data sources to issue relevant commands after analysis; and
U.S. PGPub 12,567,316, which describes a method of video analysis to determine predetermined object identity and automating action responses.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nicholas Taylor whose telephone number is (571) 272-3889. The examiner can normally be reached Mon-Fri (9:00am - 5:00pm).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NICHOLAS R TAYLOR/Supervisory Patent Examiner, Art Unit 2443
Prosecution Insights