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
The action is in response to claims dated 3/4/2024
Claims pending in the case: 1-22
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.
Claim(s) 11 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph insufficient antecedent basis. Claim 11 recites the limitation " wherein the one or more behaviors of the entity". There is insufficient antecedent basis for this limitation in the claim.
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) 1-7, 9-19, 22 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step1: determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If YES, proceed to Step 2A, broken into two prongs.
Step 2A, Prong 1: determine whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If YES, the analysis proceeds to the second prong
Step 2A, Prong 2: determine whether or not the claims integrate the judicial exception into a practical application. If NOT, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B).
Step 2B: If any element or combination of elements in the claim is sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Step 1 Analysis
According to the first part of the analysis, the instant case all claims are directed to one of the statutory categories of invention.
Step 2A Prong 1, Step 2A Prong 2, and Step 2B Analysis
Independent Claim X includes the following recitation of an abstract idea:
determine, …, and based on the first sensor data of the object, a first correlation that the object corresponds to an entity category of a set of entity categories (Determining an object for example an unknown person in an image is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.);
determine, using a second machine-learning model, and based on the second sensor data of the object, a second correlation that the object corresponds to the entity category (Determining an object for example an unfamiliar voice in a video is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.);
designate, based at least in part on the first correlation and the second correlation, the object is an entity of the entity category (Designating a category to an object for example it’s a person, is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.);
determine, based on the sensor data, one or more criteria of the entity (Determining based on criteria, for example unknown person is a burglar based on appearance is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.); and
execute one or more actions based on the entity category and the one or more criteria (Executing action, for example, during a burglary, is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.).
Claim 1 recites the following additional elements, which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
one or more sensor devices to capture sensor data in an environment; and one or more processors configured to (This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).):
collect, using a first sensor device of the one or more sensor devices, first sensor data of an object in the environment, wherein the first sensor data of the object comprises image data (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
collect, using a second sensor device of the one or more sensor devices, second sensor data of the object (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
determine, using a first machine-learning model, determine, using a second machine-learning model (This high-level recitation of the machine learning is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).),
These claimed limitations therefore do not integrate the abstract idea into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons given above with respect to integration of the abstract idea into a practical application.
Therefore the claim is not patent eligible.
Independent Claims 13, are similar in scope as claim 1 and therefore rejected under the same rationale.
The dependent claims recite at least the abstract idea identified above in the claim upon which it depends and recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Dependent claim 2, 6-7 pertain to determining object properties (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.).
Dependent claim 3-5 pertain to a training models at a high level (This high-level recitation of the machine learning model is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Dependent claim 9-12 pertain to data type (This appears to be directed to the specification of data and a restriction to a particular type of data. This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
These dependent claims therefore, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea
Dependent Claims 14-19, 21-22, are similar in scope as claims 2-7, 9, 11 respectively and therefore rejected under the same rationale.
Hence these claims are rejected as being abstract.
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) 1-7, 9, 11-19, 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Correnti (US 20210158030) in view of Packer (US 11577722).
Regarding Claim 1, Correnti teaches, An apparatus comprising:
one or more sensor devices to capture sensor data in an environment (Correnti: [4, 7]: sensor data); and
one or more processors (Correnti: [13]: processor) configured to:
collect, using a first sensor device of the one or more sensor devices, first sensor data of an object in the environment, wherein the first sensor data of the object comprises image data (Correnti: [55, 56-57]: collect sensor data which may be video data, audio data, vibrational data, or any other data);
collect, using a second sensor device of the one or more sensor devices, second sensor data of the object (Correnti: Fig. 1, [55, 56-57]: collect sensor data from sensors in the different locations);
determine, using a first machine-learning model, and based on the first sensor data of the object, a first correlation that the object corresponds to an entity category of a set of entity categories (Correnti: [57]: determine human (category of living being) with a cane (category of object));
determine, using a second machine-learning model, and based on the second sensor data of the object, a second correlation that the object corresponds to the entity category (Correnti: [59]: determination based on a collection of sensors; [44]: other sensor data for confirmation);
designate, based at least in part on the first correlation and the second correlation, the object is an entity of the entity category (Correnti: [57-58]: determine human is blind based on human with a moving cane);
determine, based on the sensor data, one or more criteria of the entity (Correnti: [60-62]: determine impairment; [33]: criteria based on monitored information); and
execute one or more actions based on the entity category and the one or more criteria (Correnti: [60-62]: determine impairment; [33-35]: execute action such as alarm, notification etc.);
Although Correnti does not specifically mention a first and a second machine learning model, Correnti discloses all the claimed functions, Correnti in [50] teaches a plurality of models may be used by the control unit. Correnti also teaches that sensor data may be different data types such audio, video etc. It would be obvious to one skilled in the art that different models may be used to process different types of data. Thus the limitations as claimed are found to be obvious over the teachings in Correnti alone;
Nonetheless, Packer teaches, using a first machine-learning model and using a second machine-learning model (Packer: Fig. 4, col 3 lines 7-22: multiple models may be used to identify and predict object behavior);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Correnti and Packer because the combination would enable multiple models to analyze data collected from sensors. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would enable performing a number of operations to determine an effect of each detected object. In an environment with a large number of objects, the combination enables reducing computational cost (see Packer col 1 lines 12-19).
Regarding Claim 2, Correnti and Packer teach the limitations as claimed in claim 1 and, wherein the one or more processors are further configured to: determine, using a third machine-learning model, and based on the first and second sensor data, that the object corresponds to an entity profile (Correnti: [8, 64]: access profile of the identified person) (Packer: Fig. 4, col 3 lines 7-22: multiple models may be used to identify and predict object behavior). Using different models to execute different tasks in the system would have been obvious to one skilled in the art.
Regarding Claim 3, Correnti and Packer teach the limitations as claimed in claim 2 and, wherein the third machine-learning model is trained by applying the third machine learning model on historical data including image data of one or more persons associated with the entity profile (Correnti: [63, 74, 76]: model training samples of user instances (historical data)) (Packer: Fig. 4, col 3 lines 7-22: multiple models may be used to identify and predict object behavior; col 18 lines 29-30: trained using historical data). Using different models to execute different tasks in the system would have been obvious to one skilled in the art.
Regarding Claim 4, Correnti and Packer teach the limitations as claimed in claim 1 and, wherein the first machine-learning model is trained by applying the first machine-learning model on historical data including image data that corresponds to the entity category (Correnti: [31, 63, 74, 76]: [31]: “mage analysis based on the feed from camera 101 shows a white cane moving back and forth in front of user” – image data of object category of human and cane)(Packer: col 18 lines 29-30: trained using image data).
Regarding Claim 5, Correnti and Packer teach the limitations as claimed in claim 4 and, wherein the second machine-learning model is trained by applying the second machine-learning model on historical data including sensor data that is not image data and that corresponds to the entity category (Correnti: [56, 63, 74, 76]: [31]: monitoring system using sensor data which may be audio, vibration etc.) (Packer: Fig. 4, col 3 lines 7-22: multiple models may be used to identify and predict object behavior; col 31 line 56- col 32 line 25: model to analyze different features which may be audio data).
Regarding Claim 6, Correnti and Packer teach the limitations as claimed in claim 1 and, wherein the one or more processors are further configured to: determine, using the first machine-learning model, based on the first sensor data of the object, and for each entity category in the set of entity categories, a first correlation that the object corresponds to the entity category (Correnti: [62]: image data of human with cane);
determine, using a second machine-learning model, based on the second sensor data of the object, and for each entity category in the set of entity categories, a second correlation that the object corresponds to the entity category (Correnti: [31]: monitoring system using sensor data which may be audio, vibration etc.; Fig. 1, [55, 56-57]: collect sensor data from sensors in the different locations)) (Packer: Fig. 4, col 3 lines 7-22: multiple models may be used to identify and predict object behavior; col 31 line 56- col 32 line 25: model to analyze different features of object).
Regarding Claim 7, Correnti and Packer teach the limitations as claimed in claim 1 and, wherein designating the object to be the entity of the entity category is based at least in part on each of the first correlation, the second correlation category (Correnti: [20]: collect sensor data to designate object types, motion etc. based on correlation of data collected from multiple sensors) (Packer: Fig. 4, col 3 lines 7-22, col 31 line 56- col 32 line 25: based on correlation of data using multiple models with data collected from multiple sensors), and a matrix lookup table (Correnti: [37]: match data with known characteristics).
Regarding Claim 9, Correnti and Packer teach the limitations as claimed in claim 1 and, wherein the second sensor data of the object is data other than image data (Correnti: [31]: monitoring system using sensor data which may be audio, vibration etc.).
Regarding Claim 11, Correnti and Packer teach the limitations as claimed in claim 1 and, wherein the one or more behaviors of the entity include one or more of a velocity of the entity, a posture of the entity, and a distance between the entity and a region of interest within the environment (Correnti: [75]: distance)(Packer: col 3 lines 9-21, 48-58: velocity, pose, location etc.).
Regarding Claim 12, Correnti and Packer teach the limitations as claimed in claim 1 and, wherein the region of interest includes one or more of a package, a vehicle, a mailbox, a sensor, a door, and a window (Correnti: [39]: package delivery)(Packer: col 3 lines 9-21, 48-58: vehicle).
Regarding Claim 13, Correnti teaches, A method comprising: collecting, using a first sensor device of one or more sensor devices, first sensor data of an object in an environment, wherein the first sensor data of the object comprises image data (Correnti: [55, 56-57]: collect sensor data which may be video data, audio data, vibrational data, or any other data);
collecting, using a second sensor device of the one or more sensor devices, second sensor data of the object in the environment (Correnti: Fig. 1, [55, 56-57]: collect sensor data from sensors in the different locations);
determining, by a computer executing a first machine-learning model and based on the first sensor data of the object, a first correlation of the object to an entity category of a set of entity categories (Correnti: [57]: determine human with a cane);
determining, by a computer executing a second machine-learning model, and based on the second sensor data of the object, a second correlation of the object to the entity category (Correnti: [59]: determination based on a collection of sensors; [44]: other sensor data for confirmation);
designating, based at least in part on the first correlation and the second correlation, the object is an entity of the entity category (Correnti: [57-58]: determine human is blind based on human with a moving cane);
determining that the entity corresponds to one or more criteria (Correnti: [60-62]: determine impairment; [33]: criteria based on monitored information); and
in response to the one or more criteria that correspond to the entity, execute one or more actions (Correnti: [60-62]: determine impairment; [33-35]: execute action such as alarm, notification etc.);
Although Correnti does not specifically mention a first and a second machine learning model, Correnti discloses all the claimed functions, Correnti in [50] teaches a plurality of models may be used by the control unit. Correnti also teaches that sensor data may be different data types such audio, video etc. It would be obvious to one skilled in the art that different models may be used to process different types of data. Thus the limitations as claimed are found to be obvious over the teachings in Correnti alone;
Nonetheless, Packer teaches, using a first machine-learning model and using a second machine-learning model (Packer: Fig. 4, col 3 lines 7-22: multiple models may be used to identify and predict object behavior);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Correnti and Packer because the combination would enable multiple models to analyze data collected from sensors. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would enable performing a number of operations to determine an effect of each detected object. In an environment with a large number of objects, the combination enables reducing computational cost (see Packer col 1 lines 12-19).
Regarding Claim(s) 14-19, 21 this/these claim(s) is/are similar in scope as claim(s) 2-7, 9 respectively. Therefore, this/these claim(s) is/are rejected under the same rationale.
Regarding Claim 22, Correnti and Packer teach the limitations as claimed in claim 1 and, wherein the one or more criteria that correspond to the entity are one or more behaviors of the entity (Correnti: [75]: distance related to entity)(Packer: col 3 lines 9-21, 48-58: velocity, pose, location etc. relating to the entity).
Claim(s) 8, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Correnti (US 20210158030) and Packer (US 11577722) in view of Zhang (US 20240005633).
Regarding Claim 8, Correnti and Packer teach the limitations as claimed in claim 1 but not, wherein designating the object to be the entity of the entity category is based at least in part on a first accuracy value used to scale the first correlation and a second accuracy value used to scale the second correlation, and wherein the first accuracy value corresponds to an average accuracy of the first sensor device and the second accuracy value corresponds to an average accuracy of the second sensor device;
Liu teaches, wherein designating the object to be the entity of the entity category is based at least in part on an accuracy value used to scale the correlation, and wherein the accuracy value corresponds to an average accuracy of the first sensor device (Zhang: [31, 61]: an accuracy value based on an average value (from the sensor data) may be used for the Re-ID model for parameter correlation);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Correnti, Packer and Zhang because the combination would enable and accuracy value for optimum selection. One of ordinary skill in the art would have been motivated to combine the teachings because the combination will improve model output by helping mitigate issues when data set is too small and the data set quality is low (see Zhang [4]).
Regarding Claim(s) 20, this/these claim(s) is/are similar in scope as claim(s) 8. Therefore, this/these claim(s) is/are rejected under the same rationale.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Correnti (US 20210158030) and Packer (US 11577722) in view of Lin (US 20190303656).
Regarding Claim 10, Correnti and Packer teach the limitations as claimed in claim 1 and, wherein one or both of the first correlation and the second correlation is a probability (Correnti: [7]: output of machine learning model indicating user behavior (this is typically a probability)) (Packer: col 15 lines 1-16: model output probabilities);
Correnti and Packer do not specifically teach, correlation is a probability;
Lin further teaches, correlation is a probability (Lin: [6, 18-19]: model output is a probability of the correlation);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Correnti, Packer and Lin because the combination would enable using model probability in the determination process in a monitoring system. One of ordinary skill in the art would have been motivated to combine the teachings because the combination uses a machine learning model to determine a state probability as is the common practice in the art (see Lin [4,6]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in the attached 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANDRITA BRAHMACHARI whose telephone number is (571)272-9735. The examiner can normally be reached Monday to Friday, 11 am to 8 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571 272 4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mandrita Brahmachari/Primary Examiner, Art Unit 2144
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