DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is in response to Application 19/288563 filed on August 1, 2025 in which Claims 1-20 are presented for examination.
Status of Claims
Claims 1-20 are pending, of which claims 1-20 are rejected under Double Patenting. Claims 2, 10, 13, 14, 18 and 20 are rejected under 112b. Claim 12 is rejected under 103. Claims 1-11 and 13-20 do not have a prior art rejection.
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 2, 10, 13, 14, 18 and 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 2 recites the limitation "the data processing device" in Line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation "the machine learning process" in Line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 10 recites the limitation "the functional modules" in Line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites the limitation "the application software update" in Line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites the limitation "the machine learning process" in Line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation "the machine learning process" in Line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 18 recites the limitation "the application software update" in Line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 18 recites the limitation "the application software update" in Line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 20 recites the limitation "the machine learning process" in Line 3-4. There is insufficient antecedent basis for this limitation in the claim.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1- 20 of the instant application are rejected on the ground of anticipatory-type nonstatutory double patenting as being unpatentable over Claims 1-20 of U.S. Patent No. US 12,443,476. Although the claims at issue are not identical, they are not patentably distinct from each other because the aforementioned claims of the instant application are rejected based on anticipatory-type double patenting with regards to the aforementioned parent patent.
The following table summarizes claim mappings associated with the obviousness-type double patenting rejections:
19/288563
18/123789 (12,443,476)
1. A method for detecting and reporting an operation error in an in-vitro diagnostic system configured to determine a sample of a bodily fluid, the method comprising: providing a plurality of sample vessels, each sample vessel of the plurality of sample vessels containing a sample of a bodily fluid; providing a plurality of functional modules comprising an analysis device configured to determine the sample, a handling system configured to handle the plurality of sample vessels, and an automation track provided by the handling system and configured to transport the plurality of sample vessels to the analysis device; providing an operation control device connected to at least one functional module of the plurality of functional modules and configured to control operation of the at least one functional module, wherein the operation control device comprises one or more data processors, wherein an application software is running on the one or more data processors for controlling operation of the at least one functional module; controlling operation of the at least one functional module by the operation control device; and detecting and reporting an operation error by an error detecting and reporting device by: detecting the operation error for the operation of the at least one functional module and the operation control device, providing error data indicative of the operation error, receiving a user input through a user interface after detecting the operation error, providing labelling data in response to receiving the user input, the labelling data being indicative of information related to the operation error in addition to the error data, providing error report data comprising the error data and the labelling data, and transmitting the error report data to an error repository remotely located with respect to both the plurality of functional modules and the operation control device; receiving the error report data in a machine learning process running in a data processing device connected to the error repository; processing the error report data by the machine learning process in the data processing device; providing an application software update for the application software in response to the processing of the error report data by the machine learning process in the data processing device; providing the application software update to the operation control device; and controlling operation of the at least one functional module by the operation control device by running the application software including the application software update.
1. A method for detecting and reporting an operation error in an in-vitro diagnostic system configured to determine a sample of a bodily fluid, the method comprising: providing a plurality of sample vessels, each sample vessel of the plurality of sample vessels containing a sample of a bodily fluid; providing a plurality of functional modules comprising an analysis device configured to determine the sample, a handling system configured to handle the plurality of sample vessels, and an automation track provided by the handling system and configured to transport the plurality of sample vessels to the analysis device; providing an operation control device connected to at least one functional module of the plurality of functional modules and configured to control operation of the at least one functional module, wherein the operation control device comprises one or more data processors, wherein an application software is running on the one or more data processors for controlling operation of the at least one functional module; controlling operation of the at least one functional module by the operation control device; and detecting and reporting an operation error by an error detecting and reporting device by: detecting the operation error for the operation of the at least one functional module and the operation control device, providing error data indicative of the operation error, receiving a user input through a user interface after detecting the operation error, providing labelling data in response to receiving the user input, the labelling data being indicative of information related to the operation error in addition to the error data, providing error report data comprising the error data and the labelling data, and transmitting the error report data to an error repository remotely located with respect to both the plurality of functional modules and the operation control device; receiving the error report data in a machine learning process running in a data processing device connected to the error repository; processing the error report data by the machine learning process in the data processing device; providing an application software update for the application software in response to the processing of the error report data by the machine learning process in the data processing device; providing the application software update to the operation control device; and controlling operation of the at least one functional module by the operation control device by running the application software including the application software update.
2. The method of claim 1, further comprising preprocessing the error report data in the data processing device, wherein preprocessing the error report data comprises formatting the error report data to formatted error report data having a data format processable by the machine learning process.
2. The method of claim 1, further comprising preprocessing the error report data in the data processing device, wherein preprocessing the error report data comprises formatting the error report data to formatted error report data having a data format processable by the machine learning process.
3. The method of claim 1, wherein the error data are indicative of an operation error of the analysis device.
5. The method of claim 1, wherein the error data are indicative of an operation error of the analysis device.
4. The method of claim 1, wherein the error data are indicative of an operation error of the handling system.
6. The method of claim 1, wherein the error data are indicative of an operation error of the handling system.
5. The method of claim 1, wherein the error data are indicative of an operation error of the automation track.
7. The method of claim 1, wherein the error data are indicative of an operation error of the automation track.
6. The method of claim 1, wherein the error data are indicative of one of a false positive error and a false negative error.
8. The method of claim 1, wherein the error data are indicative of one of a false positive error and a false negative error.
7. The method of claim 1, wherein receiving the user input further comprises: providing user information data indicative of a plurality of types of operation errors; outputting the user information data through a display device, thereby presenting a menu of the plurality of types of operation errors on the display device; receiving the user input through the user interface, the user input being indicative of a user selection of at least one of the plurality of types of operation errors; and providing labelling data assigned to the at least one of the plurality of types of operation errors selected by the user.
9. The method of claim 1, wherein receiving the user input further comprises: providing user information data indicative of a plurality of types of operation errors; outputting the user information data through a display device, thereby presenting a menu of the plurality of types of operation errors on the display device; receiving the user input through the user interface, the user input being indicative of a user selection of at least one of the plurality of types of operation errors; and providing labelling data assigned to the at least one of the plurality of types of operation errors selected by the user.
8. The method of claim 9, wherein providing the user information data comprises: receiving reading data from a data carrier reading device, the reading data being indicative of sample vessel data stored in a data carrier provided on the sample vessel; and providing the user information data in response to receiving the reading data.
10. The method of claim 9, wherein providing the user information data comprises: receiving reading data from a data carrier reading device, the reading data being indicative of sample vessel data stored in a data carrier provided on the sample vessel; and providing the user information data in response to receiving the reading data.
9. The method of claim 9, wherein providing the user information data comprises: receiving user message data from an input device, the user message data being indicative of at least one of a user video message input and a user audio message input; and providing the user information data in response to receiving the user message data.
11. The method of claim 9, wherein providing the user information data comprises: receiving user message data from an input device, the user message data being indicative of at least one of a user video message input and a user audio message input; and providing the user information data in response to receiving the user message data.
10. The method of claim 9, wherein providing the user information data comprises: outputting a visual representation of one of the functional modules from the plurality of functional modules and the operation control device through the display device; and receiving a user selection input indicative of the user selecting the visual representation.
12. The method of claim 9, wherein providing the user information data comprises: outputting a visual representation of one of the functional modules from the plurality of functional modules and the operation control device through the display device; and receiving a user selection input indicative of the user selecting the visual representation.
11. The method of claim 9, further comprising: receiving a user error report message within the receiving of the user input; generating user error report message data indicative of the user error report message; and providing the labelling data including the user error report message data.
13. The method of claim 9, further comprising: receiving a user error report message within the receiving of the user input; generating user error report message data indicative of the user error report message; and providing the labelling data including the user error report message data.
12. An in-vitro diagnostic system for determining a sample of a bodily fluid, the in-vitro diagnostic system comprising: a plurality of sample vessels each sample vessel of the plurality of sample vessels containing a sample of a bodily fluid; a plurality of functional modules, comprising: an analysis device configured to determine the sample, a handling system configured to handle the plurality of sample vessels, and an automation track provided by the handling system and configured to transport the plurality of sample vessels to the analysis device; an operation control device that is connected to at least one functional module of the plurality of functional modules and configured to control operation of the at least one functional module, wherein the operational control device comprises one or more data processors that execute an application software for controlling operation of the at least one functional module; an error detecting and reporting device configured to detect and report an operation error in the in-vitro diagnostic system, wherein to detect and report the operation error comprises to: detect the operation error for the operation of at least one of the plurality of functional modules and the operation control device, provide error data indicative of the operation error, receive a user input through a user interface after detecting the operation error, provide labelling data in response to receiving the user input, the labelling data being indicative of information related to the operation error in addition to the error data, provide error report data comprising the error data and the labelling data, and transmit the error report data to an error repository remotely located with respect to both the plurality of functional modules and the operation control device; and a data processing device connected to the error repository and configured to: receive the error report data in a machine learning process running in the data processing device; process the error report data by the machine learning process; and provide an application software update for the application software in response to processing the error report data by the machine learning process, wherein the application software update is to be provided to the operation control device for controlling operation of the at least one functional module by the operation control device by running the application software including the application software update.
20. An in-vitro diagnostic system configured to determine a sample of a bodily fluid, the in-vitro diagnostic system comprising: a plurality of sample vessels each sample vessel of the plurality of sample vessels containing a sample of a bodily fluid; a plurality of functional modules, comprising: an analysis device configured to determine the sample, a handling system configured to handle the plurality of sample vessels, and an automation track provided by the handling system and configured to transport the plurality of sample vessels to the analysis device; an operation control device that is connected to at least one functional module of the plurality of functional modules and configured to control operation of the at least one functional module, wherein the operational control device comprises one or more data processors that execute an application software for controlling operation of the at least one functional module; an error detecting and reporting device configured to detect and report an operation error in the in-vitro diagnostic system, wherein to detect and report the operation error comprises to: detect the operation error for the operation of at least one of the plurality of functional modules and the operation control device, provide error data indicative of the operation error, receive a user input through a user interface after detecting the operation error, provide labelling data in response to receiving the user input, the labelling data being indicative of information related to the operation error in addition to the error data, provide error report data comprising the error data and the labelling data, and transmit the error report data to an error repository remotely located with respect to both the plurality of functional modules and the operation control device; and a data processing device connected to the error repository and configured to: receive the error report data in a machine learning process running in the data processing device; process the error report data by the machine learning process; and provide an application software update for the application software in response to processing the error report data by the machine learning process, wherein the application software update is to be provided to the operation control device for controlling operation of the at least one functional module by the operation control device by running the application software including the application software update.
13. The method of claim 1, wherein the application software update for the application software is provided by the machine learning process.
14. The method of claim 1, wherein the application software update for the application software is provided by the machine learning process.
14. The method of claim 1, wherein the machine learning process identifies at least one systematical error in the in-vitro diagnostic system.
15. The method of claim 1, wherein the machine learning process identifies at least one systematical error in the in-vitro diagnostic system.
15. The method of claim 1, wherein the operation error relates to an amount or type of the bodily fluid detected in a sample container.
16. The method of claim 1, wherein the operation error relates to an amount or type of the bodily fluid detected in a sample container.
16. The method of claim 1, wherein the operation error relates to an identification of a sample container including the bodily fluid.
17. The method of claim 1, wherein the operation error relates to an identification of a sample container including the bodily fluid.
17. The method of claim 1, wherein the operation error relates to a determination of a quality of the bodily fluid in a sample container.
18. The method of claim 1, wherein the operation error relates to a determination of a quality of the bodily fluid in a sample container.
18. The method of claim 1, further comprising overwriting at least a portion of the application software with the application software update, resulting in a new application software; and wherein controlling the operation of the at least one functional module by the operation control device by running the application software including the application software update comprises executing the new application software.
19. The method of claim 1, further comprising overwriting at least a portion of the application software with the application software update, resulting in a new application software; and wherein controlling the operation of the at least one functional module by the operation control device by running the application software including the application software update comprises executing the new application software.
19. The method of claim 2, wherein the formatted error report data include a feature vector of features, wherein the features are indicative of operation parameters of the in-vitro diagnostic system.
3. The method of claim 2, wherein the formatted error report data include a feature vector of features, wherein the features are indicative of operation parameters of the in-vitro diagnostic system.
20. The method of claim 3, wherein the labelling data are formatted such that a target is provided that is linked to the error data; and wherein the target and the feature vector are provided as inputs to the machine learning process.
4. The method of claim 3, wherein the labelling data are formatted such that a target is provided that is linked to the error data; and wherein the target and the feature vector are provided as inputs to the machine learning process.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Friedlander (US Patent Application 2013/0311834) in view of Jain (US Patent 11,687,437) in view of Ricken (US Patent Application 2015/0227409) in view of Bartley (US Patent Application 2015/0347212) and further in view of Zhang (US Patent Application 2009/0006883).
Claim 12, Friedlander teaches an in-vitro diagnostic system for determining a sample of a bodily fluid (View Friedlander ¶ 33; lab result analysis), the in-vitro diagnostic system comprising: a plurality of sample vessels each sample vessel of the plurality of sample vessels containing a sample of a bodily fluid (View Friedlander ¶ 33; blood/stool sample); a plurality of functional modules, comprising: an analysis device configured to determine the sample (View Friedlander ¶ 33; lab result analysis).
Friedlander does not explicitly teach a handling system configured to handle the plurality of sample vessels, and an automation track provided by the handling system and configured to transport the plurality of sample vessels to the analysis device; an operation control device that is connected to at least one functional module of the plurality of functional modules and configured to control operation of the at least one functional module, wherein the operational control device comprises one or more data processors that execute an application software for controlling operation of the at least one functional module; an error detecting and reporting device configured to detect and report an operation error, wherein to detect and report the operation error comprises to: detect the operation error for the operation of at least one of the plurality of functional modules and the operation control device, provide error data indicative of the operation error, receive a user input through a user interface after detecting the operation error, provide labelling data in response to receiving the user input, the labelling data being indicative of information related to the operation error in addition to the error data, provide error report data comprising the error data and the labelling data, and transmit the error report data to an error repository remotely located with respect to both the plurality of functional modules and the operation control device; and a data processing device connected to the error repository and configured to: receive the error report data in a machine learning process running in the data processing device; process the error report data by the machine learning process; and provide an application software update for the application software in response to processing the error report data by the machine learning process, wherein the application software update is to be provided to the operation control device for controlling operation of the at least one functional module by the operation control device by running the application software including the application software update.
However, Jain teaches a handling system configured to handle the plurality of sample vessels (View Jain Col. 101, Lines 22-31; monitor samples), and an automation track provided by the handling system and configured to transport the plurality of sample vessels to the analysis device (View Jain Col. 7, Lines 58-65; Col. 71, Lines 65-67; data transmission); an operation control device that is connected to at least one functional module of the plurality of functional modules and configured to control operation of the at least one functional module (View Jain Col. 11, Lines 38-58; computer system), wherein the operational control device comprises one or more data processors that execute an application software for controlling operation of the at least one functional module (View Jain Col. 121, Lines 7-33; processor).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Friedlander with a handling system configured to handle the plurality of sample vessels, and an automation track provided by the handling system and configured to transport the plurality of sample vessels to the analysis device; an operation control device that is connected to at least one functional module of the plurality of functional modules and configured to control operation of the at least one functional module, wherein the operational control device comprises one or more data processors that execute an application software for controlling operation of the at least one functional module since it is known in the art that an error report can be transmitted (View Jain Col. 7, Lines 58-65; Col. 71, Lines 65-67; Col. 101, Lines 22-31). Such modification would have allowed samples to be transmitted.
Friedlander and Jain do not explicitly teach an error detecting and reporting device configured to detect and report an operation error, wherein to detect and report the operation error comprises to: detect the operation error for the operation of at least one of the plurality of functional modules and the operation control device, provide error data indicative of the operation error, receive a user input through a user interface after detecting the operation error, provide labelling data in response to receiving the user input, the labelling data being indicative of information related to the operation error in addition to the error data, provide error report data comprising the error data and the labelling data, and transmit the error report data to an error repository remotely located with respect to both the plurality of functional modules and the operation control device.
However, Ricken teaches an error detecting and reporting device configured to detect and report an operation error (View Ricken ¶ 5, 28; anomaly detection, error report), wherein to detect and report the operation error comprises to: detect the operation error for the operation of at least one of the plurality of functional modules and the operation control device (View Ricken ¶ 28; detect anomaly), provide error data indicative of the operation error (View Ricken ¶ 28; error report), receive a user input through a user interface after detecting the operation error (View Ricken ¶ 12; user interface).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combination of teachings with an error detecting and reporting device configured to detect and report an operation error, wherein to detect and report the operation error comprises to: detect the operation error for the operation of at least one of the plurality of functional modules and the operation control device, provide error data indicative of the operation error, receive a user input through a user interface after detecting the operation error since it is known in the art that an error report can be generated (View Ricken ¶ 28). Such modification would have allowed an error report to generated for an operation error.
Friedlander, Jain and Ricken do not explicitly teach provide labelling data in response to receiving the user input, the labelling data being indicative of information related to the operation error in addition to the error data, provide error report data comprising the error data and the labelling data, and transmit the error report data to an error repository remotely located with respect to both the plurality of functional modules and the operation control device.
However, Bartley teaches provide labelling data in response to receiving the user input, the labelling data being indicative of information related to the operation error in addition to the error data (View Bartley ¶ 15; classification of error), provide error report data comprising the error data and the labelling data (View Bartley ¶ 15; report).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combination of teachings with provide labelling data in response to receiving the user input, the labelling data being indicative of information related to the operation error in addition to the error data, provide error report data comprising the error data and the labelling data since it is known in the art that errors can be classified (View Bartley ¶ 15). Such modification would have allowed an error report to generated for classified errors.
Friedlander, Jain and Ricken do not explicitly teach transmit the error report data to an error repository remotely located with respect to both the plurality of functional modules and the operation control device.
However, Zhang teaches transmit the error report data to an error repository remotely located with respect to both the plurality of functional modules and the operation control device (View Zhang ¶ 56; store error report remotely).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combination of teachings with transmit the error report data to an error repository remotely located with respect to both the plurality of functional modules and the operation control device since it is known in the art that errors can be sent to a remote computer (View Zhang ¶ 56). Such modification would have allowed a classified errors to be transmitted to a report computer.
Prior Art Made of Record
Ulrich et al. (U.S. Patent 8,505,035), teaches the lab instrument comprises a processor coupled with a memory, that may be configured to store various instructions and configurations that are executable by the processor to analyze and modify data received from users, connected devices, or generated locally to the lab instrument, and to control the various devices and features of the lab instrument (e.g., receiving data from the testing equipment indicating an error, causing the status indicator to flash red, providing a description via the display and the communication device of the error).
Conclusion
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/SARAI E BUTLER/Primary Examiner, Art Unit 2114
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