DETAILED ACTION
This action is written in response to the application filed 1/18/23. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 6 and 18 are each missing a period. Appropriate correction is required. See MPEP 608.01(m).
Claim Rejections - 35 USC § 101
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. 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.
In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines.1
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—claim 1 recites a method, which is a process.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the claim recites one or more limitations which—under their broadest reasonable interpretation—covers performance of the limitation in the mind (see table below).
Claim limitation
Examiner analysis
generating, by the Al model and based on the sensor data, a prediction defining a utilization value of the physical dining environment.
This is a mental process akin to a human evaluation/judgment. Many AI/ML models (eg linear regression or logistic regression) can be practically trained and implemented as a mental process, typically with the aid of pencil and paper.
Because the claim recites limitations which can practically be implemented as mental processes, the claim recites a mental process.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the claim does not recited even generic computer hardware.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—the additional limitations are addressed below:
collecting, by one or more processors, sensor data from one or more sensors positioned within the physical dining environment, wherein the sensor data corresponds to one or more locations within the physical dining environment;
This is insignificant pre-solution activity: gathering data to be processed in subsequent steps.
inputting, into an Al model executing on the one or more processors, the sensor data, wherein the Al model is trained with sensor data captured by the one or more sensors positioned within the physical dining environment; and
This is insignificant pre-solution activity: gathering data to be processed in subsequent steps.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 13 and 20, which recite a system and a computer-readable medium, respectively, as well as to dependent claims 2-12 and 14-19. Regarding independent claims 13 and 20, the only limitation on the performance of the described method is that it must be performed using generic computing components (eg a processor and a memory). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. The statement that the method is performed by computer does not satisfy the test of “inventive concept.” See Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 134 S. Ct. 2347, 2360 (2014).
The additional limitations of the dependent claims are addressed briefly below. Taken alone, the additional elements of the dependent claims above do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim limitation
Examiner analysis
2 and 14. The IoT sensor based method of claim 1, wherein the Al model is further trained with timing data, wherein generating the prediction further comprises inputting a time value for the prediction, and wherein the prediction defines the utilization value for the physical dining environment at the time value.
This is merely additional information about one or more previously identified mental processes.
3 and 15. The IoT sensor based method of claim 1, wherein the sensor data corresponds to a portion of the physical dining environment, and wherein the prediction defining the utilization of the physical dining environment is an extrapolated prediction based on the portion of the physical dining environment.
This is merely additional information about one or more previously identified mental processes.
4 and 16. The IoT sensor based method of claim 1, wherein the Al model is further trained with one or more of:
weather data, event data, traffic data, a number of tables or seats within the physical dining environment, non-sensor based occupancy data defining occupancy within the physical dining environment, one or more meal duration times, customer-specific data, a type of table or seat within the physical dining environment, and/or historical transactions made by customers of the physical dining environment.
This is merely additional information about one or more previously identified mental processes. (Specifying input data.)
5 and 17. The IoT sensor based method of claim 1, wherein the Al model is further trained with infrastructure related data of the physical dining environment.
This is merely additional information about one or more previously identified mental processes.
6 and 18. The IoT sensor based method of claim 1, wherein the one or more sensors comprise one or more of:
one or more pressure sensors, one or more imaging sensors, one or more heat sensors, and/or one or more signal sensors.
This is insignificant pre-solution activity: gathering data to be processed in subsequent steps.
Regarding claims 7 and 19. The IoT sensor based method of claim 1, wherein the one or more sensors comprising an existing camera configured to capture images of users within the physical dining environment.
This is insignificant pre-solution activity: gathering data to be processed in subsequent steps.
8. The IoT sensor based method of claim 1, wherein the one or more locations areas of the physical dining environment comprise one or more of:
a seat positioned within the physical dining environment, a table positioned within the physical dining environment, or a bar area positioned within the physical dining environment.
This is merely additional information about one or more previously identified mental processes.
9. The IoT sensor based method of claim 1, wherein the prediction corresponds to a specific location within the physical dining environment.
This is merely additional information about one or more previously identified mental processes.
10. The IoT sensor based method of claim 1 further comprising determining a one or more outputs based on the prediction, the one or more outputs comprising of at least one of:
a service provided by an operator of the physical dining environment, a value of a food item provided by the operator of the physical dining environment, a value of a reservation provided by an operator of the physical dining environment, and/or a dynamic menu offered by the operator of the physical dining environment.
This is merely additional information about one or more previously identified mental processes.
11. The IoT sensor based method of claim 10, wherein the one or more outputs comprises a ranged value.
This is merely additional information about one or more previously identified mental processes.
12. The IoT sensor based method of claim 1, wherein the utilization value is generated in real time or near-real time and/or wherein an indication of the utilization value is displayed on a graphic user interface (GUI) on periodic basis.
This is insignificant post-solution activity: displaying results from a preceding step.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The following are the references relied upon in the rejections below:
Chitu (Chitu, Claudia, Grigore Stamatescu, and Alberto Cerpa. "Building occupancy estimation using supervised learning techniques." 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC). IEEE, 2019.)
Ryan (US 2020/0228759 A1)
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chitu and Ryan.
Regarding claims 1, 13 and 20, Chitu discloses an internet of things (IoT) sensor based method of improving utilization of a physical [room] environment using artificial intelligence (AI), the IoT sensor based method comprising:
collecting, by one or more processors, sensor data from one or more sensors positioned within the physical [room] environment, wherein the sensor data corresponds to one or more locations within the physical [room] environment;
P. 167, abstract, “we believe it is important to estimate occupancy using existing sensors currently deployed in buildings.”
inputting, into an Al model executing on the one or more processors, the sensor data, wherein the Al model is trained with sensor data captured by the one or more sensors positioned within the physical [room] environment; and
P. 168, second col., “In this section, we explain the algorithms and techniques used to solve the occupancy estimation problem. In our work, we use RF [random forest] and K-NN [k-nearest neighbor] classification algorithms to address the estimation of the occupancy levels of the rooms/zones based on the several inputs that are usually available in commercial buildings. We use as inputs the value of the CO2 concentration, as measured by sensors, and ventilation airflow, as measured by the position of the airflow damper, in each room/zone. The output of our classifiers is the total number of occupants in each room/zone, which is an integer number.”
P. 170, second col. “We implemented the algorithms in Python using Pandas and Scikit-learn libraries. The full dataset that we processed is about 1MB, and the Pandas software framework is suitable for our case, since it is very efficient with time series data.”
generating, by the Al model and based on the sensor data, a prediction defining a utilization value of the physical [room] environment.
P. 168, second col. “The output of our classifiers is the total number of occupants in each room/zone, which is an integer number.”
Ryan discloses the following further limitation which Chitu does not disclose wherein the room is a dining area.
[0011] “Generally, the method S100 can be executed within a work area—such as within a conference room, an agile work environment, a cafeteria, or a lounge, etc. within a facility—to monitor changes in human occupancy in the work area, to update schedulers or managers for assets and spaces within the work area, and to control various actuators throughout the work area based on these changes in human occupancy.” (Emphasis added.)
At the time of filing, it would have been obvious to a person of ordinary skill to apply the Chitu technique for modeling and estimating room occupancy to cafeterias, ie a ‘physical dining environment’ (as taught by Ryan) because this would provide actionable information to building facilities staff that may affect cleaning, meal preparation, or room scheduling operations. Both disclosures pertain to AI-assisted (smart) building operations.
Regarding independent claim 13, its further limitations comprising generic computer hardware (ie “one or more processors communicatively coupled to the one or more sensors” and “one or more memories accessible by the one or more processors” are inherent throughout the Chitu and Ryan disclosures.
Regarding independent claim 20, its further limitations comprising generic computer hardware (ie “a tangible, non-transitory computer-readable medium storing instructions”) is inherent throughout the Chitu and Ryan disclosures.
Regarding claims 2 and 14, Chitu discloses the further limitation wherein the Al model is further trained with timing data, wherein generating the prediction further comprises inputting a time value for the prediction, and wherein the prediction defines the utilization value for the physical […] environment at the time value.
P. 168, second col., “From our data set, we use data from 4 rooms from A university building with approximately 1000 occupants on normal workdays, data collected in the spring of 2017 for 15 days, with a temporal resolution of one minute. In total, the data set has 21,600 readings per room for each sensor, which means 259,200 records of 3 sensor types”.
‘utilization value’ :: P. 168, “The output of our classifiers is the total number of occupants in each room/zone, which is an integer number.”
Regarding claims 3 and 15, Ryan discloses the following further limitation wherein the sensor data corresponds to a portion of the physical dining environment, and wherein the prediction defining the utilization of the physical dining environment is an extrapolated prediction based on the portion of the physical dining environment.
Fig. 1. The Examiner notes that the system can detect an “Occupant present” in a particular seat.
Also [0023] “In one example, the system can detect or identify “proximity” of a human or human effect to a desk within a work environment in response to the human or the human effect being located within three feet of the outer edge of the desk and desk chair associated with the desk. In another example, the system can detect or identify “proximity” of a human or a human effect in response to the human or the human effect being located within a predefined area delimited by a five-foot by eight-foot region overlapping the front of the desk. In yet another example, the system can detect or identify “proximity” of a human or human effect to a desk within a work environment in response to the human or the human effect being located within a statistically relevant area around the desk as indicated by machine learning models utilized to detect occupancy at the desk. In an additional example, the system can detect or identify “proximity” between two objects, both located on the surface of a desk in response to a first object being located within a ten-centimeter radius of a second object. “ (Emphasis added.)
Regarding claims 4 and 16, Ryan discloses the following further limitation wherein the Al model is further trained with one or more of:
weather data, event data, traffic data, a number of tables or seats within the physical dining environment, non-sensor based occupancy data defining occupancy within the physical dining environment, one or more meal duration times, customer-specific data, a type of table or seat within the physical dining environment, and/or historical transactions made by customers of the physical dining environment.
‘event data’ :: [0047] “In a similar variation, once in the active state, the sensor block can execute a sequence of scan cycles, as described above. If, during a scan cycle in the sequence, the sensor block records an image in Block S120 and then detects absence of humans in the second image in Block S130, the sensor block can query a local log of motion events detected by the motion sensor.” (Emphasis added.)
‘traffic data’ :: [0042] “For example, the sensor block can determine whether the human is working independently, eating, conversing, conferencing, walking, standing, sitting, etc. based on the human's posture, proximity of human effects and assets to the human, proximity of other humans to the human, and/or other features extracted from one image or from a series of images recorded by the sensor block during one scan cycle.” The Examiner notes that this is an example of foot traffic.
Regarding claims 5 and 17, Chitu discloses the further limitation wherein the Al model is further trained with infrastructure related data of the physical dining environment.
P. 168, second col., “We use as inputs the value of the CO2 concentration, as measured by sensors, and ventilation airflow, as measured by the position of the airflow damper, in each room/zone.”
Regarding claims 6 and 18, Chitu discloses the further limitation wherein the one or more sensors comprise one or more of:
one or more pressure sensors, one or more imaging sensors, one or more heat sensors, and/or one or more signal sensors
P. 169, first col., “The occupant counts are obtained from processed data captured with PC2 3D stereo vision cameras”.
Regarding claims 7 and 19, Chitu discloses the further limitation wherein the one or more sensors comprising an existing camera configured to capture images of users within the physical dining environment.
P. 169, first col., “The occupant counts are obtained from processed data captured with PC2 3D stereo vision cameras”.
Regarding claim 8, Ryan discloses the further limitation wherein the one or more locations areas of the physical dining environment comprise one or more of:
a seat positioned within the physical […] environment, a table positioned within the physical […] environment, or a bar area positioned within the physical […] environment.
[0023] “In one example, the system can detect or identify “proximity” of a human or human effect to a desk within a work environment in response to the human or the human effect being located within three feet of the outer edge of the desk and desk chair associated with the desk.” (Emphasis added.)
Regarding claim 9, Ryan discloses the further limitation wherein the prediction corresponds to a specific location within the physical […] environment.
[0023] “In one example, the system can detect or identify “proximity” of a human or human effect to a desk within a work environment in response to the human or the human effect being located within three feet of the outer edge of the desk and desk chair associated with the desk. In another example, the system can detect or identify “proximity” of a human or a human effect in response to the human or the human effect being located within a predefined area delimited by a five-foot by eight-foot region overlapping the front of the desk. In yet another example, the system can detect or identify “proximity” of a human or human effect to a desk within a work environment in response to the human or the human effect being located within a statistically relevant area around the desk as indicated by machine learning models utilized to detect occupancy at the desk. In an additional example, the system can detect or identify “proximity” between two objects, both located on the surface of a desk in response to a first object being located within a ten-centimeter radius of a second object. “ (Emphasis added.)
Regarding claim 10, Chitu discloses the further limitation comprising determining a one or more outputs based on the prediction, the one or more outputs comprising of at least one of:
a service provided by an operator of the physical […] environment, a value of a food item provided by the operator of the physical dining environment, a value of a reservation provided by an operator of the physical dining environment, and/or a dynamic menu offered by the operator of the physical dining environment.
P. 168, first col., “Applying schedules based on estimated occupancy show promising energy savings [12] illustrating ways of implementing them such that temperature and airflow setpoints are set to vary on a larger range. This is explained by the fact that persons induce loads with their dissipated body heat and appliances used, which trigger the HVAC system to send proper commands towards the variable air volume (VAV) units that deliver the cooling/heating energy to the thermal zones.”
Regarding claim 11, Chitu discloses the further limitation wherein the one or more outputs comprises a ranged value.
P. 171, second col. “We believe that energy savings can be achieved in part by not ventilating the room for maximum capacity, but balancing the k-NN model occupancy output with a safety guard band that is proportionally to the worst case RMSE.”
Regarding claim 12, Chitu discloses the further limitation wherein the utilization value is generated in real time or near-real time and/or wherein an indication of the utilization value is displayed on a graphic user interface (GUI) on periodic basis.
P. 167, second col. “We evaluate two techniques to estimate the real-time and predictive occupancy based on existing data sources in the building”.
Additional Relevant Prior Art
The following references were identified by the Examiner as being relevant to the disclosed invention, but are not relied upon in any particular prior art rejection:
Anand discloses a machine learning system for predictive control of air control, ventilation, lighting and other equipment in institutional building based on anticipated occupancy. (Anand, Prashant, et al. "Occupancy-based energy consumption modelling using machine learning algorithms for institutional buildings." Energy and Buildings 252 (2021): 111478.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Vincent Gonzales whose telephone number is (571) 270-3837. The examiner can normally be reached on Monday-Friday 7 a.m. to 4 p.m. MT. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Miranda Huang, can be reached at (571) 270-7092.
Information regarding the status of an application may be obtained from the USPTO 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.
/Vincent Gonzales/Primary Examiner, Art Unit 2124
1 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.