DETAILED ACTION
Notice of AIA Status
The present application, filed on 3/6/24, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are rejected.
Claim 19 is objected to.
Claim Objections
Claim 19 is objected to due to an informality.
Claim 19 recites “the plurality of acts comprise:…”. For clarity in verb subject number agreement, consider rephrasing to ‘the plurality of acts comprises:…’.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without adding significantly more.
Step 1: Claims 1-10 are directed towards a method process which is a statutory category under 35 U.S.C. §101; claims 11-19 are directed towards a machine which is a statutory category under 35 U.S.C. §101; and claim 20 is directed towards a manufacture which is a statutory category under 35 U.S.C. §101 (see MPEP §2106 “the claimed invention must be to one of the four statutory categories. 35 U.S.C. §101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter”).
Step 2A, Prong 1: an abstract idea is identified. Claim 1 recites the abstract idea: “analyzing the sensor data; based on analyzing the sensor data, determining that a hazardous material is in the vicinity of the one or more sensors; based on characteristics of the hazardous material, selecting a collection device that is configured to store the hazardous material; generating a control instruction that instructs the collection device to collect and store the hazardous material ” which can be performed in the human mind because a person can analyze the sensor data to identify hazardous material, select a collection device and generate an instruction for collecting the hazardous material (see the USPTO ‘October 2019 Update: Subject Matter Eligibility’ Guideline “claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: • a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.). Therefore, the claimed inventions are directed towards mental processes, which US courts have consistently deemed abstract ideas (see MPEP 2106 “the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, “methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’” 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).
Step 2A, Prong 2: has the abstract idea been integrated into a particular practical application? Once the abstract idea is performed, a signal is generated by providing, for output to the collection device, the control instruction that instructs the collection device to collect and store the hazardous material”, which isn’t particular, and instead is merely generally linking the abstract idea to the field of endeavor which is comparable to the alarm in Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978) (see MPEP §2016.05 g) and amounts to insignificant post-solution activity. The limitation “providing, for output to the collection device, the control instruction that instructs the collection device to collect and store the hazardous material” does not amount to particular practical application because providing an output to the collection device doesn’t require any further step or result (see MPEP 2106.05(h)).
Step 2B: does the claim recite any elements which are significantly more than the abstract idea? Other than the abstract idea and the insignificant post-solution activity, claim 1 recites “receiving, from one or more sensors, sensor data that reflects characteristics of an environment in a vicinity of the one or more sensors”. Here, the claimed sensor is generic and not particular and the receiving amount to insignificant extra- solution activity consisting of mere data gathering (see MPEP §2016.05 g). The USPTO ‘October 2019 Update: Subject Matter Eligibility’ Guideline identifies examples that did not integrate a judicial exception into a practical application: merely including instructions to implement the abstract idea on a computer, or using the computer as a tool to perform an abstract idea, adding insignificant extra-solution activity to the judicial exception, generally linking the use of a judicial exception to a particular technological environment or field of use. In addition, regarding the independent claims, the processors, the memory, the collection device are well-understood, routine and conventional (see MPEP 2106.05 d). For the method claim 1, if the determining is not that the hazardous material is the vicinity of the one or more sensors, no further steps are performed. For claims 11 and 20, outputting the instructions to the collection device does not require the collection device to actually collect and store the hazardous material or otherwise carry out any other instructions.
Technological improvement: the computer-implemented method of claim 1, the system of claim 11 and the non-transitory computer-readable media of claim 20 do not improve the functionally of a computer or of a technology, rather the computer is a mere conventional tool that applies the abstract idea. The use of the computer doesn’t improve the functionality of the computer itself or provide a technological advancement in field of endeavor (see MPEP 2106.05a).
Dependent claims 2-10 and 12-19 don’t solve these issue and are likewise unpatentable under 35 U.S.C. §101 (see MPEP §2016.07). Specifically, claims 2 and 12 recite a model which is still a mental process. Claims 3 and 13 recite training the model using machine learning and claim 4 recites updating the model using machine learning which doesn’t improve the functioning of a computer or other technology, integrate a judicial exception into a practical application or provide an inventive concept. Claims 7 and 16 further limit the sensor still to well-understood, routine and conventional sensors.
In summary, claims 1-20 don’t amount to significantly more than the judicial exception and as a result the claims are unpatentable under 35 U.S.C. §101.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5-6, 11, 14-15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Amidi (US20080122641) in view of Mallett (US20060212306).
With regard to claim 1, Amidi (US20080122641A1) teaches a computer-implemented method (see [0008], which recites “method includes detecting one or more hazardous materials at a portable sensor”)(see also [0009], which recites “a computer program is embodied on a computer readable medium and is operable to be executed by a processor. The computer program includes computer readable program code for receiving measurement data associated with one or more hazardous materials from a sensor, where the sensor is associated with an environment”), comprising:
receiving (see [0053], which recites “hazardous material measurement data is received at step 602. This may include, for example, the server 208 receiving the data from a hazardous material sensor 100”), from one or more sensors (hazardous material sensor 100 in [0053]), sensor data that reflects characteristics of an environment in a vicinity of the one or more sensors (hazardous material sensor 100) (see [0017], which recites “the hazardous material sensor 100 collects readings associated with one or more hazardous materials, such as by measuring the concentration of one or more hazardous gasses in the vicinity of the hazardous material sensor 100. The hazardous material sensor 100 can also be used to provide location information to an external system, such as by identifying any locations where the measured concentration of a hazardous gas exceeds a specified threshold”) (hazardous material measurement data is received from the hazardous material sensor 100 and is associated with the location of the hazardous material sensor 100, see [0054], which recites “a determination is made as to whether the location of the hazardous material sensor 100 is included or associated with the received data at step 604. The location data could represent the location of the hazardous material sensor 100 as determined by the hazardous material sensor 100”);
analyzing the sensor data (see [0056], which recites “the data is analyzed at step 610”);
based on analyzing the sensor data, determining that a hazardous material is in the vicinity of the one or more sensors (see [0056], which recites “the data is analyzed at step 610. This could include an application 216 analyzing the data to identify the locations where hazardous materials are or may become excessive. This could also include the application 216 identifying movement of the hazardous materials or any other analysis that uses the hazardous material measurements and the location data”);
Amidi fails to teach based on characteristics of the hazardous material, selecting a collection device that is configured to store the hazardous material;
generating a control instruction that instructs the collection device to collect and store the hazardous material; and
providing, for output to the collection device, the control instruction that instructs the collection device to collect and store the hazardous material.
In the analogous art of hazardous material management, Mallett (US20060212306A1) teaches based on characteristics (waste categories in [0105]) of the hazardous material, selecting a collection device (container in [0104]) that is configured to store the hazardous material (see [0105], which recites “once an item of waste is identified, the sorting algorithm determines to which of a plurality of waste categories the item belongs”) (see also [0104], which recites “the waste identifying mechanism is configured to identify a particular item of waste. … Identification of the waste item can be accomplished by … reading … chemical sensors”);
generating a control instruction (waste item identification device in [0111] generates a control instruction commanding the selected container door to open) that instructs the collection device (container) to collect and store the hazardous material (see [0105], which recites “once an item of waste is identified, the sorting algorithm determines to which of a plurality of waste categories the item belongs. The station then indicates to the user which container is associated with that category. … the station indicates a correct container by opening a door providing access to the container”); and
providing, for output to the collection device (container), the control instruction (waste item identification device in [0111] generates a control instruction commanding the selected container door to open) that instructs the collection device (container) to collect and store the hazardous material (see also [0298], which recites “a rotary level sensor operates in conjunction with a rotary lid. One function of the lid … is to open upon command from the electronics, allowing an item of hazardous waste to be deposited”) (see also [0111], which recites “waste item identification device is configured to receive a waste item identifier from a waste item, and a decision system is configured to assign the waste item to a waste category using the waste identifier and information contained in the classification database. Each of the containers is associated with at least one of the waste categories, and the decision system is further configured to indicate into which of the containers a waste item should be deposited based on the waste category. The decision system is further configured to open an alternate container if the station does not include a container associated with the assigned category. In one embodiment, for example, the alternate container is a container associated with the highest hazardous level will be opened”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi by incorporating selecting a collection device as disclosed by Mallett with a reasonable expectation of success to facilitate appropriate collection and storage of detected hazardous material, thereby improving hazardous material handling and mitigating contamination.
With respect to claim 5, Amidi in view of Mallett teaches the method of claim 1, comprising:
accessing data that includes the characteristics of the hazardous material and other characteristics of other hazardous materials (see [0267] of Mallett, which recites “the system for sorting waste comprises a computer equipped with one or more software applications and a database system that control the handling of each identified NDC. In one embodiment, the system could be enabled to identify the specific prompts and actions for each of the approximately 135,000 drugs in the NDC database. In an alternative embodiment, the actions are grouped into approximately two-dozen different handling procedures. In this embodiment, the database only needs to associate the NDC with a code representing the corresponding procedure. A separate database can then be used to define the details for prompts and actions associated with each waste group”), wherein the data that includes the characteristics of the hazardous material and the other characteristics of the other hazardous materials is generated during labeling of the hazardous material and the other hazardous materials (see also [0120] of Mallett, which recites “the waste sorting and disposal system can be significantly simplified by appropriately labeling of products that will eventually be disposed as waste. …, a prescription drug label may provide disposal information at the time the label is generated. For example, the drug vial or other pharmaceutical product label may indicate in what waste category the item should be disposed. As illustrated in FIG. 50, in one embodiment, the label may provide alternative waste categories under which it should be disposed, depending on whether the item is empty or not empty and/or whether the items is or is not a sharps. Such waste categorization information printed on such labels may be obtained from a waste disposal database as discussed herein”) (see also [0121] of Mallett, which recites “an institution may print its own specific labels that are based on waste categories. In one embodiment, multiple labels are generated, each with its own simple code (color, numerals, letters, etc.) and affixed to a drug vial. At the time of disposal, the scanner (which is configured to read these institution specific codes) is able to associate the waste item with the appropriate waste container. In one embodiment, a scanner is not needed. Rather, the user can read the symbol and dispose of the waste accordingly”) (see [0265] of Mallett, which recites “scanning of the waste item for a determination of the National Drug Code (NDC) number”).
With respect to claim 6, Amidi in view of Mallett teaches the method of claim 1, wherein the hazardous material is a syringe (see [0009] of Mallett, which recites “Hazardous materials may include, without limitation, syringes”).
With respect to claim 11, Amidi (US20080122641) teaches a system (system 200 in [0030], illustrated in Fig. 2), comprising:
one or more processors (processors 212 in [0035]) ; and
memory (memories 214 in [0035]) including a plurality of computer-executable components (applications 216 in [0035]) that are executable by the one or more processors to perform a plurality of acts (see [0035], which recites “server 208 includes one or more processors 212 and one or more memories 214 capable of storing data and instructions used by the processors 212. As a particular example, the server 208 could include one or more applications 216 executed by the processor(s) 212. The applications 216 could use the data from the hazardous material sensors 100 to perform a wide variety of functions. For example, an application 216 could map the locations of excessive hazardous materials in a processing or other environment, determine how hazardous materials move, detect a build-up of hazardous materials, or detect trends in the presence or concentration of hazardous materials. The application 216 could perform any other or additional functions using the data from the hazardous material sensors 100.”), the plurality of acts comprising:
receiving (see [0053], which recites “hazardous material measurement data is received at step 602. This may include, for example, the server 208 receiving the data from a hazardous material sensor 100”), from one or more sensors (hazardous material sensor 100 in [0053]), sensor data that reflects characteristics of an environment in a vicinity of the one or more sensors (hazardous material sensor 100) (see [0017], which recites “the hazardous material sensor 100 collects readings associated with one or more hazardous materials, such as by measuring the concentration of one or more hazardous gasses in the vicinity of the hazardous material sensor 100. The hazardous material sensor 100 can also be used to provide location information to an external system, such as by identifying any locations where the measured concentration of a hazardous gas exceeds a specified threshold”) (hazardous material measurement data received from the hazardous material sensor 100 is associated with the location of the hazardous material sensor 100, see [0054], which recites “a determination is made as to whether the location of the hazardous material sensor 100 is included or associated with the received data at step 604. The location data could represent the location of the hazardous material sensor 100 as determined by the hazardous material sensor 100”);
analyzing the sensor data (see [0056], which recites “the data is analyzed at step 610”);
based on analyzing the sensor data, determining that a hazardous material is in the vicinity of the one or more sensors (see [0056], which recites “the data is analyzed at step 610. This could include an application 216 analyzing the data to identify the locations where hazardous materials are or may become excessive. This could also include the application 216 identifying movement of the hazardous materials or any other analysis that uses the hazardous material measurements and the location data”);
Amidi fails to teach based on characteristics of the hazardous material, selecting a collection device that is configured to store the hazardous material;
generating a control instruction that instructs the collection device to collect and store the hazardous material; and
providing, for output to the collection device, the control instruction that instructs the collection device to collect and store the hazardous material.
In the analogous art of hazardous material management, Mallett (US20060212306A1) teaches based on characteristics (waste categories in [0105]) of the hazardous material, selecting a collection device (container in [0104]) that is configured to store the hazardous material (see [0105], which recites “once an item of waste is identified, the sorting algorithm determines to which of a plurality of waste categories the item belongs”) (see also [0104], which recites “the waste identifying mechanism is configured to identify a particular item of waste. … Identification of the waste item can be accomplished by … reading … chemical sensors”);
generating a control instruction (waste item identification device in [0111] generates a control instruction commanding the selected container door to open) that instructs the collection device (container) to collect and store the hazardous material (see [0105], which recites “once an item of waste is identified, the sorting algorithm determines to which of a plurality of waste categories the item belongs. The station then indicates to the user which container is associated with that category. … the station indicates a correct container by opening a door providing access to the container”); and
providing, for output to the collection device (container), the control instruction (waste item identification device in [0111] generates a control instruction commanding the selected container door to open) that instructs the collection device (container) to collect and store the hazardous material (see also [0298], which recites “a rotary level sensor operates in conjunction with a rotary lid. One function of the lid … is to open upon command from the electronics, allowing an item of hazardous waste to be deposited”) (see also [0111], which recites “waste item identification device is configured to receive a waste item identifier from a waste item, and a decision system is configured to assign the waste item to a waste category using the waste identifier and information contained in the classification database. Each of the containers is associated with at least one of the waste categories, and the decision system is further configured to indicate into which of the containers a waste item should be deposited based on the waste category. The decision system is further configured to open an alternate container if the station does not include a container associated with the assigned category. In one embodiment, for example, the alternate container is a container associated with the highest hazardous level will be opened”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system as disclosed by Amidi by incorporating selecting a collection device as disclosed by Mallett with a reasonable expectation of success to facilitate appropriate collection and storage of detected hazardous material, thereby improving hazardous material handling and mitigating contamination.
With respect to claim 14, Amidi in view of Mallett teaches system of claim 11, wherein the plurality of acts comprise:
accessing data that includes the characteristics of the hazardous material and other characteristics of other hazardous materials (see also [0267] of Mallett, which recites “the system for sorting waste comprises a computer equipped with one or more software applications and a database system that control the handling of each identified NDC. In one embodiment, the system could be enabled to identify the specific prompts and actions for each of the approximately 135,000 drugs in the NDC database. In an alternative embodiment, the actions are grouped into approximately two-dozen different handling procedures. In this embodiment, the database only needs to associate the NDC with a code representing the corresponding procedure. A separate database can then be used to define the details for prompts and actions associated with each waste group”), wherein the data that includes the characteristics of the hazardous material and the other characteristics of the other hazardous materials is generated during labeling of the hazardous material and the other hazardous materials (see also [0120], which recites “the waste sorting and disposal system can be significantly simplified by appropriately labeling of products that will eventually be disposed as waste …, a prescription drug label may provide disposal information at the time the label is generated. For example, the drug vial or other pharmaceutical product label may indicate in what waste category the item should be disposed. As illustrated in FIG. 50, in one embodiment, the label may provide alternative waste categories under which it should be disposed, depending on whether the item is empty or not empty and/or whether the items is or is not a sharps. Such waste categorization information printed on such labels may be obtained from a waste disposal database as discussed herein”) (see also [0121], which recites “an institution may print its own specific labels that are based on waste categories. In one embodiment, multiple labels are generated, each with its own simple code (color, numerals, letters, etc.) and affixed to a drug vial. At the time of disposal, the scanner (which is configured to read these institution specific codes) is able to associate the waste item with the appropriate waste container. In one embodiment, a scanner is not needed. Rather, the user can read the symbol and dispose of the waste accordingly”) (see [0265] of Mallett, which recites “scanning of the waste item for a determination of the National Drug Code (NDC) number”).
With respect to claim 15, Amidi in view of Mallett teaches the system of claim 11, wherein the hazardous material is a syringe (see [0009] of Mallett, which recites “Hazardous materials may include, without limitation, syringes”).
With respect to claim 20, Amidi teaches one or more non-transitory computer-readable media (computer readable medium in [0009], which recites “a computer program is embodied on a computer readable medium and is operable to be executed by a processor. The computer program includes computer readable program code for receiving measurement data associated with one or more hazardous materials from a sensor, where the sensor is associated with an environment”) storing computer-executable instructions (applications 216 in [0035]) that upon execution cause one or more computers to perform acts comprising:
receiving (see [0053], which recites “Hazardous material measurement data is received at step 602. This may include, for example, the server 208 receiving the data from a hazardous material sensor 100”), from one or more sensors (hazardous material sensor 100 in [0053]), sensor data that reflects characteristics of an environment in a vicinity of the one or more sensors (hazardous material sensor 100) (see [0017], which recites “the hazardous material sensor 100 collects readings associated with one or more hazardous materials, such as by measuring the concentration of one or more hazardous gasses in the vicinity of the hazardous material sensor 100. The hazardous material sensor 100 can also be used to provide location information to an external system, such as by identifying any locations where the measured concentration of a hazardous gas exceeds a specified threshold”) (hazardous material measurement data received from the hazardous material sensor 100 is associated with the location of the hazardous material sensor 100, see [0054], which recites “a determination is made as to whether the location of the hazardous material sensor 100 is included or associated with the received data at step 604. The location data could represent the location of the hazardous material sensor 100 as determined by the hazardous material sensor 100”);
analyzing the sensor data (see also [0056], which recites “the data is analyzed at step 610”);
based on analyzing the sensor data, determining that a hazardous material is in the vicinity of the one or more sensors (see [0056], which recites “the data is analyzed at step 610. This could include an application 216 analyzing the data to identify the locations where hazardous materials are or may become excessive. This could also include the application 216 identifying movement of the hazardous materials or any other analysis that uses the hazardous material measurements and the location data”);
Amidi fails to teach based on characteristics of the hazardous material, selecting a collection device that is configured to store the hazardous material;
generating a control instruction that instructs the collection device to collect and store the hazardous material; and
providing, for output to the collection device, the control instruction that instructs the collection device to collect and store the hazardous material.
In the analogous art of hazardous material management, Mallett (US20060212306A1) teaches based on characteristics (waste categories in [0105]) of the hazardous material, selecting a collection device (container in [0104]) that is configured to store the hazardous material (see [0105], which recites “once an item of waste is identified, the sorting algorithm determines to which of a plurality of waste categories the item belongs”) (see also [0104], which recites “the waste identifying mechanism is configured to identify a particular item of waste. … Identification of the waste item can be accomplished by … reading … chemical sensors”);
generating a control instruction (waste item identification device in [0111] generates a control instruction commanding the selected container door to open) that instructs the collection device (container) to collect and store the hazardous material (see [0105], which recites “once an item of waste is identified, the sorting algorithm determines to which of a plurality of waste categories the item belongs. The station then indicates to the user which container is associated with that category. … the station indicates a correct container by opening a door providing access to the container”); and
providing, for output to the collection device (container), the control instruction (waste item identification device in [0111] generates a control instruction commanding the selected container door to open) that instructs the collection device (container) to collect and store the hazardous material (see also [0298], which recites “a rotary level sensor operates in conjunction with a rotary lid. One function of the lid … is to open upon command from the electronics, allowing an item of hazardous waste to be deposited”) (see also [0111], which recites “waste item identification device is configured to receive a waste item identifier from a waste item, and a decision system is configured to assign the waste item to a waste category using the waste identifier and information contained in the classification database. Each of the containers is associated with at least one of the waste categories, and the decision system is further configured to indicate into which of the containers a waste item should be deposited based on the waste category. The decision system is further configured to open an alternate container if the station does not include a container associated with the assigned category. In one embodiment, for example, the alternate container is a container associated with the highest hazardous level will be opened”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the one or more non-transitory computer-readable media as disclosed by Amidi by incorporating selecting a collection device as disclosed by Mallett with a reasonable expectation of success to facilitate appropriate collection and storage of detected hazardous material, thereby improving hazardous material handling and mitigating contamination.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Amidi (US20080122641) in view of Mallett (US20060212306) in view of Tomlin (US20160232498).
With respect to Claim 2, Amidi in view of Mallett teaches the method of claim 1.
Amidi in view of Mallett fails to teach that:
analyzing the sensor data comprises providing the sensor data as an input to a model that is configured to receive given sensor data and output given data indicating whether given hazardous material is in a given vicinity of given one or more sensors, and
determining that the hazardous material is in the vicinity of the one or more sensors comprises receiving, from the model, data indicating that the hazardous material is in the vicinity of the one or more sensors.
In the analogous art of hazardous material management, Tomlin (US20160232498) teaches providing sensor data (captured image/video data obtained by the user device 102 and transmitted to the CPU 110 for processing using machine learning/ artificial intelligence, see [0030], which recites “the captured data is transmitted from the user device 102 to the CPU 110 via the server 106. The captured data may be transmitted/streamed from the user device 102 in real-time or near real-time, as the user device 102 is capturing data from the scene 104. In another embodiment, the user device 104 can capture multiple images or video sequences of the scene 104. For example, the user 100 may capture a close up of specific objects in the scene 104, as well as a set-back view from a distance showing multiple objects and their surrounding environment”) as an input to a model (machine learning in [0038]) that is configured to receive given sensor data (see [0038], which recites “the transmitted data is processed by the CPU 110. In an embodiment, the CPU 110 performs object recognition of the scene 104. Various methods may be applied by the CPU 110 to perform objection recognition. In an embodiment, machine learning and artificial intelligence is used to extract objects from the scene 104 and compare them to known objects in a database coupled to the CPU 110”) and output given data indicating whether given hazardous material is in a given vicinity of given one or more sensors (see [0052], which recites “the CPU 110 determines if any of the detected objects in the scene 104 are hazardous materials”) (see also [0041], which recites “the CPU 110 may employ optical character recognition (OCR) technologies to read labels on various objects,… to not only identify objects, but also to determine if the objects may be hazardous materials”), and
determining that the hazardous material is in the vicinity of the one or more sensors comprises receiving, from the model, data indicating that the hazardous material is in the vicinity of the one or more sensors (see also [0038], which recites “In step 206, the transmitted data is processed by the CPU 110. In an embodiment, the CPU 110 performs object recognition of the scene 104. Various methods may be applied by the CPU 110 to perform objection recognition. In an embodiment, machine learning and artificial intelligence is used to extract objects from the scene 104 and compare them to known objects in a database coupled to the CPU 110”) (see also [0032], which recites “audio/visual data which may be captured by the user device 102, the user device 102 can be capture various spatial and geographical data related to the scene 104”) (see also [0041], which recites “the CPU 110 may employ optical character recognition (OCR) technologies to read labels on various objects, such as on gasoline tanks, prescription bottles, batteries, etc., to not only identify objects, but also to determine if the objects may be hazardous materials, prohibited items, illegal items, or items that require a surcharge for removal and/or disposal”) (see also Fig. 2 which illustrate that that the data is analyzed by the CPU and a determination of hazardous object as to whether a hazardous object is present results from the analysis.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi in view of Mallett by incorporating the model (machine learning/artificial intelligence) as disclosed by Tomlin with a reasonable expectation of success to facilitate automated identification of hazardous material from sensed data, thereby enabling appropriate disposal of the detected hazardous object (see [0053] of Tomlin, which recites “the CPU 110 can determine appropriate disposal recommendations for the detected objects”).
With respect to claim 12, Amidi in view of Mallett teaches the system of claim 11.
Amidi in view of Mallett fails to teach that:
analyzing the sensor data comprises providing the sensor data as an input to a model that is configured to receive given sensor data and output given data indicating whether given hazardous material is in a given vicinity of given one or more sensors, and
determining that the hazardous material is in the vicinity of the one or more sensors comprises receiving, from the model, data indicating that the hazardous material is in the vicinity of the one or more sensors.
In the analogous art of hazardous material management, Tomlin (US20160232498) teaches providing sensor data (captured image/video data obtained by the user device 102 and transmitted to the CPU 110 for processing using machine learning/ artificial intelligence, see [0030], which recites “the captured data is transmitted from the user device 102 to the CPU 110 via the server 106. The captured data may be transmitted/streamed from the user device 102 in real-time or near real-time, as the user device 102 is capturing data from the scene 104. In another embodiment, the user device 104 can capture multiple images or video sequences of the scene 104. For example, the user 100 may capture a close up of specific objects in the scene 104, as well as a set-back view from a distance showing multiple objects and their surrounding environment”) as an input to a model (machine learning in [0038]) that is configured to receive given sensor data (see [0038], which recites “the transmitted data is processed by the CPU 110. In an embodiment, the CPU 110 performs object recognition of the scene 104. Various methods may be applied by the CPU 110 to perform objection recognition. In an embodiment, machine learning and artificial intelligence is used to extract objects from the scene 104 and compare them to known objects in a database coupled to the CPU 110”) and output given data indicating whether given hazardous material is in a given vicinity of given one or more sensors (see [0052], which recites “the CPU 110 determines if any of the detected objects in the scene 104 are hazardous materials”) (see also [0041], which recites “the CPU 110 may employ optical character recognition (OCR) technologies to read labels on various objects,… to not only identify objects, but also to determine if the objects may be hazardous materials”), and
determining that the hazardous material is in the vicinity of the one or more sensors comprises receiving, from the model, data indicating that the hazardous material is in the vicinity of the one or more sensors (see also [0038], which recites “In step 206, the transmitted data is processed by the CPU 110. In an embodiment, the CPU 110 performs object recognition of the scene 104. Various methods may be applied by the CPU 110 to perform objection recognition. In an embodiment, machine learning and artificial intelligence is used to extract objects from the scene 104 and compare them to known objects in a database coupled to the CPU 110”) (see also [0032], which recites “audio/visual data which may be captured by the user device 102, the user device 102 can be capture various spatial and geographical data related to the scene 104”) (see also [0041], which recites “the CPU 110 may employ optical character recognition (OCR) technologies to read labels on various objects, such as on gasoline tanks, prescription bottles, batteries, etc., to not only identify objects, but also to determine if the objects may be hazardous materials, prohibited items, illegal items, or items that require a surcharge for removal and/or disposal”) (see also Fig. 2 which illustrate that that the data is analyzed by the CPU and a determination of hazardous object as to whether a hazardous object is present results from the analysis.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system as disclosed by Amidi in view of Mallett by incorporating the model (machine learning/artificial intelligence) as disclosed by Tomlin with a reasonable expectation of success to facilitate automated identification of hazardous material from sensed data, thereby enabling appropriate disposal of the detected hazardous object (see [0053] of Tomlin, which recites “the CPU 110 can determine appropriate disposal recommendations for the detected objects”).
Claims 3-4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Amidi (US20080122641A1) in view of Mallett (US20060212306) in view of Tomlin (US20160232498) in view of Moolman (WO2022013738A1).
With respect to claim 3, Amidi in view of Mallett in view of Tomlin teaches the method of claim 2.
Amidi in view of Mallett in view of Tomlin fails to teach receiving historical data that includes previous sensor data and previous data indicating whether previous hazardous material is in a previous vicinity of previous one or more sensors; and
training, using machine learning and the historical data, the model.
In the analogous art of hazardous material management, Moolman (WO2022013738A1) teaches receiving historical data that includes previous sensor data (see page 18 which recites “server (12) and/or the data analytics module (57) may be enabled to keep track of historical data of each worker and this historical data may be included in that worker’s profile (64). The historical data may include sensor data”) and previous data indicating whether previous hazardous material is in a previous vicinity of previous one or more sensors (see page 13, which recites “video analytics or detections include…, identifying … hazardous objects, hazardous substances (e.g. oil spills), and/or hazardous events (e.g. smoke and/or fire)”); and
training, using machine learning and the historical data, the model (see page 23, which recites “server may include an industrial database for machine learning training, which may be implemented, accessed or used by the data analytics module (57). The data analytics module (57) may also implement video labelling technology, for example …labelling potentially harmful environments, or potentially harmful environments or equipment. The data analytics module (57) may be trained with a plurality of training images”) (see page 30, which recites “sensor data or environmental parameters may be centrally located or it may be stored in a central repository, for example the database (40 or 42). This data repository may be kept up to date, updated frequently, repetitively or in real-time”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi in view of Mallett in view of Tomlin by training the model using historical data as disclosed by Moolman with a reasonable expectation of success to improve identification of hazardous materials.
With respect to claim 4, Amidi in view of Mallett in view of Tomlin in view of Moolman teaches the method of claim 3, comprising:
receiving data (see also page 32 of Moolman, which recites “expert rules and Al may further compare and process (1057.2) the sensor data (1037) or environmental data, and it may be compared to a threshold, depending on the type of sensor”) confirming that the hazardous material is in the vicinity of the one or more sensors (see page 13, which recites “video analytics or detections include…, identifying … hazardous objects, hazardous substances (e.g. oil spills), and/or hazardous events (e.g. smoke and/or fire)”) (see page 18 of Moolman which recites “server (12) and/or the data analytics module (57) may be enabled to keep track of historical data of each worker and this historical data may be included in that worker’s profile (64). The historical data may include sensor data”) (see also page 26-27 of Moolman, which recites “Machine learning may be implemented by the data analytics module (57), which … include efficient capture of training data, data labelling and preparation, appropriate model selection and training, model performance evaluation”); and
updating, using machine learning, the model using the sensor data and the data confirming that the hazardous material is in the vicinity of the one or more sensors (see page 26-27 of Moolman, which recites “Machine learning may be implemented by the data analytics module (57), which … include …, ongoing model improvements”) (see also Fig. 7).
With respect to claim 13, Amidi in view of Mallett in view of Tomlin teaches the system of claim 12.
Amidi in view of Mallett in view of Tomlin fails to teach receiving historical data that includes previous sensor data and previous data indicating whether previous hazardous material is in a previous vicinity of previous one or more sensors;
training, using machine learning and the historical data, the model;
receiving data confirming that the hazardous material is in the vicinity of the one or more sensors; and
updating, using machine learning, the model using the sensor data and the data confirming that the hazardous material is in the vicinity of the one or more sensors.
In the analogous art of hazardous material management, Moolman (WO2022013738A1) teaches receiving historical data that includes previous sensor data (see page 18 which recites “server (12) and/or the data analytics module (57) may be enabled to keep track of historical data of each worker and this historical data may be included in that worker’s profile (64). The historical data may include sensor data”) and previous data indicating whether previous hazardous material is in a previous vicinity of previous one or more sensors (see page 13, which recites “video analytics or detections include…, identifying … hazardous objects, hazardous substances (e.g. oil spills), and/or hazardous events (e.g. smoke and/or fire)”); and
training, using machine learning and the historical data, the model (see page 23, which recites “server may include an industrial database for machine learning training, which may be implemented, accessed or used by the data analytics module (57). The data analytics module (57) may also implement video labelling technology, for example …labelling potentially harmful environments, or potentially harmful environments or equipment. The data analytics module (57) may be trained with a plurality of training images”) (see page 30, which recites “sensor data or environmental parameters may be centrally located or it may be stored in a central repository, for example the database (40 or 42). This data repository may be kept up to date, updated frequently, repetitively or in real-time”);
receiving data (see page 32 of Moolman, which recites “expert rules and Al may further compare and process (1057.2) the sensor data (1037) or environmental data, and it may be compared to a threshold, depending on the type of sensor”) confirming that the hazardous material is in the vicinity of the one or more sensors (see page 13, which recites “video analytics or detections include…, identifying … hazardous objects, hazardous substances (e.g. oil spills), and/or hazardous events (e.g. smoke and/or fire). Captured images of workers may be received from the camera or image capturing device (28) by the server (12)”) (see page 18 of Moolman which recites “server (12) and/or the data analytics module (57) may be enabled to keep track of historical data of each worker and this historical data may be included in that worker’s profile (64). The historical data may include sensor data”) (see also page 26-27 of Moolman, which recites “Machine learning may be implemented by the data analytics module (57), which … include efficient capture of training data, data labelling and preparation, appropriate model selection and training, model performance evaluation”); and
updating, using machine learning, the model using the sensor data and the data confirming that the hazardous material is in the vicinity of the one or more sensors (see page 26-27 of Moolman, which recites “machine learning may be implemented by the data analytics module (57), which … include …, ongoing model improvements”) (see also Fig. 7 on Moolman).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system as disclosed by Amidi in view of Mallett in view of Tomlin by training the model using historical data as disclosed by Moolman with a reasonable expectation of success to improve identification of hazardous materials.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Amidi (US20080122641A1) in view of Mallett (US20060212306) in view of Graves (CA2595830A1).
With respect to claim 8, Amidi in view of Mallett teaches the method of claim 1, wherein the characteristics of the hazardous material includes measurements (see [0104] of Mallett, which recites “measuring or evaluating any other qualitative parameter of the waste item presented for identification”), and a type of the hazardous material (see [0229] of Mallett, which recites “the sorting algorithm has assigned an item to a waste category”).
Amidi in view of Mallett fails to teach that the characteristics of the hazardous material include a distribution location and a location.
In the analogous art of hazardous material management, Graves (CA2595830A1) teaches characteristics of the hazardous material (see page 223, which recites “Hazardous Drugs Spill) includes a distribution location (see page 183, which recites “Drug status - in storage, on cart for distribution, at distribution point/ administration point”) and a location (see page 182, which recites “Tracking, identifying drugs by their container tags at each location, identifying when drugs are moved … e) Drug location”) (see page 21, which recites “Functions …Tracking whereabouts of drugs through the chain … Outputs …. Drug location”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi in view of Mallett by incorporating the distribution location and the location of hazardous materials as disclosed by Graves with a reasonable expectation of success to facilitate tracking the location and movement of hazardous materials during storage, distribution and collection.
With respect to claim 17, Amidi in view of Mallett teaches the system of claim 11, wherein the characteristics of the hazardous material includes measurements (see [0104] of Mallett, which recites “measuring or evaluating any other qualitative parameter of the waste item presented for identification”), and a type of the hazardous material (see [0229] of Mallett, which recites “the sorting algorithm has assigned an item to a waste category”).
Amidi in view of Mallett fails to teach that the characteristics of the hazardous material include a distribution location and a location.
In the analogous art of hazardous material management, Graves (CA2595830A1) teaches characteristics of the hazardous material (see page 223, which recites “Hazardous Drugs Spill) includes a distribution location (see page 183, which recites “Drug status - in storage, on cart for distribution, at distribution point/administration point”) and a location (see page 182, which recites “Tracking, identifying drugs by their container tags at each location, identifying when drugs are moved … e) Drug location”) (see pag1 21, which recites “Functions …Tracking whereabouts of drugs through the chain … Outputs …. Drug location”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system as disclosed by Amidi in view of Mallett by incorporating the distribution location and the location of hazardous materials as disclosed by Graves with a reasonable expectation of success to facilitate tracking the location and movement of hazardous materials during storage, distribution and collection.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Amidi (US20080122641A1) in view of Mallett (US20060212306) in view of Glock (EP1970843A1).
With respect to claim 9, Amidi in view of Mallett teaches the method of claim 1.
Amidi in view of Mallett fails to teach based on the characteristics of the hazardous material, determining a likely location of additional hazardous material.
In the analogous art of hazardous material management, Glock (EP1970843A1) teaches based on the characteristics of the hazardous material (see page 3, which recites “providing the coordinates of the site of the accident and the identification of the hazardous substance, Providing flow and hazard data of the hazardous substance from a hazardous materials database, based on the nature of the hazardous substance”) determining a likely location of additional hazardous material (see page 3, which recites “After the local environmental data are collected and the hazardous substance is determined, a calculation of the propagation and penetration behavior is performed of the hazardous substance into the soil and into the drainage system. From this calculation, conclusions can be drawn on the groundwater contamination and in particular on the areal distribution of the hazardous substance, in particular with regard to the flow direction and velocity of the hazardous substance contamination. Thus, it is possible to draw conclusions about the flow direction and velocity of the hazardous substance from the calculation, so that targeted defensive measures from the place of the accident must be taken only in certain areas”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi in view of Mallett by incorporating determining a likely location of additional hazardous material based on the characteristics of the hazardous material as disclosed by Glock with a reasonable expectation of success to facilitate targeted defensive measures (see page 3 of Glock).
With respect to claim 18, Amidi in view of Mallett teaches the system of claim 11.
Amidi in view of Mallett fails to teach based on the characteristics of the hazardous material, determining a likely location of additional hazardous material.
In the analogous art of hazardous material management, Glock (EP1970843A1) teaches based on the characteristics of the hazardous material (see page 3, which recites “providing the coordinates of the site of the accident and the identification of the hazardous substance, providing flow and hazard data of the hazardous substance from a hazardous materials database, based on the nature of the hazardous substance”) determining a likely location of additional hazardous material (see page 3, which recites “after the local environmental data are collected and the hazardous substance is determined, a calculation of the propagation and penetration behavior is performed of the hazardous substance into the soil and into the drainage system. From this calculation, conclusions can be drawn on the groundwater contamination and in particular on the areal distribution of the hazardous substance, in particular with regard to the flow direction and velocity of the hazardous substance contamination. Thus, it is possible to draw conclusions about the flow direction and velocity of the hazardous substance from the calculation, so that targeted defensive measures from the place of the accident must be taken only in certain areas”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system as disclosed by Amidi in view of Mallett by incorporating determining a likely location of additional hazardous material based on the characteristics of the hazardous material as disclosed by Glock with a reasonable expectation of success to facilitate targeted defensive measures (see page 3 of Glock).
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Amidi (US20080122641A1) in view of Mallett (US20060212306) in view of Graves (CA2595830A1) in view of Moolman (WO2022013738).
With respect to claim 10, Amidi in view of Mallett teaches the method of claim 1.
Amidi in view of Mallett fails to teach storing data indicating a distribution location of the hazardous material and data indicating the location of the hazardous material.
In the analogous art of hazardous material management, Graves (CA2595830A1) teaches characteristics of the hazardous material (see page 223, which recites “Hazardous Drugs Spill) includes a distribution location (see page 183, which recites “Drug status - in storage, on cart for distribution, at distribution point/administration point”) and a location (see page 182, which recites “Tracking, identifying drugs by their container tags at each location, identifying when drugs are moved … e) Drug location”) (see pag1 21, which recites “Functions …Tracking whereabouts of drugs through the chain … Outputs …. Drug location”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi in view of Mallett by incorporating the distribution location and the location of hazardous materials as disclosed by Graves with a reasonable expectation of success to facilitate tracking the location and movement of hazardous materials during storage, distribution and collection.
Graves doesn’t teach storing data.
In the analogous art of hazardous material management, Moolman (WO2022013738) teaches storing data (see page 10, which recites “plurality of sensors (22, 24, 26, 28, 30, 32, 34, 36, 10 38) may be in data communication with the server (12) and may be capable of sensing or generating sensor data in real-time, … It will be appreciated that sensing need not necessarily be performed in real-time or near real-time, and data may be sensed, stored and communicated at a later stage”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi in view of Mallett in view of Graves by incorporating storing data as disclosed by Moolman with a reasonable expectation of success for the benefit of communicating at a later stage (see page 10 of Moolman).
With respect to claim 19, Amidi in view of Mallett teaches the system of claim 11.
Amidi in view of Mallett fails to teach storing data indicating a distribution location of the hazardous material and data indicating the location of the hazardous material.
In the analogous art of hazardous material management, Graves (CA2595830A1) teaches characteristics of the hazardous material (see page 223, which recites “Hazardous Drugs Spill) includes a distribution location (see page 183, which recites “Drug status - in storage, on cart for distribution, at distribution point/administration point”) and a location (see page 182, which recites “Tracking, identifying drugs by their container tags at each location, identifying when drugs are moved … e) Drug location”) (see pag1 21, which recites “Functions …Tracking whereabouts of drugs through the chain … Outputs …. Drug location”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi in view of Mallett by incorporating the distribution location and the location of hazardous materials as disclosed by Graves with a reasonable expectation of success to facilitate tracking the location and movement of hazardous materials during storage, distribution and collection.
Graves doesn’t teach storing data.
In the analogous art of hazardous material management, Moolman (WO2022013738) teaches storing data (see page 10, which recites “plurality of sensors (22, 24, 26, 28, 30, 32, 34, 36, 10 38) may be in data communication with the server (12) and may be capable of sensing or generating sensor data in real-time, … It will be appreciated that sensing need not necessarily be performed in real-time or near real-time, and data may be sensed, stored and communicated at a later stage”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system as disclosed by Amidi in view of Mallett in view of Graves by incorporating storing data as disclosed by Moolman with a reasonable expectation of success for the benefit of communicating at a later stage (see page 10 of Moolman).
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Amidi (US20080122641) in view of Mallett (US20060212306) in view of Johnson (US202000160682) in view of Morgan (US20130175373) in view of Kurani (US20210188541) in view of Tomlin (US20160232498) in view of Chen (CN105574700A) in view of Phillips (US20060270421).
With respect to claim 7, Amidi in view of Mallett teaches the method of claim 1, wherein the one or more sensors comprise a camera (video camera in [0065] of Mallett), an RFID detector (sorting station in [0025], which recites “the sorting station detects … such as … reading RFID tag”) a chemical detector (chemical sensors in [0104]), a light sensor (light detector 232 in [0175]) and a pressure sensor (pressure transducer in [0205]).
Amidi in view of Mallett fails to teach a light detection and ranging device, a metal detector, a fluorescence detector, a microphone, a thermometer, a proximity sensor, an accelerometer, a gyroscope, a gravity sensor, a magnetometer, a humidity sensor, and a barometer.
In the analogous art of hazardous material management, Johnson (US20200160682) teaches a light detection and ranging device (see [0026], which recites “a light detection and ranging (LIDAR) sensor”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi in view of Mallett by incorporating the light detection and ranging device as disclosed by Johnson with a reasonable expectation of success for the benefit of obtaining depth and distance data for hazardous material and surrounding objects, thereby improving localization during hazardous material handling.
Amidi in view of Mallett in view of Johnson fails to teach a metal detector.
In the analogous art of hazardous material management, Morgan (US20130175373) teaches a metal detector (see [0009]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi in view of Mallett in view of Johnson by incorporating the metal detector as disclosed by Morgan with a reasonable expectation of success for the benefit of detecting metallic hazardous material not reliably detectable using other sensors, thereby increasing detection accuracy.
Amidi in view of Mallett in view of Johnson in view of Morgan fails to teach a fluorescence detector; a microphone; humidity sensor; a barometer; a thermometer and an accelerometer.
In the analogous art of hazardous material management, Kurani (US20210188541) teaches a fluorescence detector (fluorescence gas sensor in [0140]); a microphone (microphone in [0124]); and humidity sensor (humidity sensor in [0035]), a barometer (barometer in [0164]) and a thermometer (temperature sensor in [0186]) and an accelerometer (accelerometer sensor in [0026]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi in view of Mallett in view of Johnson in view of Morgan by incorporating the fluorescence detector as disclosed by Kurani with a reasonable expectation of success for the benefit of detecting hazardous material exhibiting fluorescence not reliably detectable using other sensors, thereby increasing detection accuracy.
Amidi in view of Mallett in view of Johnson in view of Morgan in view of Kurani fails to teach a gyroscope (see [0032]); a proximity sensor
In the analogous art of hazardous material management, Tomlin (US20160232498) teaches a gyroscope (gyroscope in [0032]); and a proximity sensor (depth sensor in [0032]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi in view of Mallett in view of Johnson in view of Morgan in view of Kurani by incorporating the gyroscope and the proximity sensor as disclosed by Tomlin with a reasonable expectation of success for the benefit of determining angular orientation and distance to nearly objects, thereby improving spatial localization of the hazardous materials.
Amidi in view of Mallett in view of Johnson in view of Morgan in view of Kurani in view of Tomlin fails to teach a gravity sensor.
In the analogous art of hazardous material management, Chen (CN 105574700 A) teaches a gravity sensor (gravity sensor on page 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi in view of Mallett in view of Johnson in view of Morgan in view of Kurani in view of Tomlin by incorporating the gravity sensor as disclosed by Chen with a reasonable expectation of success for the benefit of determining orientation relative to gravity field, thereby improving interpretation of the collected sensor data during hazardous material detection.
Amidi in view of Mallett in view of Johnson in view of Morgan in view of Kurani in view of Tomlin in view of Chen fails to teach a magnetometer.
In the analogous art of hazardous material management, Phillips (US 20060270421) teaches a magnetometer (see [0098]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as disclosed by Amidi in view of Mallett in view of Johnson in view of Morgan in view of Kurani in view of Tomlin in view of Chen by incorporating the magnetometer as disclosed by Phillips with a reasonable expectation of success for the benefit of providing heading and magnetic field information, thereby improving localization and navigation of the hazardous material detection including magnetic hazardous material.
With respect to claim 16, Amidi in view of Mallett teaches the system of claim 11, wherein the one or more sensors comprise a camera (video camera in [0065] of Mallett), an RFID detector (sorting station in [0025], which recites “the sorting station detects … such as … reading RFID tag”) a chemical detector (chemical sensors in [0104]), a light sensor (light detector 232 in [0175]) and a pressure sensor (pressure transducer in [0205]).
Amidi in view of Mallett fails to teach a light detection and ranging device, a metal detector, a fluorescence detector, a microphone, a thermometer, a proximity sensor, an accelerometer, a gyroscope, a gravity sensor, a magnetometer, a humidity sensor, and a barometer.
In the analogous art of hazardous material management, Johnson (US20200160682) teaches a light detection and ranging device (see [0026], which recites “a light detection and ranging (LIDAR) sensor”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system as disclosed by Amidi in view of Mallett by incorporating the light detection and ranging device as disclosed by Johnson with a reasonable expectation of success for the benefit of obtaining depth and distance data for hazardous material and surrounding objects, thereby improving localization during hazardous material handling.
Amidi in view of Mallett in view of Johnson fails to teach a metal detector.
In the analogous art of hazardous material management, Morgan (US20130175373) teaches a metal detector (see [0009]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system as disclosed by Amidi in view of Mallett in view of Johnson by incorporating the metal detector as disclosed by Morgan with a reasonable expectation of success for the benefit of detecting metallic hazardous material not reliably detectable using other sensors, thereby increasing detection accuracy.
Amidi in view of Mallett in view of Johnson in view of Morgan fails to teach a fluorescence detector; a microphone; humidity sensor; a barometer; a thermometer and an accelerometer.
In the analogous art of hazardous material management, Kurani (US20210188541) teaches a fluorescence detector (fluorescence gas sensor in [0140]); a microphone (microphone in [0124]); and humidity sensor (humidity sensor in [0035]), a barometer (barometer in [0164]) and a thermometer (temperature sensor in [0186]) and an accelerometer (accelerometer sensor in [0026]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system as disclosed by Amidi in view of Mallett in view of Johnson in view of Morgan by incorporating the fluorescence detector as disclosed by Kurani with a reasonable expectation of success for the benefit of detecting hazardous material exhibiting fluorescence not reliably detectable using other sensors, thereby increasing detection accuracy.
Amidi in view of Mallett in view of Johnson in view of Morgan in view of Kurani fails to teach a gyroscope (see [0032]); a proximity sensor
In the analogous art of hazardous material management, Tomlin (US20160232498) teaches a gyroscope (gyroscope in [0032]); a proximity sensor (depth sensor in [0032]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system as disclosed by Amidi in view of Mallett in view of Johnson in view of Morgan in view of Kurani by incorporating the gyroscope and the proximity sensor as disclosed by Tomlin with a reasonable expectation of success for the benefit of determining angular orientation and distance to nearly objects, thereby improving spatial localization of the hazardous materials.
Amidi in view of Mallett in view of Johnson in view of Morgan in view of Kurani in view of Tomlin fails to teach a gravity sensor.
In the analogous art of hazardous material management, Chen (CN 105574700 A) teaches a gravity sensor (gravity sensor on page 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system as disclosed by Amidi in view of Mallett in view of Johnson in view of Morgan in view of Kurani in view of Tomlin by incorporating the gravity sensor as disclosed by Chen with a reasonable expectation of success for the benefit of determining orientation relative to gravity field, thereby improving interpretation of the collected sensor data during hazardous material detection.
Amidi in view of Mallett in view of Johnson in view of Morgan in view of Kurani in view of Tomlin in view of Chen fails to teach a magnetometer.
In the analogous art of hazardous material management, Phillips (US 20060270421) teaches a magnetometer (see [0098]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system as disclosed by Amidi in view of Mallett in view of Johnson in view of Morgan in view of Kurani in view of Tomlin in view of Chen by incorporating the magnetometer as disclosed by Phillips with a reasonable expectation of success for the benefit of providing heading and magnetic field information, thereby improving localization and navigation of the hazardous material detection including magnetic hazardous material.
Conclusion
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/JONATHAN BORTOLI/Examiner, Art Unit 1797
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