DETAILED ACTION
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
2. Claims 7, 11, and 13-14 are objected to because of the following informalities: a) In claim 7 lines 2-3, please change "using a plurality of presence sensor interactions, the method comprising," to --using a plurality of presence sensor interactions, the method comprising:--. b) In claim 11 lines 5-6, please change "the current fill status of the container and the calibrated fill status data model" to --the current fill status of the container and a calibrated fill status data model--. c) In claim 13 line 3, please change "the fill status signal comprises at least one the current fill status" to --the fill status signal comprises at least one of the current fill status--. d) In claim 14 lines 4-6, please change "inputting a plurality of data relating to the current fill status of the container and a plurality of presence sensor interactions in the remote processing unit" to --inputting a plurality of data relating to a current fill status of the container and a plurality of presence sensor interactions in a remote processing unit --. Appropriate correction is required.
Claim Rejections - 35 USC § 101
3. 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 14-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In view of the new 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register Vol. 84, No. 4, January 7, 2019), the Examiner has considered the claims and has determined that under step 1, claims 14-18 are to a process. Next under the new step 2A prong 1 analysis, the claims are considered to determine if they recite an abstract idea (judicial exception) under the following groupings: (a) mathematical concepts, (b) certain methods of organizing human activity, or (c) mental processes. Independent claim 14 contains at least the following bolded limitations that fall into the grouping of mathematical concepts and/or mental processes:
14. A method for creating a fill status data model for enabling predicting a predicted fill status of a container using the fill status data model, the method comprising:
inputting a plurality of data relating to the current fill status of the container and a plurality of presence sensor interactions in the remote processing unit;
correlating the current fill status with the plurality of presence sensor interactions using a machine learning algorithm; and
creating the fill status data model from the correlating of the current fill status.
It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula."(see MPEP 2106.04(a)(2) I.). Thus the limitations of "creating a fill status data model for enabling predicting a predicted fill status of a container using the fill status data model," "inputting a plurality of data relating to the current fill status of the container and a plurality of presence sensor interactions," "correlating the current fill status with the plurality of presence sensor interactions using a machine learning algorithm," and "creating a the fill status data model from the correlating of the current fill status" all are considered as words serving the same purpose as a formula. The claim describes in words the use of input data from the current fill status and presence sensor interactions that are correlated together using a machine learning algorithm (mathematical relationship) to generate an abstract data-based fill status model as output. The use of an existing machine learning algorithm is just the action of using a function which takes input parameters and returns output parameters. The correlating of the current fill status with the plurality of presence sensor interactions can involve a mental process when the relationship between the two is at basic correspondence level, or can involve mathematical calculations/formulas when the relationship between the two requires a more advanced analytical expression/formula.
Next in step 2A prong 2, independent claim 14 is analyzed to determine whether there are additional elements or combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception such that it is more than a drafting effort designed to monopolize the exception, in order to integrate the judicial exception into a practical application. These limitations have been identified and underlined above, and are not indicative of integration into a practical application because: (1) the recitation of the remote processing unit amounts to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Next in step 2B, independent claim 14 is considered to determine if it recites additional elements that amount to an inventive concept (“significantly more”) than the recited judicial exception. The limitation of the remote processing unit does not add something significantly more because such a recitation amounts to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). The use of generic computer equipment is considered insignificant additional elements. As recited in the MPEP, 2106.07(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection (see Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94).
Dependent claims 15-17 contain additional limitations that fall under the abstract idea grouping of a mental process or mathematical concepts, as they describe further steps of updating and adjusting the mathematical-based model, calculating a predicted fill status, or describing the type of mathematically-based learning algorithm used. Dependent claim 18 describes measuring the current fill status, but such a step does not provide an integration into a practical application or significantly more as it amounts to adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Claims 1-13 contain patent eligible subject matter as they describe sufficient structural details regarding the physical components and transmission of signals between such components in a container processing system to apply the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP 2106.05(e)).
4. An invention is not rendered ineligible for patent simply because it involves an abstract concept. Applications of such concepts "to a new and useful end" remain eligible for patent protection (see Alice Corp., 134 S. Ct. at 2354 (quoting Benson, 409 U.S. at 67)). However, "a claim for a new abstract idea is still an abstract idea" (see Synopsys v. Mentor Graphics Corp. _F.3d_, 120 U.S.P.Q. 2d1473 (Fed. Cir. 2016)). There needs to be additional elements or combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception or render the claim as a whole to be significantly more than the exception itself in order to demonstrate “integration into a practical application” or an “inventive concept.” For instance, particular physical arrangements for actively obtaining the sensor data, or further physical applications using the calculated fill status model to drive a physical transformation, change in operation, or repair/maintenance of a technology or technical process could provide integration into a practical application to demonstrate an improvement to the technology or technical field.
Claim Rejections - 35 USC § 102
5. 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.
6. Claim(s) 1-9 and 11-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mueller et al. (US Pat. Pub. 2014/0107953, hereinafter "Mueller").
In regards to claim 1, Mueller teaches a container system for calculating a current fill status of a container using a plurality of presence sensor interactions (Mueller abstract teaches a system for determining a fill level status of a container using a sensor unit that detects a plurality of objects passing into the container as as sensor interactions), the container system comprising:
a presence sensor arrangement for calculating the plurality of presence sensor interactions with the container (Mueller abstract and paragraphs [0036] and [0087] teach a sensor unit as a presence sensor arrangement for calculating a plurality of interaction outputs corresponding to an input or change in input as an object passes through the physical environment of the container);
a local counting unit for recording numbers of the plurality of presence sensor interactions as presence sensor interaction data (Mueller paragraphs [0046] and [0088] teach a waste counter (local counting unit) in a memory for incrementing and storing a count of the number of objects dropped in the container as a number of presence sensor sensor interactions);
a local communication unit for transmitting the presence sensor interaction data to a remote processing unit for processing the presence sensor interaction data and calculating the current fill status (Mueller Fig. 2 and paragraphs [0045] and [0088]-[0089] teach where each sensor unit has a suggested local communication unit for communicatively coupling the sensor interaction data output from the sensor unit 1120 to a separate remote processor 1108 (having cloud based data storage) for processing the interaction data and calculating the fill status); and
predicting a predicted fill status of the container in the remote processing unit by estimating the predicted fill status of the container as a function of time (Mueller abstract and paragraphs [0044] and [0089] teach the processor predicting a fill status of the container as objects fill the container as a function of time).
In regards to claim 2, Mueller teaches wherein the presence sensor interactions comprise interactions between a person coming close to the presence sensor arrangement or an object coming close to the presence sensor arrangement (Mueller abstract and paragraphs [0032], [0052], [0059], [0070], and [0087] teach where the presence sensor interactions comprise objects passing or being dropped into a waste container close to the sensor unit).
In regards to claim 3, Mueller teaches wherein the presence sensor arrangement is installed on at least one of an outside of the container, on an aperture of the container, in an aperture of the container, on a wall of the container, or in close vicinity to the container (Mueller paragraphs [0052] and [0070] teach where the sensor unit may be installed in close vicinity on an outside of the container such as near a chute arrangement or on a deckbase above the container).
In regards to claim 4¸ Mueller teaches wherein the presence sensor arrangement comprises at least one of a capacitive approach sensor, a switch pressure sensor, a pressure mapping sensor, an optical sensor, or an acoustic sensor (Mueller paragraph [0036] teaches where the sensor unit (presence sensor arrangement) comprises at least one of an optical sensor or acoustic sensor).
In regards to claim 5¸ Mueller teaches wherein the container comprises at least one of a glass waste container, a paper waste container, an organic waste container, a plastic waste container (Mueller paragraph [0032] teaches where the container comprises a waste container used in a medical laboratory system to hold waste objects such as glass test tube waste or pipette tip plastic waste).
In regards to claim 6, Mueller teaches a processing system for calculating a current fill status of a container using a plurality of presence sensor interactions (Mueller abstract and Fig. 2 teach a processing system for determining a fill level status of a container using a sensor unit that detects a plurality of objects passing into the container as as sensor interactions), the processing system comprising:
a remote communication unit for receiving presence sensor interaction data from the container (Mueller paragraph [0045]-[0046] and [0088]-[0089] suggest a remote communication unit for allowing an external (remote) cloud based data storage memory to receive sensor-measured interaction data corresponding to each object being dropped into a container; Mueller paragraph [0141] teaches communications over a wide area network such as the internet);
a remote processing unit (Mueller paragraph [0045] and Fig. 2 Item 1108 teach a remote processor 1108) comprising a fill status data model wherein the fill status data model comprises data correlating the current fill status of the container with the presence sensor interaction data (Mueller paragraphs [0089] and [0092] teach where the processor comprises a fill model that correlates a fill level status H1 with the presence interaction data of the number of objects);
wherein the remote processing unit is adapted to issue a fill status signal representative of the current fill status of the container (Mueller paragraphs [0058] and [0089] teaches generating a fill status notification message (signal) between zero and full to indicate a current fill status of the container); and
wherein the remote processing unit is adapted to transmit, using the remote communication unit, the fill status signal to a control center (Mueller Fig. 2 and paragraphs [0058]-[0059] and [0089] teach where the fill status signal is transmitted from the processor to an operator 1102 at a suggested control center to allow the operator to determine if the container needs to be emptied or replaced).
In regards to claim 7, Mueller teaches a method for calculating a current fill status of a container using a plurality of presence sensor interactions (Mueller abstract teaches a method for determining a fill level status of a container using a sensor unit that detects a plurality of objects passing into the container as as sensor interactions), the method comprising,
detecting, using a detection unit, the plurality of presence sensor interactions (Mueller abstract and paragraphs [0036] and [0087] teach detecting, using a sensor unit, a plurality of interaction outputs corresponding to an input or change in input as an object passes through the physical environment of the container);
recording numbers of the plurality of presence sensor interactions as a presence sensor interaction data in a local counting unit (Mueller paragraphs [0046] and [0088] teach recording a count of the number of objects dropped in the container as a number of presence sensor sensor interactions in a waste counter (local counting unit) in memory);
transmitting the presence sensor interaction data from the local counting unit to a remote processing unit (Mueller Fig. 2 and paragraphs [0045] and [0088]-[0089] teach communicatively coupling the sensor interaction data output from the sensor unit 1120 for transmission to a separate remote processor 1108 (having cloud based data storage) for processing the interaction data and calculating the fill status);
processing the presence sensor interaction data using the remote processing unit (Mueller abstract and paragraphs [0044]-[0045] and [0088]-[0089] teach the processor processing the sensor interaction data);
calculating the current fill status of the container from the processed presence sensor interaction data using a fill status data model, wherein the fill status data model comprises data correlating the fill status of the container with the presence sensor interaction data (Mueller paragraphs [0089] and [0092] teach where the processor calculates a fill status of the container using a fill status model that correlates a fill level status H1 with the presence interaction data of the number of objects); and
generating a fill status signal indicative of the current fill status of the container, calculated by the remote processing unit (Mueller paragraphs [0058] and [0089] teach generating a fill status notification message (signal) between zero and full to indicate a current fill status of the container).
In regards to claim 8, Mueller teaches wherein detecting the plurality of presence sensor interactions comprises a detected interaction, using a presence sensor arrangement, of at least one of a person or an object with the container (Mueller abstract and paragraphs [0032], [0052], [0059], [0070], and [0087] teach where the presence sensor interactions comprise objects passing or being dropped into a waste container close to the sensor unit).
In regards to claim 9¸ Mueller teaches wherein processing the presence sensor interaction data comprises updating the fill status data model (Mueller Table 1 and paragraphs [0090], [0092], and [0100] teach wherein processing the estimated number of presence interaction data comprises updating the fill status data model according to different weight factors that may change based on the fill level of the container).
In regards to claim 11, Mueller teaches wherein predicting the predicted fill status of the container comprises estimating the predicted fill status of the container as a function of time, using the current fill status of the container and the calibrated fill status data model (Mueller abstract and paragraphs [0044] and [0089] teach predicting a fill status of the container as objects fill the container as a function of time, paragraph [0088] teaches using the current fill status of the container to set an initialization value to a waste interactions counter of the model, and paragraphs [0090], [0092], and [0100] teach using a a calibrated fill status data model that uses correcting weighting factors that change based on the fill level of the container).
In regards to claim 12, Mueller teaches wherein generating the fill status signal comprises calculating at least one of the current fill status or the predicted fill status of the container as a fraction of the overall volume encompassed by the container (Mueller paragraphs [0058] and [0089] teach generating a fill status notification message (signal) as a percent fraction out of the overall volume encompassed by the container).
In regards to claim 13¸ Mueller teaches wherein the fill status signal comprises at least one the current fill status or the predicted fill status of the container or at least one of a requested collection time of the container (Mueller paragraphs [0058] and [0089] teach where the fill status notification message (signal) ranges between zero and full to indicate at least the current fill status of the container).
7. 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)(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.
Claim(s) 14-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Armstrong et al. (US Pat. Pub. No. 2021/0158097, hereinafter "Armstrong").
In regards to claim 14, Armstrong teaches a method for creating a fill status data model for enabling predicting a predicted fill status of a container using the fill status data model (Armstrong abstract and paragraph [0029] teach a method for creating a trained neural network model for predicting a fill level status of a container), the method comprising:
inputting a plurality of data relating to the current fill status of the container and a plurality of presence sensor interactions in the remote processing unit (Armstrong paragraphs [0023]-[0024] teach where a content sensor senses (interacts) with the interior of the container to generate a measurement image, and Armstrong paragraphs [0027], [0030], and [0034] teach inputting a plurality of training set data to a second system (remote processing unit) including a labeled fill status of the container and a plurality of corresponding presence sensor interaction images);
correlating the current fill status with the plurality of presence sensor interactions using a machine learning algorithm (Armstrong paragraph [0043], [0045], and [0049] teach carrying out training (correlating) of the current fill status with the plurality of presence sensor interaction images using a neural network learning algorithm (machine learning algorithm), including performing supervised learning using the training set data); and
creating the fill status data model from the correlating of the current fill status (Armstrong paragraph [0029] teaches creating a trained fill level determination model (i.e., fullness model) from the training (correlating) of the current fill status).
In regards to claim 15, Armstrong teaches further comprising:
updating the fill status data model by adjusting the fill status data model by a machine learning algorithm (Armstrong paragraph [0043] teaches updating the fill status data model by adjusting the weight values of the model by a neural network learning algorithm (machine learning algorithm)); and
adjusting the fill status data model comprises processing, using at least one of a plurality of presence sensor interactions, at least one of a measured current fill status of the container and at least one of a predicted current fill status of the container, by a machine learning algorithm (Armstrong paragraph [0043] and [0045] teach where adjusting the fill status data model comprises using a reference presence sensor interaction image associated with a known fill level of the container and a subject presence sensor interaction image for a to-be-assessed (predicted) fill status of the container).
In regards to claim 16, Armstrong teaches wherein predicting the predicted fill status of the container comprises estimating the predicted fill status of the container as a function of time (Armstrong paragraph [0044] teaches where the predicted fill status of the container comprises estimating the predicted fill status as a function of time as the neural network is trained on a timestamped series of images).
In regards to claim 17, Armstrong teaches wherein the machine learning algorithm comprises at least one of a supervised deep learning algorithm, an unsupervised deep learning algorithm, or a reinforcement deep learning algorithm (Armstrong paragraph [0043] teaches where the neural network learning algorithm comprises at least one of a supervised deep learning algorithm or unsupervised deep learning algorithm or a recurrent (reinforcement) deep learning algorithm).
In regards to claim 18, Armstrong teaches wherein measuring the current fill status of the container comprises at least one of a manual measurement, a weight measurement, or another detection of a current fill status of the container (Armstrong paragraph [0045] teaches measuring a known current fill status of the container using a human-determined manual measurement).
Claim Rejections - 35 USC § 103
8. 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
9. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Mueller et al. (US Pat. Pub. 2014/0107953) as applied to claim 7 above, and further in view of Romano et al. (US Pat. Pub. 2020/0034785, hereinafter "Romano").
In regards to claim 10, Mueller teaches wherein updating the fill status data model comprises adjusting the calculated current fill status of the container in the fill status data model, using at least one of a measured current fill status of the container and the plurality of current presence sensor interactions (Mueller Table 1 and paragraphs [0090], [0092], and [0100] teach updating the fill status data calculation model according to different weight factors that may change based on the current fill level of the container and the number of presence sensor interactions indicating a number of counted objects in the container).
Mueller fails to expressly teach updating by a deep learning algorithm.
Romano paragraph [0127] teaches applying an artificial intelligence (AI) system to refuse collection systems and services, and using deep learning as a type of machine learning (ML) technique to automatically learn, reconfigure, and improve over time for achieving contaminant detection and quantification. Romano paragraph [0179] teaches employing a suitable deep learning network to provide a measure of fit to train a model until a loss function is less than a threshold value for ensuring an appropriate degree of accuracy (e.g., confidence) in the prediction output by the model. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further combine the teachings of Romano because the use of a deep learning algorithm allows for automatic learning, reconfiguring, and improving of a model. Therefore, it would be beneficial to specify the use of a deep learning algorithm for updating the fill status data model in order to train and improve the model until a loss function is less than a threshold value, in order to ensure an appropriate degree of accuracy in the prediction output by the model.
Pertinent Art
10. Applicants are directed to consider additional pertinent prior art included on the Notice of References Cited (PTOL 892) attached herewith. The Examiner has pointed out particular references contained in the prior art of record within the body of this action for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the of the passage as taught by the prior art or disclosed by the Examiner. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
C. Patel et al. (US Pat. No. 11,475,417) discloses System and Method for Auditing the Fill Status of a Customer Waste Container by a Waste Services Provider During Performance of a Waste Service Activity.
D. Di Giugno et al. (US Pat. Pub. 2012/0330850) discloses Measuring Device, Container and System for Monitoring and Managing a Container. E. Faber et al. (US Pat. Pub. 2013/0335557) discloses Container With Detection Device for Determining a Status of The Container and Monitoring System for Dynamic Status Monitoring With at Least One Such Container.
F. Christensen et al. (US Pat. Pub. 2020/0191580) discloses Storage and Collection Systems and Methods for Use.
G. Welle et al. (US Pat. Pub. 2020/0355536) discloses Fill Level Measuring Device.
H. Anderson et al. (US Pat. Pub. 2022/0101280) discloses Systems and Methods for Waste Management.
I. Little et al. (US Pat. No. 6,123,017) discloses System and Method for Evaluating the Fill State of a Waste Container and Predicting When the Container Will be Full.
Conclusion
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL D LEE whose telephone number is (571)270-1598. The examiner can normally be reached on M to F, 9:30 am to 6 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen Vazquez can be reached at 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAUL D LEE/Primary Examiner, Art Unit 2857 10/14/2025