DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after allowance or after an Office action under Ex Parte Quayle, 25 USPQ 74, 453 O.G. 213 (Comm'r Pat. 1935). Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant's submission filed on 06/09/2026 has been entered.
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 04/07/2026 and 04/21/2026 was filed after the mailing date of the Notice of Allowance on 03/10/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner’s Note
In response to the Request for Continued Examination, new rejections under 35 U.S.C. 112(b) are made with respect to Claims 1-8 and 9-21, a 35 U.S.C. 102(a)(1) rejection of Claims 1-2, 10-13, and 19 with respect to newly discovered prior art Popp (WO2019083556). A new grounds of rejection under 35 U.S.C. 103 is presented with respect to Claims 3-7, 9, and 20-21. Furthermore, a rejection under 35 U.S.C. 101 is presented for Claims 1-7, 9-13, and 19-20. Examiner notes that the Applicant’s arguments/Remarks made in the After Final action filed on 01/27/2026 detail that the amended limitation of “controls the manufacturing apparatus based on the analysis result” as integrates the judicial exception to a particular technological environment. Examiner notes that the amended limitation of control of the manufacturing apparatus is considered to be mere instructions to apply an exception, as the claim limitation only recites the idea of a solution/outcome (i.e. control of the manufacturing apparatus based on the analysis result) but does not recite any details to how the solution is accomplished. As recited in MPEP section 2106.05(f), The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8 and 9-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 details the limitation “acquires, from a first detector, a-posteriori information related to samples of a predetermined target that are produced under various conditions”. Claim 1 further details limitations of “the target”. It is not clear nor distinct whether the further limitations with regards to “the target” are that of the “predetermined target”, or of another “target”. Examiner interprets the target as “the predetermined target”.
Claims 4, 5, 7, 9, 14-18, and 20 detail “the target” as well.
Claims 2-7, 9-20 are rejected due to dependence on Claim 1.
Claim 21 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 21 details the limitation “related to samples of a target that are produced”. Claim 21 further details the limitation of “the a-posteriori information related to the predetermined target an a-priori information related to the target obtained during a production of the target”. It is not clear nor distinct whether “the predetermined target” is in reference to “the target” or it is in reference to some other “target”. Examiner interprets the predetermined target as “the target”.
Claims 1-7 and 9-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “a-posteriori information” in claims 1 and 21 is a relative term which renders the claim indefinite. The term “a-posteriori information” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. [0028] of the specification details that “Thus, it is possible to analyze the target using a-posteriori information being information that is difficult to handle deductively with human reasoning, in the manner described below, for example.”. That is, applicant defines “a-posteriori information” as “information that is difficult to handle deductively with human reasoning”. Thus, it is not clear nor distinct what the metes and bounds are for “a-posteriori information”. Examiner interprets the “a-posteriori information” as information acquired from a detector.
Claims 2-7 and 9-20 are rejected due to dependence on Claim 1.
Claim 21 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 21 details the limitation of “An information processing apparatus comprising a non- transitory medium storing a trained model that has been trained by machine learning in advance, using a-posteriori information related to samples of a target that are produced under various conditions and information related to a predetermined condition of the target as ground- truth data, and that outputs information related to whether the target meets a production specification, in response to an input of the a-posteriori information related to the predetermined target an a-priori information related to the target obtained during a production of the target, and
a hardware processor that controls the manufacturing apparatus based on the information output from the trained model.”
Breaking down this limitation based in order presented by the comma’s yields the following limitations:
a non- transitory medium storing a trained model that has been trained by machine learning in advance
using a-posteriori information related to samples of a target that are produced under various conditions and information related to a predetermined condition of the target as ground- truth data, and
that outputs information related to whether the target meets a production specification
in response to an input of the a-posteriori information related to the predetermined target an a-priori information related to the target obtained during a production of the target
a hardware processor that controls the manufacturing apparatus based on the information output from the trained model
Limitation (4) can be further broken down into (a) “an input of the a-posteriori information related to the predetermined target” and (b) “an a-priori information related to the target obtained during a production of the target”, and as limitation (4) details in response (a) (b), the sentence structure is not structurally sound to relay what the limitation is intended to limit as two separate concepts are conjoined together in a run-on structure with no delineation between them.
Limitations (2), (3), and (4) are providing further limitations to (1), but it is not clear what limitation (4) is modifying as there are multiple interpretation for what (4) is in respect to. Interpretations include: (4) is related to the “output information” of (3), (4) is related to the production specification of (3), (4) is related to the trained model of (1).
Examiner interpretations the limitation (4) as being in response to the output information of (3), and reading as “in response to an input of the a-posterior information related to the predetermined target and an a-priori information related to the target obtained during a production of the target”.
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 1-7, 9-13, and 19-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing abstract steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
1. (Currently Amended) An information processing apparatus comprising a hardware processor that:
acquires, from a first detector, a-posteriori information related to samples of a predetermined target that are produced under various conditions;
extracts a feature from the a-posteriori information that is suitable for determining whether the target satisfies a product specification;
acquires, from a second detector, a-priori information related to the target obtained during a production process of the target;
analyzes the target based on the feature and the a-priori information to generate an analysis result including a determination of whether the target meets a product specification, the analysis result being usable to control manufacturing conditions of a manufacturing apparatus; and
controls the manufacturing apparatus based on the analysis result.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea. Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because a step of acquiring information from a detector is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. acquiring information) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). The additional limitation of “controls the manufacturing apparatus based on the analysis result” is considered to be mere instructions to apply an exception, as the claim limitation only recites the idea of a solution/outcome (i.e. control of the apparatus based on the analysis result) but does not recite any details to how the solution is accomplished. As recited in MPEP section 2106.05(f), The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). The additional limitation of claim 1 of the hardware processor is interpreted under broadest reasonable interpretation to be a generic computer element. Generic computer elements are not considered significantly more than the abstract idea and do not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(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. 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.
Claims 2-7, 9-13, and 19 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
The additional limitation of Claim 20 of “the second detector includes a light-projector that emits excitation light at a predetermined wavelength to the target, and a light-receiver that receives fluorescence generated in the target by the excitation light” is considered to be necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. acquiring information) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Furthermore, light projectors and light emitters are well understood, routine, and conventional in the art as evidenced by Young (US20190072483) in [0060] and Horigome (US20190025209) in Figure 1.
The additional limitation of Claim 14 of “a sensitivity adjuster that performs predetermined processing to the target in order to adjust sensitivity of detection by the detection device” integrates the judicial exception into a practical application. Thus Claims 14-18 are not rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 10-13, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Popp (WO2019083556).
In regards to Claim 1, Popp teaches “acquires, from a first detector, a-posteriori information related to samples of a predetermined target that are produced under various conditions (“"Mobile Wireless Sensor Network" ("MWSN") means mobile spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, pH, concentration, DO, pressure, magnetic, moisture, flow, fluid velocity, density, temperature, heat, etc. and to cooperatively pass their data through the network to other locations. The more modern networks are bi-directional, also enabling control of sensor activity” – [0121]; “"sensor" means a data capture apparatus used by wireless sensor nodes to capture data from its environment. Sensors are hardware devices that produce a measurable response to a change in a physical condition like temperature or pressure.” – [0138]; “"sensor node" (a.k.a. mote) means a node in a sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network.” – [0139]; “In one embodiment, a MES of the present disclosure interfaces via a Soft PLC and/or a sensor note (a.k.a. a mote) to a sensor used to produce a measurable response to a change in physical condition in a pharmaceutical or biopharmaceutical manufacturing system, device or devices.” – [0245]; “Accordingly, sensors of the present invention comprise thermal, electromagnetic, mechanical, chemical, optical, radiation, acoustic, and biological sensors. In one embodiment, thermal sensors include but are not limited to thermometers, thermocouples, temperature sensitive resistors, bolometers, and calorimeter” – [0247]; “In a further embodiment, optical and radiation sensors include but are not limited to RADAR, LIDAR, dosimeters, particle detectors, scintillators, wire chambers, cloud chambers, bubble chambers, infrared sensors, photocells, photodiodes, phototransistors, image sensors; vacuum tube devices, and proximity sensors” – [0251]; “In a preferred embodiment, a mobile wireless sensor network ("MWSN") is interfaced to an MES of the invention. In this preferred embodiment, a MWSN is integrated with a pharmaceutical or biopharmaceutical hardware device or devices and placed to monitor a critical control point within a pharmaceutical or biopharmaceutical process. The data compiled from the sensor readings are transmitted to the MES and "cloud" based application servers and "cloud" based storage databases in real-time, near real-time, or live for analysis and analytics on the pharmaceutical or biopharmaceutical manufacturing process (See, section entitled "Data and Process Analytics").” – [0254]);
extracts a feature from the a-posteriori information that is suitable for determining whether the target satisfies a product specification (“"critical control point" means a function or an area in a manufacturing process or procedure, the failure of which, or loss of control over, may have an adverse affect on the quality of the finished product and may result in an unacceptable health risk.” – [0094]; “"product specification" means a statement (usually in documentary form) of how a drug or biologic is made. Specifications for active ingredients are essential for product quality and patient safety. The goal of a product specification is to determine appropriate and safe limits or quantitative ranges during drug or biologic production” – [0131]; “In a preferred embodiment, a mobile wireless sensor network ("MWSN") is interfaced to an MES of the invention. In this preferred embodiment, a MWSN is integrated with a pharmaceutical or biopharmaceutical hardware device or devices and placed to monitor a critical control point within a pharmaceutical or biopharmaceutical process. The data compiled from the sensor readings are transmitted to the MES and "cloud" based application servers and "cloud" based storage databases in real-time, near real-time, or live for analysis and analytics on the pharmaceutical or biopharmaceutical manufacturing process (See, section entitled "Data and Process Analytics").” – [0254]; “a) initiating activation of a software based programmable logic controller, whereby the software based programmable logic controller comprises an embedded system, wherein said embedded system is integrated into a pharmaceutical manufacturing hardware device adapted for use in manufacturing a pharmaceutical; b) compiling data generated during the pharmaceutical manufacturing, wherein the data relates to a pharmaceutical product specification;” – Claim 9);
acquires, from a second detector, a-priori information related to the target obtained during a production process of the target ([0121] details that the mobile wireless sensor network contains a plurality of sensors, with [0138]-[0139] defining the sensors; “In a preferred embodiment, a mobile wireless sensor network ("MWSN") is interfaced to an MES of the invention. In this preferred embodiment, a MWSN is integrated with a pharmaceutical or biopharmaceutical hardware device or devices and placed to monitor a critical control point within a pharmaceutical or biopharmaceutical process. The data compiled from the sensor readings are transmitted to the MES and "cloud" based application servers and "cloud" based storage databases in real-time, near real-time, or live for analysis and analytics on the pharmaceutical or biopharmaceutical manufacturing process (See, section entitled "Data and Process Analytics").” – [0254]);
analyzes the target based on the feature and the a-priori information to generate an analysis result including a determination of whether the target meets a product specification, the analysis result being usable to control manufacturing conditions of a manufacturing apparatus (“c) transmitting the data over a distributed control system whereby the data is analyzed against the product specification; d) taking corrective action on said pharmaceutical manufacturing process, where by the corrective action comprises stopping or modifying the pharmaceutical manufacturing process.” – Claim 9); and
controls the manufacturing apparatus based on the analysis result (“In a further embodiment, the invention provides for data analysis using failure modes and effects analysis. The analysis of actual or potential failure modes within a pharmaceutical or biopharmaceutical manufacturing system on a component-by-component and process-by-process level is analyzed for classification or determination of a failure upon the pharmaceutical or biopharmaceutical manufacturing system. Failures which cause any error or defects in a pharmaceutical or biopharmaceutical process, design, manufacture, or product are analyzed and corrective action is taken during pharmaceutical or biopharmaceutical manufacture. The corrective action of the invention comprises modifying or stopping pharmaceutical or biopharmaceutical manufacture to obviate a failure” – [0221]; “In a further embodiment, the invention provides for data analysis using hazard analysis and critical control points. The analysis occurs in a systematic preventive approach to pharmaceutical or biopharmaceutical manufacturing that addresses physical, chemical, and biological hazards of a drug or biologic as a means of prevention rather than finished drug product inspection. The analysis is used in pharmaceutical or biopharmaceutical manufacture to identify hazards, so that key actions and locations within a pharmaceutical or biopharmaceutical manufacturing process, known as critical control points can be taken to reduce or eliminate the risk of the hazards being realized. The analysis is used at all stages of pharmaceutical or biopharmaceutical production including powder blending processes, granulation processes, crystallization processes, tablet pressing, chromatography processing, pH measurement, liquid mixing processes, water-based processes, finishing and packaging processes, cell culture processes, filtration / purification processes, centrifugation processes, viral inactivation processes, field instrumentation component(s) operation and assessment and operational unit(s) operation and assessment(s). Failures which cause any error or defects in a pharmaceutical or biopharmaceutical process, design, manufacture, or product are analyzed and corrective action is taken during pharmaceutical or biopharmaceutical manufacture. The corrective action of the invention comprises initiating, modifying, halting, temporarily halting, or stopping pharmaceutical or biopharmaceutical manufacture to obviate a failure” – [0223]).”
In regards to Claim 2, Popp discloses the claimed invention as detailed above. Popp further teaches “wherein the hardware processor uses the a-priori information to extract the feature (“"critical control point" means a function or an area in a manufacturing process or procedure, the failure of which, or loss of control over, may have an adverse affect on the quality of the finished product and may result in an unacceptable health risk.” – [0094]; “"product specification" means a statement (usually in documentary form) of how a drug or biologic is made. Specifications for active ingredients are essential for product quality and patient safety. The goal of a product specification is to determine appropriate and safe limits or quantitative ranges during drug or biologic production” – [0131]; “In a preferred embodiment, a mobile wireless sensor network ("MWSN") is interfaced to an MES of the invention. In this preferred embodiment, a MWSN is integrated with a pharmaceutical or biopharmaceutical hardware device or devices and placed to monitor a critical control point within a pharmaceutical or biopharmaceutical process. The data compiled from the sensor readings are transmitted to the MES and "cloud" based application servers and "cloud" based storage databases in real-time, near real-time, or live for analysis and analytics on the pharmaceutical or biopharmaceutical manufacturing process (See, section entitled "Data and Process Analytics").” – [0254]; “a) initiating activation of a software based programmable logic controller, whereby the software based programmable logic controller comprises an embedded system, wherein said embedded system is integrated into a pharmaceutical manufacturing hardware device adapted for use in manufacturing a pharmaceutical; b) compiling data generated during the pharmaceutical manufacturing, wherein the data relates to a pharmaceutical product specification;” – Claim 9; As the compiled data is utilized all the detected data and the evaluation with the critical control point, the “a-priori” and “a-posteriori” information is utilized).
In regards to Claim 10, Popp discloses the claimed invention as detailed above. Popp further teaches “wherein the a-posteriori information includes information related to an electromagnetic wave spectrum (“In a further embodiment, optical and radiation sensors include but are not limited to RADAR, LIDAR, dosimeters, particle detectors, scintillators, wire chambers, cloud chambers, bubble chambers, infrared sensors, photocells, photodiodes, phototransistors, image sensors; vacuum tube devices, and proximity sensors” – [0251]).”
In regards to Claim 11, Popp discloses the claimed invention as detailed above. Popp further teaches “wherein the a-posteriori information includes information related to a continuous signal (“In certain embodiments, a cloud-controlled MES is integrated into a pharmaceutical or biopharmaceutical manufacturing system whereby the cloud-controlled MES controls the quality control function of the pharmaceutical or biopharmaceutical system and whereby the quality control function is continuously monitored and whereby the monitoring occurs at critical control points located throughout the pharmaceutical or biopharmaceutical system / process” – [0064]).”
In regards to Claim 12, Popp discloses the claimed invention as detailed above. Popp further teaches “wherein the information related to the continuous signal includes information related to a spectrum, a chromatogram, a pattern, or a map (Chromatography Processes/systems – [0278]).”
In regards to Claim 13, Popp discloses the claimed invention as detailed above. Popp further teaches “wherein the a-posteriori information includes information related to an impedance (“In a further embodiment, electromagnetic sensors include but are not limited to ohmmeters, multimeters, galvanometers, ammeters, leaf electroscopes, watt-hour meters, magnetic compasses, fluxgate compasses, magnetometers, and metal detectors” – [0248]).”
In regards to Claim 19, Popp discloses the claimed invention as detailed above in Claim 2. Popp further teaches “the first detector that detects the a-posteriori information from the predetermined target; and the second detector that detects the a-priori information from the predetermined target (“"Mobile Wireless Sensor Network" ("MWSN") means mobile spatially distributed autonomous sensors [i.e. first and second detectors] to monitor physical or environmental conditions, such as temperature, sound, pressure, pH, concentration, DO, pressure, magnetic, moisture, flow, fluid velocity, density, temperature, heat, etc. and to cooperatively pass their data through the network to other locations. The more modern networks are bi-directional, also enabling control of sensor activity” – [0121]; “"sensor" means a data capture apparatus used by wireless sensor nodes to capture data from its environment. Sensors are hardware devices that produce a measurable response to a change in a physical condition like temperature or pressure.” – [0138]; “"sensor node" (a.k.a. mote) means a node in a sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network.” – [0139]; “In one embodiment, a MES of the present disclosure interfaces via a Soft PLC and/or a sensor note (a.k.a. a mote) to a sensor used to produce a measurable response to a change in physical condition in a pharmaceutical or biopharmaceutical manufacturing system, device or devices.” – [0245]; “Accordingly, sensors of the present invention comprise thermal, electromagnetic, mechanical, chemical, optical, radiation, acoustic, and biological sensors. In one embodiment, thermal sensors include but are not limited to thermometers, thermocouples, temperature sensitive resistors, bolometers, and calorimeter” – [0247]; “In a further embodiment, optical and radiation sensors include but are not limited to RADAR, LIDAR, dosimeters, particle detectors, scintillators, wire chambers, cloud chambers, bubble chambers, infrared sensors, photocells, photodiodes, phototransistors, image sensors; vacuum tube devices, and proximity sensors” – [0251]; “In a preferred embodiment, a mobile wireless sensor network ("MWSN") is interfaced to an MES of the invention. In this preferred embodiment, a MWSN is integrated with a pharmaceutical or biopharmaceutical hardware device or devices and placed to monitor a critical control point within a pharmaceutical or biopharmaceutical process. The data compiled from the sensor readings are transmitted to the MES and "cloud" based application servers and "cloud" based storage databases in real-time, near real-time, or live for analysis and analytics on the pharmaceutical or biopharmaceutical manufacturing process (See, section entitled "Data and Process Analytics").” – [0254]).”
Claim Rejections - 35 USC § 103
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.
Claims 3-5 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Popp as applied to claim 1 above, and further in view of Osoekawa (US20200292509).
In regards to Claim 3, Popp discloses the claimed invention as detailed above in Claim 1. Popp is silent with regards to the language of “wherein the hardware processor extracts the feature using machine learning.”
Osoekawa teaches “wherein the hardware processor extracts the feature using machine learning (“A normal SSD method uses a feature map extracted by CNN in order to estimate a section within the two-dimensional image where an object is present. A variety of feature maps with various sizes (pixel numbers) are used by gradually convoluting the original feature map. By this technique, object-area candidates with various sizes can be detected. On the other hand, the object to be detected in the present embodiment is the positions of the starting and ending points of a peak in the x direction. Therefore, the algorithm has been modified so as to detect the presence of the starting and ending points of a peak within each of the sections which vary in size in the x direction” – [0067]; “FIG. 5 is a model diagram showing a trained model using a neural network, to be used in the present embodiment. FIG. 6 is a model diagram for explaining the process of creating a trained model. As shown in FIG. 6, in the present example, a segment having a window whose width equals the entire length in x the direction of the image created through the processing of Steps S 2 -S 4 is initially defined as “segment Sg 1 ”. The window of this segment Sg 1 is subsequently divided into two sections to define segments Sg 2 and Sg 3 whose window width is equal to one half of the first window. Then, each of the windows of segments Sg 2 and Sg 3 is similarly divided into two sections to define four segments Sg 4 , Sg 5 , Sg 6 and Sg 7 whose window width is equal to one quarter of the original window. By repeating such operations, a total of 120 segments Sg 1 -Sg 120 are defined. Each segment forms a unit for extracting a feature map by CNN. Based on the images prepared as the training data, the peak range defined by the starting and ending points of the peak is learned for each unit” – [0068]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Popp to incorporate the teaching of Osoekawa to utilize machine learning to extract features from the measured data. By utilizing machine learning to extract features from data, this is an improvement to the efficiency of the analysis of the data and reducing the need for human intervention thus improving the economic benefit.
In regards to Claim 4, Popp discloses the claimed invention as detailed above in Claim 1. Popp is silent with regards to the language of “wherein the hardware processor analyzes the target using machine learning.”
Osoekawa teaches “wherein the hardware processor analyzes the target using machine learning (“A normal SSD method uses a feature map extracted by CNN in order to estimate a section within the two-dimensional image where an object is present. A variety of feature maps with various sizes (pixel numbers) are used by gradually convoluting the original feature map. By this technique, object-area candidates with various sizes can be detected. On the other hand, the object to be detected in the present embodiment is the positions of the starting and ending points of a peak in the x direction. Therefore, the algorithm has been modified so as to detect the presence of the starting and ending points of a peak within each of the sections which vary in size in the x direction” – [0067]; “FIG. 5 is a model diagram showing a trained model using a neural network, to be used in the present embodiment. FIG. 6 is a model diagram for explaining the process of creating a trained model. As shown in FIG. 6, in the present example, a segment having a window whose width equals the entire length in x the direction of the image created through the processing of Steps S 2 -S 4 is initially defined as “segment Sg 1 ”. The window of this segment Sg 1 is subsequently divided into two sections to define segments Sg 2 and Sg 3 whose window width is equal to one half of the first window. Then, each of the windows of segments Sg 2 and Sg 3 is similarly divided into two sections to define four segments Sg 4 , Sg 5 , Sg 6 and Sg 7 whose window width is equal to one quarter of the original window. By repeating such operations, a total of 120 segments Sg 1 -Sg 120 are defined. Each segment forms a unit for extracting a feature map by CNN. Based on the images prepared as the training data, the peak range defined by the starting and ending points of the peak is learned for each unit” – [0068]; “The peak position estimator 113 applies the trained model stored in the trained model storage section 114 to the pixel values of the pixels of the created image, to obtain the five-dimensional information for each of the 120 segments. In other words, it obtains the information of the pixel positions which are estimated to be the starting and ending points of a peak in the image, along with the peak detection confidence (Step S 15 ).” – [0076]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Popp to incorporate the teaching of Osoekawa to utilize machine learning. By utilizing machine learning, this is an improvement to the efficiency of the analysis of the data and reducing the need for human intervention thus improving the economic benefit.
In regards to Claim 5, Popp discloses the claimed invention as detailed above in Claim 1. Popp is silent with regards to the language of “wherein the hardware processor includes a machine learning model to extract the feature and analyze the target.”
Osoekawa teaches “wherein the hardware processor includes a machine learning model to extract the feature and analyze the target (“A normal SSD method uses a feature map extracted by CNN in order to estimate a section within the two-dimensional image where an object is present. A variety of feature maps with various sizes (pixel numbers) are used by gradually convoluting the original feature map. By this technique, object-area candidates with various sizes can be detected. On the other hand, the object to be detected in the present embodiment is the positions of the starting and ending points of a peak in the x direction. Therefore, the algorithm has been modified so as to detect the presence of the starting and ending points of a peak within each of the sections which vary in size in the x direction” – [0067]; “FIG. 5 is a model diagram showing a trained model using a neural network, to be used in the present embodiment. FIG. 6 is a model diagram for explaining the process of creating a trained model. As shown in FIG. 6, in the present example, a segment having a window whose width equals the entire length in x the direction of the image created through the processing of Steps S 2 -S 4 is initially defined as “segment Sg 1 ”. The window of this segment Sg 1 is subsequently divided into two sections to define segments Sg 2 and Sg 3 whose window width is equal to one half of the first window. Then, each of the windows of segments Sg 2 and Sg 3 is similarly divided into two sections to define four segments Sg 4 , Sg 5 , Sg 6 and Sg 7 whose window width is equal to one quarter of the original window. By repeating such operations, a total of 120 segments Sg 1 -Sg 120 are defined. Each segment forms a unit for extracting a feature map by CNN. Based on the images prepared as the training data, the peak range defined by the starting and ending points of the peak is learned for each unit” – [0068]; “The peak position estimator 113 applies the trained model stored in the trained model storage section 114 to the pixel values of the pixels of the created image, to obtain the five-dimensional information for each of the 120 segments. In other words, it obtains the information of the pixel positions which are estimated to be the starting and ending points of a peak in the image, along with the peak detection confidence (Step S 15 ).” – [0076]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Popp to incorporate the teaching of Osoekawa to utilize machine learning. By utilizing machine learning, this is an improvement to the efficiency of the analysis of the data and reducing the need for human intervention thus improving the economic benefit.
In regards to Claim 9, Popp discloses the claimed invention as detailed above in Claim 1. Popp further teaches “wherein the hardware processor acquires a plurality of pieces of the a-priori information related to the target (“"Mobile Wireless Sensor Network") ("MWSN") means mobile spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, pH, concentration, DO, pressure, magnetic, moisture, flow, fluid velocity, density, temperature, heat, etc. and to cooperatively pass their data through the network to other locations. The more modern networks are bi-directional, also enabling control of sensor activity” – [0121]; “"sensor" means a data capture apparatus used by wireless sensor nodes to capture data from its environment. Sensors are hardware devices that produce a measurable response to a change in a physical condition like temperature or pressure.” – [0138]; “"sensor node" (a.k.a. mote) means a node in a sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network.” – [0139]; “In one embodiment, a MES of the present disclosure interfaces via a Soft PLC and/or a sensor note (a.k.a. a mote) to a sensor used to produce a measurable response to a change in physical condition in a pharmaceutical or biopharmaceutical manufacturing system, device or devices.” – [0245]; “Accordingly, sensors of the present invention comprise thermal, electromagnetic, mechanical, chemical, optical, radiation, acoustic, and biological sensors. In one embodiment, thermal sensors include but are not limited to thermometers, thermocouples, temperature sensitive resistors, bolometers, and calorimeter” – [0247]; “In a further embodiment, optical and radiation sensors include but are not limited to RADAR, LIDAR, dosimeters, particle detectors, scintillators, wire chambers, cloud chambers, bubble chambers, infrared sensors, photocells, photodiodes, phototransistors, image sensors; vacuum tube devices, and proximity sensors” – [0251]; “In a preferred embodiment, a mobile wireless sensor network ("MWSN") is interfaced to an MES of the invention. In this preferred embodiment, a MWSN is integrated with a pharmaceutical or biopharmaceutical hardware device or devices and placed to monitor a critical control point within a pharmaceutical or biopharmaceutical process. The data compiled from the sensor readings are transmitted to the MES and "cloud" based application servers and "cloud" based storage databases in real-time, near real-time, or live for analysis and analytics on the pharmaceutical or biopharmaceutical manufacturing process (See, section entitled "Data and Process Analytics").” – [0254]).”
Propp is silent with regards to the language of “analyzes the target using machine learning.”
Osoekawa teaches “wherein the hardware processor analyzes the target using machine learning (“A normal SSD method uses a feature map extracted by CNN in order to estimate a section within the two-dimensional image where an object is present. A variety of feature maps with various sizes (pixel numbers) are used by gradually convoluting the original feature map. By this technique, object-area candidates with various sizes can be detected. On the other hand, the object to be detected in the present embodiment is the positions of the starting and ending points of a peak in the x direction. Therefore, the algorithm has been modified so as to detect the presence of the starting and ending points of a peak within each of the sections which vary in size in the x direction” – [0067]; “FIG. 5 is a model diagram showing a trained model using a neural network, to be used in the present embodiment. FIG. 6 is a model diagram for explaining the process of creating a trained model. As shown in FIG. 6, in the present example, a segment having a window whose width equals the entire length in x the direction of the image created through the processing of Steps S 2 -S 4 is initially defined as “segment Sg 1 ”. The window of this segment Sg 1 is subsequently divided into two sections to define segments Sg 2 and Sg 3 whose window width is equal to one half of the first window. Then, each of the windows of segments Sg 2 and Sg 3 is similarly divided into two sections to define four segments Sg 4 , Sg 5 , Sg 6 and Sg 7 whose window width is equal to one quarter of the original window. By repeating such operations, a total of 120 segments Sg 1 -Sg 120 are defined. Each segment forms a unit for extracting a feature map by CNN. Based on the images prepared as the training data, the peak range defined by the starting and ending points of the peak is learned for each unit” – [0068]; “The peak position estimator 113 applies the trained model stored in the trained model storage section 114 to the pixel values of the pixels of the created image, to obtain the five-dimensional information for each of the 120 segments. In other words, it obtains the information of the pixel positions which are estimated to be the starting and ending points of a peak in the image, along with the peak detection confidence (Step S 15 ).” – [0076]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Popp to incorporate the teaching of Osoekawa to utilize machine learning. By utilizing machine learning, this is an improvement to the efficiency of the analysis of the data and reducing the need for human intervention thus improving the economic benefit.
Claims 6-7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Popp as applied to claims 1 and 19 above, and further in view of Young (US20190072483).
In regards to Claim 6, Popp discloses the claimed invention as detailed above in Claim 1. Popp is silent with regards to the language of “wherein the a-posteriori information includes information related to a fluorescent fingerprint.”
Young teaches “wherein the a-posteriori information includes information related to a fluorescent fingerprint (“Referring to FIG. 1, a real-time fluid analysis system ( 100 ) is shown. Fluid analysis system ( 100 ) may include an enclosure ( 300 ) having a cooling system ( 302 ) attached/coupled to a sampling system ( 304 ), and an analytical system ( 400 ) connected to the sampling system ( 304 ). Fluid may be routed out from a fluid source ( 200 ) and into cooling system ( 302 ) (shown via arrow, A) for cooling the fluid prior to routing the fluid into sampling system ( 304 ) (shown via arrow, A) for collecting real-time data from the fluid. In an exemplary embodiment, real-time data may include a “fingerprint” of a fluid sample obtained via spectroscopy” – [0032]; “In exemplary embodiments, accurate analysis may be performed and precise data obtained from fluid samples by performing electro-optical analysis on the fluids. Sub-sampling system ( 330 ) may utilize a spectral scanner/spectrometer/custom electro-optical system to instantaneously and continuously scan and inform a user of the molecular makeup and condition of any fluids such as for e.g. industrial oil and water. Different types of fluids/materials have their own “fingerprint” and the electro-optical system may read and analyze the differences between these materials. In exemplary embodiments, sub-sampling system ( 330 ) may be at least one of a Raman sub-sampling system ( 330 , 350 ) (e.g., see FIG. 9), a fluorescence sub-sampling system ( 330 , 352 ) (e.g., see FIG. 10), an absorbance sub-sampling system ( 330 , 354 ) (e.g., see FIG. 11), a Fourier Transform IR absorbance sub-sampling system ( 330 , 356 ) (e.g., see FIG. 12), and an absorbance-fluorescence-scatter sub-sampling system ( 330 , 358 ) (e.g., see FIG. 13). Each type of electro-optical analysis based sub-sampling system ( 330 ) may provide for different methods of analyzing the fluids by identifying different parts of the fluids.” – [0056]; “Fluorescence spectroscopy based systems utilize electromagnetic spectroscopy to analyze fluorescence from a sample. These systems may involve using a beam of light, usually ultraviolet light, that excites the electrons in molecules of certain compounds and causes them to emit light; typically, but not necessarily, visible light. Fluorescence sub-sampling/detection systems may generally require at least: an excitation light source, a fluorophore (fluorescent chemical compound that can re-emit light upon light excitation), wavelength filters to isolate emission photons from excitation photons, and a detector that registers emission photons and produces a recordable output, usually as an electrical signal” – [0078]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Popp to incorporate the teaching of Young to perform fluorescence spectroscopy to get a signature of the sample for analysis. By utilizing fluorescence analysis this is an improvement in the measurement and analysis of the product for evaluation thereof by increasing the efficiency of the operation to include the fluorescence spectroscopy.
In regards to Claim 7, Popp in view of Young discloses the claimed invention as detailed above. Popp further teaches “wherein the target is a product (product – [0009]).
In regards to Claim 20, Popp discloses the claimed invention as detailed above in Claim 19. Popp is silent with regards to the language of “wherein the first detector detects information related to a fluorescent fingerprint acquired from the target, and the second detector includes a light-projector that emits excitation light at a predetermined wavelength to the target, and a light-receiver that receives fluorescence generated in the target by the excitation light.”
Young teaches “wherein the first detector detects information related to a fluorescent fingerprint acquired from the target (“Referring to FIG. 1, a real-time fluid analysis system ( 100 ) is shown. Fluid analysis system ( 100 ) may include an enclosure ( 300 ) having a cooling system ( 302 ) attached/coupled to a sampling system ( 304 ), and an analytical system ( 400 ) connected to the sampling system ( 304 ). Fluid may be routed out from a fluid source ( 200 ) and into cooling system ( 302 ) (shown via arrow, A) for cooling the fluid prior to routing the fluid into sampling system ( 304 ) (shown via arrow, A) for collecting real-time data from the fluid. In an exemplary embodiment, real-time data may include a “fingerprint” of a fluid sample obtained via spectroscopy” – [0032]; “In exemplary embodiments, accurate analysis may be performed and precise data obtained from fluid samples by performing electro-optical analysis on the fluids. Sub-sampling system ( 330 ) may utilize a spectral scanner/spectrometer/custom electro-optical system to instantaneously and continuously scan and inform a user of the molecular makeup and condition of any fluids such as for e.g. industrial oil and water. Different types of fluids/materials have their own “fingerprint” and the electro-optical system may read and analyze the differences between these materials. In exemplary embodiments, sub-sampling system ( 330 ) may be at least one of a Raman sub-sampling system ( 330 , 350 ) (e.g., see FIG. 9), a fluorescence sub-sampling system ( 330 , 352 ) (e.g., see FIG. 10), an absorbance sub-sampling system ( 330 , 354 ) (e.g., see FIG. 11), a Fourier Transform IR absorbance sub-sampling system ( 330 , 356 ) (e.g., see FIG. 12), and an absorbance-fluorescence-scatter sub-sampling system ( 330 , 358 ) (e.g., see FIG. 13). Each type of electro-optical analysis based sub-sampling system ( 330 ) may provide for different methods of analyzing the fluids by identifying different parts of the fluids.” – [0056]; “Fluorescence spectroscopy based systems utilize electromagnetic spectroscopy to analyze fluorescence from a sample. These systems may involve using a beam of light, usually ultraviolet light, that excites the electrons in molecules of certain compounds and causes them to emit light; typically, but not necessarily, visible light. Fluorescence sub-sampling/detection systems may generally require at least: an excitation light source, a fluorophore (fluorescent chemical compound that can re-emit light upon light excitation), wavelength filters to isolate emission photons from excitation photons, and a detector that registers emission photons and produces a recordable output, usually as an electrical signal” – [0078]), and the second detector includes a light-projector that emits excitation light at a predetermined wavelength to the target, and a light-receiver that receives fluorescence generated in the target by the excitation light (“Excitation source ( 344 ) and detection system ( 346 ) may be used in tandem to perform fluid analysis (e.g., see FIG. 8). Detection system ( 346 ) may act as electro-optical “eyes” for a given excitation source ( 344 ). Controller ( 332 ) may inform the detection system ( 346 ) to prepare for sampling, after which it may inform the excitation source ( 344 ) to “fire” electromagnetic radiation into the fluid sample, and the detection system ( 346 ) may then register the results of this “firing”. In exemplary embodiments, this “firing” may be milliseconds to seconds long depending on the excitation source used and the type of detection required. In particular embodiments, excitation source ( 344 ) may be an LED source (specific chromatic source, mono chromatic, UV), IR/NIR (infrared/near-infrared) source, and/or wavelength stabilized laser (specific wavelength laser for excitation). In various embodiments, detection system ( 346 ) may be a type of charge-coupled device (CCD) (that may simply report direct data without a spectrometer for filtering), a set of photodiodes with a matching set of spectral filters (looking for specific wavelengths), and/or a spectrometer coupled to a thermally controlled CCD that may detect multiple sources coupled to the spectrometer for filtering.” – [0060]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Popp to incorporate the teaching of Young to perform fluorescence spectroscopy to get a signature of the sample for analysis. By utilizing fluorescence analysis this is an improvement in the measurement and analysis of the product for evaluation thereof by increasing the efficiency of the operation to include the fluorescence spectroscopy.
Claim 21 rejected under 35 U.S.C. 103 as being unpatentable over Popp in view of Osoekawa.
In regards to Claim 21, Popp teaches “using a-posteriori information related to samples of a target that are produced under various conditions and information related to a predetermined condition of the target as ground-truth data (“"Mobile Wireless Sensor Network") ("MWSN") means mobile spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, pH, concentration, DO, pressure, magnetic, moisture, flow, fluid velocity, density, temperature, heat, etc. and to cooperatively pass their data through the network to other locations. The more modern networks are bi-directional, also enabling control of sensor activity” – [0121]; “"sensor" means a data capture apparatus used by wireless sensor nodes to capture data from its environment. Sensors are hardware devices that produce a measurable response to a change in a physical condition like temperature or pressure.” – [0138]; “"sensor node" (a.k.a. mote) means a node in a sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network.” – [0139]; “In one embodiment, a MES of the present disclosure interfaces via a Soft PLC and/or a sensor note (a.k.a. a mote) to a sensor used to produce a measurable response to a change in physical condition in a pharmaceutical or biopharmaceutical manufacturing system, device or devices.” – [0245]; “Accordingly, sensors of the present invention comprise thermal, electromagnetic, mechanical, chemical, optical, radiation, acoustic, and biological sensors. In one embodiment, thermal sensors include but are not limited to thermometers, thermocouples, temperature sensitive resistors, bolometers, and calorimeter” – [0247]; “In a further embodiment, optical and radiation sensors include but are not limited to RADAR, LIDAR, dosimeters, particle detectors, scintillators, wire chambers, cloud chambers, bubble chambers, infrared sensors, photocells, photodiodes, phototransistors, image sensors; vacuum tube devices, and proximity sensors” – [0251]; “In a preferred embodiment, a mobile wireless sensor network ("MWSN") is interfaced to an MES of the invention. In this preferred embodiment, a MWSN is integrated with a pharmaceutical or biopharmaceutical hardware device or devices and placed to monitor a critical control point within a pharmaceutical or biopharmaceutical process. The data compiled from the sensor readings are transmitted to the MES and "cloud" based application servers and "cloud" based storage databases in real-time, near real-time, or live for analysis and analytics on the pharmaceutical or biopharmaceutical manufacturing process (See, section entitled "Data and Process Analytics").” – [0254]), and that outputs information related to whether the target meets a production specification (“"critical control point" means a function or an area in a manufacturing process or procedure, the failure of which, or loss of control over, may have an adverse affect on the quality of the finished product and may result in an unacceptable health risk.” – [0094]; “"product specification" means a statement (usually in documentary form) of how a drug or biologic is made. Specifications for active ingredients are essential for product quality and patient safety. The goal of a product specification is to determine appropriate and safe limits or quantitative ranges during drug or biologic production” – [0131]; “In a preferred embodiment, a mobile wireless sensor network ("MWSN") is interfaced to an MES of the invention. In this preferred embodiment, a MWSN is integrated with a pharmaceutical or biopharmaceutical hardware device or devices and placed to monitor a critical control point within a pharmaceutical or biopharmaceutical process. The data compiled from the sensor readings are transmitted to the MES and "cloud" based application servers and "cloud" based storage databases in real-time, near real-time, or live for analysis and analytics on the pharmaceutical or biopharmaceutical manufacturing process (See, section entitled "Data and Process Analytics").” – [0254]; “a) initiating activation of a software based programmable logic controller, whereby the software based programmable logic controller comprises an embedded system, wherein said embedded system is integrated into a pharmaceutical manufacturing hardware device adapted for use in manufacturing a pharmaceutical; b) compiling data generated during the pharmaceutical manufacturing, wherein the data relates to a pharmaceutical product specification; “c) transmitting the data over a distributed control system whereby the data is analyzed against the product specification; d) taking corrective action on said pharmaceutical manufacturing process, where by the corrective action comprises stopping or modifying the pharmaceutical manufacturing process.” – Claim 9), in response to an input of the a-posteriori information related to the predetermined target an a-priori information related to the target obtained during a production of the target ([0121] details that the mobile wireless sensor network contains a plurality of sensors, with [0138]-[0139] defining the sensors; “In a preferred embodiment, a mobile wireless sensor network ("MWSN") is interfaced to an MES of the invention. In this preferred embodiment, a MWSN is integrated with a pharmaceutical or biopharmaceutical hardware device or devices and placed to monitor a critical control point within a pharmaceutical or biopharmaceutical process. The data compiled from the sensor readings are transmitted to the MES and "cloud" based application servers and "cloud" based storage databases in real-time, near real-time, or live for analysis and analytics on the pharmaceutical or biopharmaceutical manufacturing process (See, section entitled "Data and Process Analytics").” – [0254]), and
a hardware processor that controls the manufacturing apparatus based on the information output from the trained model (“In a further embodiment, the invention provides for data analysis using failure modes and effects analysis. The analysis of actual or potential failure modes within a pharmaceutical or biopharmaceutical manufacturing system on a component-by-component and process-by-process level is analyzed for classification or determination of a failure upon the pharmaceutical or biopharmaceutical manufacturing system. Failures which cause any error or defects in a pharmaceutical or biopharmaceutical process, design, manufacture, or product are analyzed and corrective action is taken during pharmaceutical or biopharmaceutical manufacture. The corrective action of the invention comprises modifying or stopping pharmaceutical or biopharmaceutical manufacture to obviate a failure” – [0221]; “In a further embodiment, the invention provides for data analysis using hazard analysis and critical control points. The analysis occurs in a systematic preventive approach to pharmaceutical or biopharmaceutical manufacturing that addresses physical, chemical, and biological hazards of a drug or biologic as a means of prevention rather than finished drug product inspection. The analysis is used in pharmaceutical or biopharmaceutical manufacture to identify hazards, so that key actions and locations within a pharmaceutical or biopharmaceutical manufacturing process, known as critical control points can be taken to reduce or eliminate the risk of the hazards being realized. The analysis is used at all stages of pharmaceutical or biopharmaceutical production including powder blending processes, granulation processes, crystallization processes, tablet pressing, chromatography processing, pH measurement, liquid mixing processes, water-based processes, finishing and packaging processes, cell culture processes, filtration / purification processes, centrifugation processes, viral inactivation processes, field instrumentation component(s) operation and assessment and operational unit(s) operation and assessment(s). Failures which cause any error or defects in a pharmaceutical or biopharmaceutical process, design, manufacture, or product are analyzed and corrective action is taken during pharmaceutical or biopharmaceutical manufacture. The corrective action of the invention comprises initiating, modifying, halting, temporarily halting, or stopping pharmaceutical or biopharmaceutical manufacture to obviate a failure” – [0223]).
Popp is silent with regards to the language of “a non- transitory medium storing a trained model that has been trained by machine learning in advance.”
Osoekawa teaches “a non- transitory medium storing a trained model that has been trained by machine learning in advance (“In FIG. 1, the model-creating unit 2 , which is separately provided from the LC system 1 , includes a training data input section 20 , image creator 21 , training executor 22 , and model constructor 23 as its functional blocks. A trained model created in this model-creating unit 2 is stored in a storage unit in the data-analyzer unit 11 of the LC system 1 and functions as the trained model storage section 114” – [0053]; “A normal SSD method uses a feature map extracted by CNN in order to estimate a section within the two-dimensional image where an object is present. A variety of feature maps with various sizes (pixel numbers) are used by gradually convoluting the original feature map. By this technique, object-area candidates with various sizes can be detected. On the other hand, the object to be detected in the present embodiment is the positions of the starting and ending points of a peak in the x direction. Therefore, the algorithm has been modified so as to detect the presence of the starting and ending points of a peak within each of the sections which vary in size in the x direction” – [0067]; “FIG. 5 is a model diagram showing a trained model using a neural network, to be used in the present embodiment. FIG. 6 is a model diagram for explaining the process of creating a trained model. As shown in FIG. 6, in the present example, a segment having a window whose width equals the entire length in x the direction of the image created through the processing of Steps S 2 -S 4 is initially defined as “segment Sg 1 ”. The window of this segment Sg 1 is subsequently divided into two sections to define segments Sg 2 and Sg 3 whose window width is equal to one half of the first window. Then, each of the windows of segments Sg 2 and Sg 3 is similarly divided into two sections to define four segments Sg 4 , Sg 5 , Sg 6 and Sg 7 whose window width is equal to one quarter of the original window. By repeating such operations, a total of 120 segments Sg 1 -Sg 120 are defined. Each segment forms a unit for extracting a feature map by CNN. Based on the images prepared as the training data, the peak range defined by the starting and ending points of the peak is learned for each unit” – [0068])”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Popp to incorporate the teaching of Osoekawa to utilize machine learning. By utilizing machine learning, this is an improvement to the efficiency of the analysis of the data and reducing the need for human intervention thus improving the economic benefit.
Allowable Subject Matter
Claims 14-18 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
In regards to Claim 14, Popp is silent with regards to the language of “a sensitivity adjuster that performs predetermined processing to the target in order to adjust sensitivity of detection by the detection device. “
Claims 15-18 are dependent on Claim 14.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOSSEF KORANG-BEHESHTI whose telephone number is (571)272-3291. The examiner can normally be reached Monday - Friday 10:00 am - 6:30 pm.
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/YOSSEF KORANG-BEHESHTI/Primary Examiner, Art Unit 2857
