DETAILED ACTION
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 final rejection. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on XXXXXXXXXXXXXX has been entered.
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 .
Status of Claims
Claims 15-20 are canceled by Applicant.
Claims 21-26 are new.
Claims 1-14 and 21-26 are pending and have been examined.
This action is in reply to the papers filed on 10/07/2024 (effective filing date 02/12/2021).
Applicant Elects Group I claims without traverse
Group I (Claims 1-14) claims are elected (without traverse), are pending, and have been examined.
Group II (Claims 15-20) claims are non-elected (cancelled) without traverse.
This action is in reply to the papers filed on 10/07/2024 (originally filed papers) and 02/19/2026 (Response to Election / Restriction).
Information Disclosure Statement
No Information Disclosure Statement has been filed.
The information disclosure statement(s) submitted: xxxxxxxx, has/have been considered by the Examiner and made of record in the application file.
Amendment
The present Office Action is based upon the original patent application filed on 10/07/2024 as modified by the amendment filed on 02/19/2026 (Response to Election / Restriction).
Terminal Disclaimer
The terminal disclaimer filed on xxx disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of US Pat. No. xxxx has been reviewed and has been placed in the file.
Examiner acknowledges Applicant’s filed Terminal Disclaimer to prior art patent McCauley et al. US Pat. No. 5,930,775. A terminal disclaimer may be filed to overcome or obviate a nonstatutory double patenting rejection (37 CFR 1.321; MPEP 706.02; 1490).
Double Patenting - Withdrawn
The double patenting rejection is withdrawn per the filed terminal disclaimer noted above.
Reasons For Allowance
Prior-Art Rejection withdrawn
Claims xxx are allowed. The closest prior art (See PTO-892, Notice of References Cited) does not teach the claimed:
The closest prior-art (xxx) teach the features as disclosed in Non-final Rejection (xxxx), however, these cited references do not teach and the prior-art does not teach at least the following combination of features and/or elements:
determining, at a second time after associating the information corresponding to the first loyalty card with the logged location, that a second user computing device is located within a specified distance of the logged location using a second positioning system of the second user computing device; in response to determining that the second user computing device is located within the specified distance of the logged location of the first user computing device at the first time of detecting: retrieving information corresponding to a second loyalty card, the second loyalty card being associated with the merchant and the second user computing device; and displaying, by the second user computing device, data describing the second loyalty card.
Claim Rejections - 35 USC §101 - Withdrawn
Per Applicant’s amendments and arguments and considering new guidance in the MPEP, the rejections are withdrawn. Specifically, in Applicant’s Remarks (dated 03/14/2017, pgs. 8-11), Applicant traverses the 35 USC §101 rejections arguing that the amended claims recite new limitations that are not abstract, amount to significantly more, are directed to a practical application, etc… For example, Applicant argues….
In support of their arguments, Applicant cites to the following recent Fed. Cir. court cases (i.e., Alice Corp. v. CLS Bank Int’l, SRI Int’l, Inc. v. Cisco Systems, Inc., Ultramercial, Inc. v. Hulu, LLC, Berkheimer, Core Wireless, McRO, Enfish, Bascom, DDR, etc…).
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 § 2146 et seq. 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 1-14 and 21-26 are rejected on the ground of anticipatory-nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 12,112,297.
18/908,603 – Claim 1. A method, comprising:
US 12,112,297 – Claim 1. A method, comprising:
18/908,603 – Claim 1. receiving first data from a development ecosystem, wherein the development ecosystem corresponds to a first system used to develop a technology product;
US 12,112,297 – Claim 1. receiving first data from a development ecosystem, wherein the development ecosystem corresponds to a first system used to develop a technology product;
18/908,603 – Claim 1. receiving second data from a non-development ecosystem, wherein the non-development ecosystem corresponds to a second system corresponding to end-use of the technology product;
US 12,112,297 – Claim 1. receiving second data from a non-development ecosystem, wherein the non-development ecosystem corresponds to a second system corresponding to end-use of the technology product;
18/908,603 – Claim 1. generating a combined set of data from both the first data from the development ecosystem and the second data from the non-development ecosystem;
US 12,112,297 – Claim 1. generating a combined set of data from both the first data from the development ecosystem and the second data from the non-development ecosystem;
18/908,603 – Claim 1. performing machine-learning (ML) on the combined set of data from both the development ecosystem and the non-development ecosystem; and
US 12,112,297 – Claim 1. performing machine-learning (ML) on the combined set of data from both the development ecosystem and the non-development ecosystem; and
18/908,603 – Claim 1. funneling results from performing ML-based analysis into multiple levels of funneled data objects.
US 12,112,297 – Claim 1. funneling results from performing ML-based analysis into multiple levels of funneled data objects;
18/908,603 – Claim 2. The method of claim 1, wherein the first data from the development ecosystem and the second data from the non-development ecosystem are both provided to a united analysis platform, and an ML processor resides at the united analysis platform to perform the machine learning.
US 12,112,297 – Claim 2. The method of claim 1, wherein the first data from the development ecosystem and the second data from the non-development ecosystem are both provided to a united analysis platform, and an ML processor resides at the united analysis platform to perform the machine learning.
18/908,603 – Claim 3. The method of claim 1, wherein the machine-learning performed on the combined set of the data from both the development ecosystem and the non-development ecosystem comprises automatic processing of the data to generate a database comprising both raw data and categorized data, where correlation is performed against the raw data and the categorized data from both the development ecosystem and the non-development ecosystem.
US 12,112,297 – Claim 15. The system of claim 13, wherein the ML operations performed on the data from both the development ecosystem and the non-development ecosystem comprises automatic processing of the data to generate a database comprising both raw data and categorized data, where correlation is performed against the raw data and the categorized data from both the development ecosystem and the non-development ecosystem.
18/908,603 – Claim 4. The method of claim 1, wherein the first data from the development ecosystem and the second data from the non-development ecosystem correspond to event hierarchies, the event hierarchies comprise stages of funneling for receiving and analyzing the first and second data, where a first event hierarchy corresponds to the first data from the development ecosystem and a second event hierarchy corresponds to the second data from the non-development ecosystem, and the first event hierarchy has different stages compared to the second event hierarchy.
US 12,112,297 – Claim 9. The computer program product of claim 7, wherein the first data from the development ecosystem and the second data from the non-development ecosystem correspond to event hierarchies, the event hierarchies comprise stages of funneling for receiving and analyzing the first and second data, where a first event hierarchy corresponds to the first data from the development ecosystem and a second event hierarchy corresponds to the second data from the non-development ecosystem, and the first event hierarchy has different stages compared to the second event hierarchy.
18/908,603 – Claim 5. The method of claim 1, further comprising: receiving third data from a user ecosystem, wherein the user ecosystem corresponds to one or more users that use the technology product; receiving fourth data from a customer relations ecosystem, wherein the customer relations ecosystem corresponds to a customer relations system related to the technology product; and wherein the combined set of data comprises the first data, the second data, the third data, and the fourth data.
US 12,112,297 – Claim 4. The method of claim 1, further comprising: receiving third data from a user ecosystem, wherein the user ecosystem corresponds to one or more users that use the technology product; receiving fourth data from a customer relations ecosystem, wherein the customer relations ecosystem corresponds to a customer relations system related to the technology product; and wherein the combined set of data comprises the first data, the second data, the third data, and the fourth data.
18/908,603 – Claim 6. The method of claim 1, wherein events from the combined set of data are analyzed to identify an incident, and the incident is analyzed to identify a ticket that is assigned within a software organization to address the ticket.
US 12,112,297 – Claim 5. The method of claim 1, wherein events from the combined set of data are analyzed to identify an incident, and the incident is analyzed to identify a ticket that is assigned within a software organization to address the ticket.
18/908,603 – Claim 7. The method of claim 6, wherein the ticket is created based upon clustering of multiple incidents.
US 12,112,297 – Claim 6. The method of claim 5, wherein the ticket is created based upon clustering of multiple incidents.
18/908,603 – Claim 8. A computer program product embodied on a computer readable medium, the computer readable medium having stored thereon a sequence of instructions which, when executed by a processor, performs: receiving first data from a development ecosystem, wherein the development ecosystem corresponds to a first system used to develop a technology product; receiving second data from a non-development ecosystem, wherein the non-development ecosystem corresponds to a second system corresponding to end-use of the technology product; generating a combined set of data from both the first data from the development ecosystem and the second data from the non-development ecosystem; performing machine-learning (ML) on the combined set of data from both the development ecosystem and the non-development ecosystem; and funneling results from performing ML-based analysis into multiple levels of funneled data objects.
18/908,603 – Claim 9. The computer program product of claim 8, wherein the first data from the development ecosystem and the second data from the non-development ecosystem are both provided to a united analysis platform, and an ML processor resides at the united analysis platform to perform the machine learning.
18/908,603 – Claim 10. The computer program product of claim 8, wherein the machine-learning performed on the combined set of the data from both the development ecosystem and the non-development ecosystem comprises automatic processing of the data to generate a database comprising both raw data and categorized data, where correlation is performed against the raw data and the categorized data from both the development ecosystem and the non-development ecosystem.
18/908,603 – Claim 11. The computer program product of claim 8, wherein the first data from the development ecosystem and the second data from the non-development ecosystem correspond to event hierarchies, the event hierarchies comprise stages of funneling for receiving and analyzing the first and second data, where a first event hierarchy corresponds to the first data from the development ecosystem and a second event hierarchy corresponds to the second data from the non-development ecosystem, and the first event hierarchy has different stages compared to the second event hierarchy.
18/908,603 – Claim 12. The computer program product of claim 8, further comprising: receiving third data from a user ecosystem, wherein the user ecosystem corresponds to one or more users that use the technology product; receiving fourth data from a customer relations ecosystem, wherein the customer relations ecosystem corresponds to a customer relations system related to the technology product; and wherein the combined set of data comprises the first data, the second data, the third data, and the fourth data.
18/908,603 – Claim 13. The computer program product of claim 8, wherein events from the combined set of data are analyzed to identify an incident, and the incident is analyzed to identify a ticket that is assigned within a software organization to address the ticket.
18/908,603 – Claim 14. The computer program product of claim 13, wherein the ticket is created based upon clustering of multiple incidents.
18/908,603 – Claim 21. (New) A system, comprising: a processor; a memory for holding programmable code; and wherein the programmable code includes instructions for: receiving first data from a development ecosystem, wherein the development ecosystem corresponds to a first system used to develop a technology product; receiving second data from a non-development ecosystem, wherein the non-development ecosystem corresponds to a second system corresponding to end- use of the technology product; generating a combined set of data from both the first data from the development ecosystem and the second data from the non-development ecosystem; performing machine-learning (ML) on the combined set of data from both the development ecosystem and the non-development ecosystem; and funneling results from performing ML- based analysis into multiple levels of funneled data objects.
18/908,603 – Claim 22. (New) The system of claim 21, wherein the first data from the development ecosystem and the second data from the non-development ecosystem are both provided to a united analysis platform, and an ML processor resides at the united analysis platform to perform the machine learning.
18/908,603 – Claim 23. (New) The system of claim 21, wherein the machine-learning performed on the combined set of the data from both the development ecosystem and the non-development ecosystem comprises automatic processing of the data to generate a database comprising both raw data and categorized data, where correlation is performed against the raw data and the categorized data from both the development ecosystem and the non-development ecosystem.
18/908,603 – Claim 24. (New) The system of claim 21, wherein the first data from the development ecosystem and the second data from the non-development ecosystem correspond to event hierarchies, the event hierarchies comprise stages of funneling for receiving and analyzing the first and second data, where a first event hierarchy corresponds to the first data from the development ecosystem and a second event hierarchy corresponds to the second data from the non-development ecosystem, and the first event hierarchy has different stages compared to the second event hierarchy.
18/908,603 – Claim 25. (New) The system of claim 21, wherein events from the combined set of data are analyzed to identify an incident, and the incident is analyzed to identify a ticket that is assigned within a software organization to address the ticket.
18/908,603 – Claim 26. (New) The system of claim 25, wherein the ticket is created based upon clustering of multiple incidents.
The remaining independent claims contain feature like that of claim 1 and are rejected accordingly. The dependent claims are further rejected for their dependency upon a rejected independent base claim.
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 1-14 and 21-26 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more. These claims recite a method, system, and computer readable medium for integrated analysis of data from multiple source environments.
Claim 1 recites [a] method, comprising: receiving first data from a development ecosystem, wherein the development ecosystem corresponds to a first system used to develop a technology product; receiving second data from a non-development ecosystem, wherein the non-development ecosystem corresponds to a second system corresponding to end-use of the technology product; generating a combined set of data from both the first data from the development ecosystem and the second data from the non-development ecosystem; performing machine-learning (ML) on the combined set of data from both the development ecosystem and the non-development ecosystem; and funneling results from performing ML-based analysis into multiple levels of funneled data objects.
The claims are being rejected according to the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 5, p. 50-57 (Jan. 7, 2019)).
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 1-7 recite a method and, therefore, are directed to the statutory class of a process. Claims 21-26 recite a system/apparatus and, therefore, are directed to the statutory class of machine. Claims 8-14 recite a computer program product embodied on a computer readable medium and, therefore, are directed to the statutory class of a manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited?
Yes. The following tables identify the specific limitations that recite an abstract idea. The column that identifies the additional elements will be relevant to the analysis in step 2A, prong two, and step 2B.
Claim 1: Identification of Abstract Idea and Additional Elements, using Broadest Reasonable Interpretation
Claim Limitation
Abstract Idea
Additional Element
1. A method, comprising:
No additional elements are positively claimed.
receiving first data from a development ecosystem, wherein the development ecosystem corresponds to a first system used to develop a technology product;
This limitation includes the step(s) of: receiving first data from a development ecosystem, wherein the development ecosystem corresponds to a first system used to develop a technology product.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate integrated analysis of data from multiple source environments which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed. The development ecosystem is interpreted as purely software as the specification, does not define nor disclose any structural components of the ecosystem that may be interpreted as ‘additional elements’.
receiving second data from a non-development ecosystem, wherein the non-development ecosystem corresponds to a second system corresponding to end-use of the technology product;
This limitation includes the step(s) of: receiving second data from a non-development ecosystem, wherein the non-development ecosystem corresponds to a second system corresponding to end-use of the technology product.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate integrated analysis of data from multiple source environments which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed. The non-development ecosystem is interpreted as purely software as the specification does not define nor disclose any structural components of the non-development ecosystem that may be interpreted as ‘additional elements’.
generating a combined set of data from both the first data from the development ecosystem and the second data from the non-development ecosystem;
This limitation includes the step(s) of: generating a combined set of data from both the first data from the development ecosystem and the second data from the non-development ecosystem.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate integrated analysis of data from multiple source environments which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
performing machine-learning (ML) on the combined set of data from both the development ecosystem and the non-development ecosystem; and
This limitation includes the step(s) of: performing machine-learning (ML) on the combined set of data from both the development ecosystem and the non-development ecosystem.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate integrated analysis of data from multiple source environments which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
funneling results from performing ML-based analysis into multiple levels of funneled data objects.
This limitation includes the step(s) of: funneling results from performing ML-based analysis into multiple levels of funneled data objects.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate integrated analysis of data from multiple source environments which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
As shown above, under Step 2A, Prong One, the claims recite a judicial exception (an abstract idea). The claims are directed to the abstract idea of integrated analysis of data from multiple source environments, which, pursuant to MPEP 2106.04, is aptly categorized as a mathematical concept, mental process and/or a method of organizing human activity. Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
The method claims do NOT recite any additional elements. Consequently, at least the method claims must be construed as abstract and capable of being performed mentally and/or manually with just pen and paper. The Office encourages Applicant to positively claim the structural features necessary to perform each individual method step and feature.
Next, the aforementioned claims recite additional functional elements that are associated with the judicial exception, including: a processor and computer readable medium for implementing the CRM claims (Claim 8 CRM claim) and processor and memory for implementing the system claims (Claim 21 system claim). Examiner understands these limitations to be insignificant extrasolution activity. (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191-192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle in to a patentable process.”).
The aforementioned claims also recite additional technical elements including: a processor and computer readable medium for implementing the CRM claims (Claim 8 CRM claim) and processor and memory for implementing the system claims (Claim 21 system claim). These limitations are recited at a high level of generality and appear to be nothing more than generic computer components. Examiner further notes that the “development ecosystem” and “non-development ecosystem” is nothing more than a software application being executed on a generic computer. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
No. The judicial exception is not integrated into a practical application. The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating, receiving, processing, analyzing, and outputting/displaying data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, these claims are directed to an abstract idea.
Furthermore, looking at the elements individually and in combination, under Step 2A, Prong Two, the claims as a whole do not integrate the judicial exception into a practical application because they fail to: improve the functioning of a computer or a technical field, apply the judicial exception in the treatment or prophylaxis of a disease, apply the judicial exception with a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Rather, the claims merely use a computer as a tool to perform the abstract idea(s), and/or add insignificant extra-solution activity to the judicial exception, and/or generally link the use of the judicial exception to a particular technological environment.
Step 2B: Does the Claim Provide an Inventive Concept?
Next, under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Simply put, as noted above, there is no indication that the combination of elements improves the functioning of a computer (or any other technology), and their collective functions merely provide conventional computer implementation. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate, receive, send, process, analyze, output, or display data.
Additionally, pursuant to the requirement under Berkheimer, the following citations are provided to demonstrate that the additional elements, identified as extra-solution activity, amount to activities that are well-understood, routine, and conventional. See MPEP 2106.05(d).
Capturing an image (code) with an RFID reader. Ritter, US Patent No. 7734507 (Col. 3, Lines 56-67); “RFID: Riding on the Chip” by Pat Russo. Frozen Food Age. New York: Dec. 2003, vol. 52, Issue 5; page S22.
Receiving or transmitting data over a network. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Storing and retrieving information in memory. Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Outputting/Presenting data to a user. 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); MPEP 2106.05(g)(3).
Using a machine learning model to determine user segment characteristics for an ad campaign. https://whites.agency/blog/how-to-use-machine-learning-for-customer-segmentation/.
Thus, taken alone and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea), and are ineligible under 35 USC 101.
Independent system claim 21 and CRM claim 8 also contains the identified abstract ideas, with the additional elements of a processor and storage medium, which are a generic computer components, and thus not significantly more for the same reasons and rationale above.
Dependent claims 2-7, 9-14, and 22-26 further describe the abstract idea. The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
As such, the claims are not patent eligible.
Invention Could be Performed Manually
It is conceivable that the invention could be performed manually without the aid of machine and/or computer. For example, Applicant claims receiving data, generating a combined set of data, performing machine-learning on the data (e.g., processing data), funneling results, etc… Each of these features could be performed manually and/or with the aid of a simple generic computer to facilitate the transmission of data.
See also Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., and In re Venner, which stand for the concept that automating manual activity and/or applying modern electronics to older mechanical devices to accomplish the same result is not sufficient to distinguish over the prior art. Here, applicant is merely claiming computers to facilitate and/or automate functions which used to be commonly performed by a human.
Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 82 USPQ2d 1687 (Fed. Cir. 2007) "[a]pplying modern electronics to older mechanical devices has been commonplace in recent years…"). The combination is thus the adaptation of an old idea or invention using newer technology that is commonly available and understood in the art.
In In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958), the court held that broadly providing an automatic or mechanical means to replace manual activity which accomplished the same result is not sufficient to distinguish over the prior art. MPEP 2144.04, III Automating a Manual Activity.
MPEP 2144.04 III - Automating a Manual Activity and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) further stand for and provide motivation for using technology, hardware, computer, or server to automate a manual activity.
Therefore, the Office finds no improvements to another technology or field, no improvements to the function of the computer itself, and no meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Therefore, based on the two-part Alice Corp. analysis, there are no limitations in any of the claims that transform the exception (i.e., the abstract idea) into a patent eligible application.
Claim Rejections - Not an Ordered Combination
None of the limitations, considered as an ordered combination provide eligibility, because taken as a whole, the claims simply instruct the practitioner to implement the abstract idea with routine, conventional activity.
Claim Rejections - Preemption
Allowing the claims, as presently claimed, would preempt others from performing ‘integrated analysis of data from multiple source environments’. Furthermore, the claim language only recites the abstract idea of performing this method, there are no concrete steps articulating a particular way in which this idea is being implemented or describing how it is being performed.
Claim Rejections - 35 USC § 101 – electromagnetic signals per se
Claims 8-14 are also rejected under 35 USC §101 as being directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to electromagnetic signals per se. Claim 8 is directed to a “computer program product embodied on a computer readable medium…”. Applicant’s specification (US PGPub. 2025/0029059 [0121-0124]) does not expressly exclude wireless signals. Thus, the claimed computer-readable medium could be an electromagnetic signal being communicated through a hardwired or a wireless communication connection. Therefore, since such signals do not fall within any of the four recognized categories of patent eligible subject matter, Claims 8-14 are rejected as being directed to non-statutory subject matter, i.e. electromagnetic signals per se. See the David Kappos memo titled “Subject Matter Eligibility of Computer Readable Media” dated 1/26/2010 and available at: http://www.uspto.gov/patents/law/notices/101_crm_20100127.pdf. Adding “non-transitory” should remedy this 101 issue. For example, amending to state a “non-transitory computer program product embodied on a computer readable medium …” should fix this 101 issue.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 8, 21 are rejected under 35 U.S.C. 103 as being unpatentable over: Toyoshima et al. 2005/0187737; in view of Bui et al. 2021/0200760; in further view of Sarferaz 2021/0342738.
18/908,603 – Claim 1. Toyoshima et al. 2005/0187737 teaches A method, comprising: receiving first data from a development ecosystem (Toyoshima et al. 2005/0187737 [0006 – performing data analysis on data gathered…] An embodiment of the present invention is a method for performing data analysis on data gathered in an electronic device manufacturing process. The method includes collecting production data, collecting non-production data, keying the production data, keying the non-production data, combining the production data and the non-production data into a single data set, and analyzing single data-set. An embodiment of the present invention further includes performing calculations on the production data and the non-production data. Collection of production data includes at least one of collecting parametric production data, collecting film thickness data, critical dimension data, and any other data that is relevant to the production process and its condition. Collection of non-production data includes at least one of collecting non-production data from a single data source at a single source location or from a plurality of locations, and collecting non-production data from a single data source with some temporal periodicity. In an embodiment, the temporal periodicity is fixed. In an embodiment, the temporal periodicity is not fixed. [0037 – Data is collected from both production and non-production sources…] FIG. 2 presents a method, according to an embodiment of the present invention, for combining the data taken during the manufacturing process for the purposes of data analysis. Data is collected from both production and non-production sources, at 201, 202, 203. Non-production sources may include, but not be limited to, thermometers providing temperature readings 201a, barometers providing pressure measurements 201c, staff productivity recording systems, electrical measurements 201b, equipment control data, metrology tool calibration data, etc. Non-production sources also include sources that provide any other data relevant to the production environment, where the production environment may include, but not be limited to, the facility where the production is performed, a larger physical gathering of multiple production facilities in a single geographical location, etc. Production sources may further be divided into online production sources and offline production sources. Online production sources may include, but not be limited to, critical dimension readings 202a, chemical sampling 202b, surface readings, 202c, etc. Offline production sources may include, but not be limited to, offline circuit testing 203a, photomask inspection 203b, operating temperature readings 203c, etc. At 204 and 205 the non-production data and the production data are keyed to some value, respectively. In an embodiment, the data is keyed to a temporal, or date-time, value. In an embodiment, at 204, that data is keyed with a date-time value. In an embodiment, at 205, that data is keyed with production lot data, which may be further keyed with date-time values associated with the time that a particular production lot began a manufacturing process as well as the time that the particular production lot completed the manufacturing process. In an embodiment, non-production data is measured at a single location, such as depicted in FIG. 3. In an embodiment, the sampling location for the data measurement 310 is separated some distance 320 from the process equipment 301. In an embodiment, production data is measured at a plurality of locations, such as depicted in FIG. 4. The process equipment 301 is separated by a variable distance from the locations where data is being measured. For sampling location 1 at 310, the distance 320 can be defined as d.sub.1. For sampling location 2 at 410, the distance 420 can be defined as d.sub.2. There may be many locations where the data is being measured. For all sampling locations I at 411, the distance 421 can be defined as d.sub.i. Such multiple sampling locations 310, 410, 411 allow statistical operations on a similar type of sampled data from a plurality of different sample locations. For example, the different sample locations allow a similar type of data to be collected at a plurality of locations in a lot.), wherein the development ecosystem corresponds to a first system used to develop a technology product (Toyoshima et al. 2005/0187737 [Fig. 7; 0030 - a data analysis system, and an environment with which it is used, for processing and analyzing production and non-production data] FIG. 7 is a block diagram illustrating generally, among other things, one example of portions of a data analysis system, and an environment with which it is used, for processing and analyzing production and non-production data. [0053 - processor 730 may receive data from the input device] The processor 730 may represent a central processing unit of any type of architecture, such as a CISC (Complex Instruction Set Computing), RISC (Reduced Instruction Set Computing), VLIW (Very Long Instruction Word), or a hybrid architecture, although any appropriate processor may be used. The processor 730 may execute instructions and may include that portion of the computer 702 that controls the operation of the entire computer. Although not depicted in FIG. 7, the processor 730 typically includes a control unit that organizes data and program storage in memory and transfers data and other information between the various parts of the computer 702. The processor 730 may receive data from the input device 750, may read and store code and data in the storage device 740, may send data to the output device 760, and may send and receive code and/or data to/from the network 710.); receiving second data from a non-development ecosystem, wherein the non-development ecosystem corresponds to a second system corresponding to end-use of the technology product (Toyoshima et al. 2005/0187737 [0004 – non-production interpreted as non-development] Further compounding the analysis problem is that factors typically thought to be non-production are not considered in the analysis. Environmental measurements which can greatly affect the quality of manufacturing end-product are just one example of these factors. Even when one is able to qualitatively measure these factors, connecting that meaningfully to other measurements considered to be non-production for the purposes of data analysis requires a user to manually examine the data for commonalities and correlate the data based on those. Further combining that combination with actual production data greatly compounds the amount of data as well as compounding the inability to perform meaningful and timely data analysis. [0005 - combine data from production and non-production sources into a combined set of data for quicker analysis] What is needed is a technique to quickly combine data from production and non-production sources into a combined set of data for quicker analysis. [0006 - a method for performing data analysis on data gathered … method includes collecting production data, collecting non-production data…] An embodiment of the present invention is a method for performing data analysis on data gathered in an electronic device manufacturing process. The method includes collecting production data, collecting non-production data, keying the production data, keying the non-production data, combining the production data and the non-production data into a single data set, and analyzing single data-set. An embodiment of the present invention further includes performing calculations on the production data and the non-production data. Collection of production data includes at least one of collecting parametric production data, collecting film thickness data, critical dimension data, and any other data that is relevant to the production process and its condition. Collection of non-production data includes at least one of collecting non-production data from a single data source at a single source location or from a plurality of locations, and collecting non-production data from a single data source with some temporal periodicity. In an embodiment, the temporal periodicity is fixed. In an embodiment, the temporal periodicity is not fixed. [0035 – end product] FIG. 1 depicts a pictorial representation of a simplified manufacturing process for items. Items, in an embodiment, include integrated circuits. The item undergoing processing 101 enters the process 110 and exits as a finished product 102. The process 110 is located in a larger manufacturing facility 120. Conditions in the machine performing the process are very important to the quality of the end product 102. The conditions of the item undergoing processing 103 are also very important to the quality of the end product. In addition, the conditions of the manufacturing facility 120 may also impact the quality of the end product. Measurements may be taken on the item 101, 102, and 103, as well as conditions of the actual manufacturing process 110. These measurements can be called production data. The production data is from sources that are directly related to the manufacturing process being performed. These sources include, but are not limited to, test probe data, parametric data, film thickness data, and critical dimension data. In an embodiment, a particular production data sample is gathered once per lot, i.e. production lot data. A production lot can be defined as a subset of the entirety of manufactured items, for example a plurality of work pieces such as electronic devices, integrated circuits, substrates, semiconductor wafers, or other similar structures in this art. A lot may further be considered as that quantity of product produced under similar conditions, at a similar establishment, over some period of time. In an embodiment, a particular production data sample is gathered multiple times per lot. In an embodiment, a particular data sample is applied across multiple production lots. Though this detailed description uses the term production data to refer to these data measurements, this is not to be taken in a limiting sense, as any data that relates directly to the manufacturing process being performed is considered to be production data, regardless of what it is actually called. Further, production data may be further defined as being either online or offline. Online data may be data which is measured directly on the item being manufactured and may be things such as the temperature of the manufactured item, or its thickness. Online data may also be data measured from the manufacturing process in question while the item is being processed. Offline data is that data that, though directly related to the manufacturing process, is not measured on the actual manufactured item or during the actual manufacturing step, such as the operating temperature of the machine, the operating pressure, or some other measurement.); generating a combined set of data from both the first data from the development ecosystem and the second data from the non-development ecosystem (Toyoshima et al. 2005/0187737 [0005 - a technique to quickly combine data from production and non-production sources into a combined set of data for quicker analysis] What is needed is a technique to quickly combine data from production and non-production sources into a combined set of data for quicker analysis. [0006-0008]); performing machine-learning (ML) on the combined set of data from both the development ecosystem and the non-development ecosystem (Toyoshima et al. 2005/0187737 [0006 - a method for performing data analysis on data gathered] An embodiment of the present invention is a method for performing data analysis on data gathered in an electronic device manufacturing process. The method includes collecting production data, collecting non-production data, keying the production data, keying the non-production data, combining the production data and the non-production data into a single data set, and analyzing single data-set. An embodiment of the present invention further includes performing calculations on the production data and the non-production data. Collection of production data includes at least one of collecting parametric production data, collecting film thickness data, critical dimension data, and any other data that is relevant to the production process and its condition. Collection of non-production data includes at least one of collecting non-production data from a single data source at a single source location or from a plurality of locations, and collecting non-production data from a single data source with some temporal periodicity. In an embodiment, the temporal periodicity is fixed. In an embodiment, the temporal periodicity is not fixed.); and funneling results from performing ML-based analysis into multiple levels of funneled data objects (Toyoshima et al. 2005/0187737 [0030; FIG. 7 is a block diagram illustrating generally, among other things, one example of portions of a data analysis system, and an environment with which it is used, for processing and analyzing production and non-production data] FIG. 7 is a block diagram illustrating generally, among other things, one example of portions of a data analysis system, and an environment with which it is used, for processing and analyzing production and non-production data.).
Toyoshima et al. 2005/0187737 may not expressly disclose the performing machine-learning (ML) features, however, Bui et al. 2021/0200760 teaches these features as follows (Bui et al. 2021/0200760 [0011 - machine learning operations may be performed] In various embodiments, this lack of point-in-time consistency of data between datacenters may present significant technical problems. As one non-limiting example, within a particular multi-datacenter topology, there may be both a production environment, which includes one or more datacenters primarily used to host software applications and serve online traffic from end users, and a non-production environment, which includes one or more datacenters used primarily to perform testing, simulations, or other analytical operations. (Note, however, that datacenters in a production environment may also be used to perform operations other than servicing online traffic, such as performing analytical operations, and that datacenters in a non-production environment may be used to perform operations other than testing and simulations. The terms “production environment” and “non-production environment,” are simply used herein to denote a significant or common function performed by datacenters within their respective environments.) In some instances, it is desirable to perform analytical operations both in the production environment and, at a subsequent time, to perform the same or similar analytical operations in the non-production environment. As a non-limiting example, machine learning operations may be performed at a first datacenter that is in the production environment, using a particular dataset maintained at the first datacenter to perform a simulation (e.g., to test a risk-detection scenario). In such an example, the machine learning model is trained based on the particular dataset as it existed at a first point in time.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Toyoshima et al. 2005/0187737 to include the features as taught by Bui et al. 2021/0200760. One of ordinary skill in the art would have been motivated to do so to utilize well known ML techniques for analyzing data which should prove to improve user experience, maximize profits, and optimize revenue.
Toyoshima et al. 2005/0187737 may not expressly disclose the ‘funneling results’ from performing ML-based analysis features, however, Sarferaz 2021/0342738 teaches these features as follows (Sarferaz 2021/0342738 [0222 - provide information regarding analysis provided by a machine learning algorithm] The first level explanation of the user interface screen 2918 can provide a global explanation 2922. The global explanation 2922 can provide information regarding analysis provided by a machine learning algorithm, generally (e.g., not with respect to any particular analysis, but which may be calculated based at least in part on a plurality of analyses). The global explanation 2922 can include information such as the predictive power of a machine learning model, the confidence level of a machine learning model, contributions of individual features to results (generally), relationships (such as dependencies) between features, how results are filtered, sorted, or ranked, details regarding the model (e.g., the theoretical basis of the model, details regarding how the model was trained, such as a number of data points used to trained the model, information regarding when the model was put into use or last trained, how many analyses have been performed using the model, user ratings of the model, etc.), or combinations of these types of information.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Toyoshima et al. 2005/0187737 to include the features as taught by Sarferaz 2021/0342738. One of ordinary skill in the art would have been motivated to do so in order to utilize well known ML techniques for analyzing data which should prove to improve user experience, maximize profits, and optimize revenue.
18/908,603 – Claim 8. Toyoshima et al. 2005/0187737 teaches A computer program product embodied on a computer readable medium (Toyoshima et al. 2005/0187737 [0055 - machine-readable media] The storage device 740 represents one or more mechanisms for storing data. For example, the storage device 740 may include read only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, and/or other machine-readable media. In other embodiments, any appropriate type of storage device may be used. Although only one storage device 740 is shown, multiple storage devices and multiple types of storage devices may be present. Further, although the computer 702 is drawn to contain the storage device 740, it may be distributed across other computers, for example on server 701. [0067 - invention may be implemented as a program product…] As was described in detail above, aspects of an embodiment pertain to specific apparatus and method elements implementable on a computer or other electronic device. In another embodiment, the invention may be implemented as a program product for use with an electronic device. The programs defining the functions of this embodiment may be delivered to an electronic device via a variety of signal-bearing media, which include, but are not limited to:), the computer readable medium having stored thereon a sequence of instructions which, when executed by a processor (Toyoshima et al. 2005/0187737 [0053 - processor 730 may execute instructions…] The processor 730 may represent a central processing unit of any type of architecture, such as a CISC (Complex Instruction Set Computing), RISC (Reduced Instruction Set Computing), VLIW (Very Long Instruction Word), or a hybrid architecture, although any appropriate processor may be used. The processor 730 may execute instructions and may include that portion of the computer 702 that controls the operation of the entire computer. Although not depicted in FIG. 7, the processor 730 typically includes a control unit that organizes data and program storage in memory and transfers data and other information between the various parts of the computer 702. The processor 730 may receive data from the input device 750, may read and store code and data in the storage device 740, may send data to the output device 760, and may send and receive code and/or data to/from the network 710.), performs: …
receiving first data from a development ecosystem, wherein the development ecosystem corresponds to a first system used to develop a technology product; receiving second data from a non-development ecosystem, wherein the non-development ecosystem corresponds to a second system corresponding to end-use of the technology product; generating a combined set of data from both the first data from the development ecosystem and the second data from the non-development ecosystem; performing machine-learning (ML) on the combined set of data from both the development ecosystem and the non-development ecosystem; and funneling results from performing ML-based analysis into multiple levels of funneled data objects.
18/908,603 – Claim 21. (New) A system, comprising: a processor; a memory for holding programmable code (Toyoshima et al. 2005/0187737 [0030 - FIG. 7 is a block diagram illustrating generally, among other things, one example of portions of a data analysis system] FIG. 7 is a block diagram illustrating generally, among other things, one example of portions of a data analysis system, and an environment with which it is used, for processing and analyzing production and non-production data. [0053 - FIG. 7, the processor 730 typically includes a control unit that organizes data and program storage in memory and transfers data and other information between the various parts of the computer 702. The processor 730 may receive data from the input device 750, may read and store code and data in the storage device 740, may send data to the output device 760, and may send and receive code and/or data to/from the network] The processor 730 may represent a central processing unit of any type of architecture, such as a CISC (Complex Instruction Set Computing), RISC (Reduced Instruction Set Computing), VLIW (Very Long Instruction Word), or a hybrid architecture, although any appropriate processor may be used. The processor 730 may execute instructions and may include that portion of the computer 702 that controls the operation of the entire computer. Although not depicted in FIG. 7, the processor 730 typically includes a control unit that organizes data and program storage in memory and transfers data and other information between the various parts of the computer 702. The processor 730 may receive data from the input device 750, may read and store code and data in the storage device 740, may send data to the output device 760, and may send and receive code and/or data to/from the network 710.); and wherein the programmable code includes instructions (Toyoshima et al. 2005/0187737 [0071 - machine-readable instructions] Such signal-bearing media, when carrying machine-readable instructions that direct the functions of the present invention, represent embodiments of the present invention. [0061 - programming devices] The computer 702 may be implemented using any suitable hardware and/or software, such as a personal computer or other electronic computing device. Portable computers, laptop or notebook computers, PDAs (Personal Digital Assistants), two-way alphanumeric pagers, keypads, portable telephones, appliances with a computing unit, pocket computers, and mainframe computers are examples of other possible configurations of the computer 702. The hardware and software depicted in FIG. 7 may vary for specific applications and may include more or fewer elements than those depicted. For example, other peripheral devices such as audio adapters, or chip programming devices, such as EPROM (Erasable Programmable Read-Only Memory) programming devices may be used in addition to or in place of the hardware already depicted. [0033 - programming instructions] Parts of the description may be presented in terms of operations performed through the execution of programming instructions. As well understood by those skilled in the art, those operations may take the form of electrical, magnetic, or optical signals capable of being stored, transferred, combined, and otherwise manipulated through, for example, electrical components.) for: …
receiving first data from a development ecosystem, wherein the development ecosystem corresponds to a first system used to develop a technology product; receiving second data from a non-development ecosystem, wherein the non-development ecosystem corresponds to a second system corresponding to end- use of the technology product; generating a combined set of data from both the first data from the development ecosystem and the second data from the non-development ecosystem; performing machine-learning (ML) on the combined set of data from both the development ecosystem and the non-development ecosystem; and funneling results from performing ML- based analysis into multiple levels of funneled data objects.
The remaining features/limitations of Claims 8 and 21, have similar features/limitations as of Claim 1, therefore those features/limitations and the claims are REJECTED under the same rationale as Claim 1.
Claims 2, 9, 22 are rejected under 35 U.S.C. 103 as being unpatentable over: Toyoshima et al. 2005/0187737; in view of Bui et al. 2021/0200760; in further view of Sarferaz 2021/0342738; in even further view of Zhou et al. 2020/0374696.
18/908,603 – Claim 2. Toyoshima et al. 2005/0187737 further teaches The method of claim 1, wherein the first data from the development ecosystem and the second data from the non-development ecosystem are both provided to a united analysis platform (Toyoshima et al. 2005/0187737 [0006 – performing data analysis on data gathered…] An embodiment of the present invention is a method for performing data analysis on data gathered in an electronic device manufacturing process. The method includes collecting production data, collecting non-production data, keying the production data, keying the non-production data, combining the production data and the non-production data into a single data set, and analyzing single data-set. An embodiment of the present invention further includes performing calculations on the production data and the non-production data. Collection of production data includes at least one of collecting parametric production data, collecting film thickness data, critical dimension data, and any other data that is relevant to the production process and its condition. Collection of non-production data includes at least one of collecting non-production data from a single data source at a single source location or from a plurality of locations, and collecting non-production data from a single data source with some temporal periodicity. In an embodiment, the temporal periodicity is fixed. In an embodiment, the temporal periodicity is not fixed. [0037 – Data is collected from both production and non-production sources…] FIG. 2 presents a method, according to an embodiment of the present invention, for combining the data taken during the manufacturing process for the purposes of data analysis. Data is collected from both production and non-production sources, at 201, 202, 203. Non-production sources may include, but not be limited to, thermometers providing temperature readings 201a, barometers providing pressure measurements 201c, staff productivity recording systems, electrical measurements 201b, equipment control data, metrology tool calibration data, etc. Non-production sources also include sources that provide any other data relevant to the production environment, where the production environment may include, but not be limited to, the facility where the production is performed, a larger physical gathering of multiple production facilities in a single geographical location, etc. Production sources may further be divided into online production sources and offline production sources. Online production sources may include, but not be limited to, critical dimension readings 202a, chemical sampling 202b, surface readings, 202c, etc. Offline production sources may include, but not be limited to, offline circuit testing 203a, photomask inspection 203b, operating temperature readings 203c, etc. At 204 and 205 the non-production data and the production data are keyed to some value, respectively. In an embodiment, the data is keyed to a temporal, or date-time, value. In an embodiment, at 204, that data is keyed with a date-time value. In an embodiment, at 205, that data is keyed with production lot data, which may be further keyed with date-time values associated with the time that a particular production lot began a manufacturing process as well as the time that the particular production lot completed the manufacturing process. In an embodiment, non-production data is measured at a single location, such as depicted in FIG. 3. In an embodiment, the sampling location for the data measurement 310 is separated some distance 320 from the process equipment 301. In an embodiment, production data is measured at a plurality of locations, such as depicted in FIG. 4. The process equipment 301 is separated by a variable distance from the locations where data is being measured. For sampling location 1 at 310, the distance 320 can be defined as d.sub.1. For sampling location 2 at 410, the distance 420 can be defined as d.sub.2. There may be many locations where the data is being measured. For all sampling locations I at 411, the distance 421 can be defined as d.sub.i. Such multiple sampling locations 310, 410, 411 allow statistical operations on a similar type of sampled data from a plurality of different sample locations. For example, the different sample locations allow a similar type of data to be collected at a plurality of locations in a lot.), and an ML processor resides at the united analysis platform to perform the machine learning (Toyoshima et al. 2005/0187737 [0006 - a method for performing data analysis on data gathered] An embodiment of the present invention is a method for performing data analysis on data gathered in an electronic device manufacturing process. The method includes collecting production data, collecting non-production data, keying the production data, keying the non-production data, combining the production data and the non-production data into a single data set, and analyzing single data-set. An embodiment of the present invention further includes performing calculations on the production data and the non-production data. Collection of production data includes at least one of collecting parametric production data, collecting film thickness data, critical dimension data, and any other data that is relevant to the production process and its condition. Collection of non-production data includes at least one of collecting non-production data from a single data source at a single source location or from a plurality of locations, and collecting non-production data from a single data source with some temporal periodicity. In an embodiment, the temporal periodicity is fixed. In an embodiment, the temporal periodicity is not fixed.).
Toyoshima et al. 2005/0187737 may not expressly disclose the “platform” features, however, Zhou et al. 2020/0374696 teaches these features as follows (Zhou et al. 2020/0374696 [0051 – IoT platform interpreted as united analysis platform] “IoT platform” described in this application is a relatively broad concept, can perform operations such as integration, sorting, analysis, and feedback on data information collected by an IoT terminal, and mainly performs management on many terminals, data management, operation management, and security management. The IoT platform integrates many advanced technologies, including cloud computing, big data, artificial intelligence, and the like, to meet requirements for performing information transmission and exchange on the IoT. The IoT platform may include a plurality of processing platforms with different functions, and is responsible for extracting data used for control and decision making from perception data based on an application requirement, and converting the data into different formats for sharing among a plurality of application systems. In actual application, the IoT platform may include one or more devices. In terms of a type, the IoT platform may be divided into four types of platforms from bottom to top a terminal management platform, a connection management platform, an application development platform, and a service analysis platform. The terminal management platform is mainly responsible for performing registration management, identity identification, access control, configuration, monitoring, query, system upgrade, troubleshooting, lifecycle management, and the like on the IoT terminal. The connection management platform is mainly responsible for performing configuration and fault management, network resource usage management, connection resource management, package change, number/IP address/media access control (MAC) resource management, and the like on an IoT connection. The application development platform may provide a platform as a service (Paas) platform used for application development and unified data storage, and provide an application development tool, middleware, data storage, a service logic engine, an application platform interface (API) for connecting to a third party, and the like. The service analysis platform is mainly configured to classify and analyze service data, and provide a visual data analysis result. The service analysis platform monitors a device status and gives a warning through real-time dynamic analysis, or analyzes and predicts a service through machine learning.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Toyoshima et al. 2005/0187737 to include the features as taught by Zhou et al. 2020/0374696. One of ordinary skill in the art would have been motivated to do so to utilize well known data analysis and ML techniques for analyzing data which should prove to improve user experience, maximize profits, and optimize revenue.
18/908,603 – Claim 9. The computer program product of claim 8, wherein the first data from the development ecosystem and the second data from the non-development ecosystem are both provided to a united analysis platform, and an ML processor resides at the united analysis platform to perform the machine learning.
18/908,603 – Claim 22. (New) The system of claim 21, wherein the first data from the development ecosystem and the second data from the non-development ecosystem are both provided to a united analysis platform, and an ML processor resides at the united analysis platform to perform the machine learning.
Claims 9 and 2, have similar limitations as of Claim 2, therefore they are REJECTED under the same rationale as Claim 2.
Claims 3, 10, 23 are rejected under 35 U.S.C. 103 as being unpatentable over: Toyoshima et al. 2005/0187737; in view of Bui et al. 2021/0200760; in further view of Sarferaz 2021/0342738; in even further view of Duncan et al. 2017/0220943.
18/908,603 – Claim 3. Toyoshima et al. 2005/0187737 further teaches The method of claim 1, wherein the machine-learning performed on the combined set of the data from both the development ecosystem and the non-development ecosystem (Toyoshima et al. 2005/0187737 [0006 – performing data analysis on data gathered…] An embodiment of the present invention is a method for performing data analysis on data gathered in an electronic device manufacturing process. The method includes collecting production data, collecting non-production data, keying the production data, keying the non-production data, combining the production data and the non-production data into a single data set, and analyzing single data-set. An embodiment of the present invention further includes performing calculations on the production data and the non-production data. Collection of production data includes at least one of collecting parametric production data, collecting film thickness data, critical dimension data, and any other data that is relevant to the production process and its condition. Collection of non-production data includes at least one of collecting non-production data from a single data source at a single source location or from a plurality of locations, and collecting non-production data from a single data source with some temporal periodicity. In an embodiment, the temporal periodicity is fixed. In an embodiment, the temporal periodicity is not fixed. [0037 – Data is collected from both production and non-production sources…] FIG. 2 presents a method, according to an embodiment of the present invention, for combining the data taken during the manufacturing process for the purposes of data analysis. Data is collected from both production and non-production sources, at 201, 202, 203. Non-production sources may include, but not be limited to, thermometers providing temperature readings 201a, barometers providing pressure measurements 201c, staff productivity recording systems, electrical measurements 201b, equipment control data, metrology tool calibration data, etc. Non-production sources also include sources that provide any other data relevant to the production environment, where the production environment may include, but not be limited to, the facility where the production is performed, a larger physical gathering of multiple production facilities in a single geographical location, etc. Production sources may further be divided into online production sources and offline production sources. Online production sources may include, but not be limited to, critical dimension readings 202a, chemical sampling 202b, surface readings, 202c, etc. Offline production sources may include, but not be limited to, offline circuit testing 203a, photomask inspection 203b, operating temperature readings 203c, etc. At 204 and 205 the non-production data and the production data are keyed to some value, respectively. In an embodiment, the data is keyed to a temporal, or date-time, value. In an embodiment, at 204, that data is keyed with a date-time value. In an embodiment, at 205, that data is keyed with production lot data, which may be further keyed with date-time values associated with the time that a particular production lot began a manufacturing process as well as the time that the particular production lot completed the manufacturing process. In an embodiment, non-production data is measured at a single location, such as depicted in FIG. 3. In an embodiment, the sampling location for the data measurement 310 is separated some distance 320 from the process equipment 301. In an embodiment, production data is measured at a plurality of locations, such as depicted in FIG. 4. The process equipment 301 is separated by a variable distance from the locations where data is being measured. For sampling location 1 at 310, the distance 320 can be defined as d.sub.1. For sampling location 2 at 410, the distance 420 can be defined as d.sub.2. There may be many locations where the data is being measured. For all sampling locations I at 411, the distance 421 can be defined as d.sub.i. Such multiple sampling locations 310, 410, 411 allow statistical operations on a similar type of sampled data from a plurality of different sample locations. For example, the different sample locations allow a similar type of data to be collected at a plurality of locations in a lot.) comprises automatic processing of the data to generate a database comprising both raw data and categorized data, where correlation is performed against the raw data and the categorized data from both the development ecosystem and the non-development ecosystem (Toyoshima et al. 2005/0187737 [0024 - FIG. 2 is a flowchart illustrating … a method for collecting and correlating production and non-production data for analysis] FIG. 2 is a flowchart illustrating generally, among other things, a method for collecting and correlating production and non-production data for analysis according to an embodiment of the present invention.).
Toyoshima et al. 2005/0187737 may not expressly disclose the “raw and uncategorized data” features, however, Duncan et al. 2017/0220943 teaches these features as follows (Duncan et al. 2017/0220943 [0009 - statistical, pattern recognition, and machine learning tools to support the discovery of patterns, trends and rules that lie within the data] To satisfy this need, a new interdisciplinary field appeared. It encompasses statistical, pattern recognition, and machine learning tools to support the discovery of patterns, trends and rules that lie within the data. [0011 - machine-learning algorithms are applied to extract non-obvious knowledge from data] In this approach, machine-learning algorithms are applied to extract non-obvious knowledge from data to reduce, or even eliminate, the above-mentioned drawbacks. The methods also extend the possibilities of discovering information, trends and patterns by using richer model representations (e.g. decision rules, decision trees) than standard statistical methods, and are therefore well suited for making the results more comprehensible to non-technically oriented business users. [0301 - machine-learning techniques available for each type of CRM model] There are numerous machine-learning techniques available for each type of CRM model. The expert system 11 may choose algorithms using methods knowledge base 42 based on data characteristics and business requirements as determined in stages 100 and 200 of the process shown in FIG. 3. Exemplary algorithms include association rule, decision tree, genetic algorithm, neural networks, K-Nearest neighbor, and linear/logistic regression, as outlined below. [0048 - production environment – interpreted as non-development ecosystem] Embodiments of the presently disclosed methods and systems bring scientific and engineering dimensions to automated knowledge extraction and application to business users in the retail sector, with minimal help from various human experts; furthermore, as the area of analytical CRM (for example) represents a dynamic environment with continuous need for repeated analyses, the automation of processes present in a production environment is key to meeting an entity's business objectives. Embodiments focus on business users and other decision makers, enabling them to develop data models via a user-friendly and intuitive GUI, and through a cloud-computing platform. As a result, knowledge extraction and application become more fully integrated in business environments and their decision processes. [0091- production environment – interpreted as non-development ecosystem] The business user is an active executor of the production stage 2. The production stage 2 provides the business user with updated models for its production environment when data changes, or the business user's strategy, warrant it. [0336 – raw input data] In certain embodiments, the system 810 may have functionality for generating and applying one or more models to data acquired by the system. For example, the retail network management system 850 may comprise an expert system 11 as described above. The expert system 11 determines, based on user input, the analysis objectives, applies preprocessing 200 to the raw input data acquired by system 810 to obtain processed input data, generates and validates 300 one or more models, and applies 400 the model(s) to the processed input data to produce numerical and graphical output which is displayable on a display 822 of a device of a user 832. [0372 - categorized data] Just like with the sales force CRM application, the use of a digital application over a paper form greatly enhances the reliability of the data obtained. Also, similarly, data based on categories and limited multiple choice options over free text fields is privileged whenever possible; indeed, from a data quality point of view, categorized data is easier to control, whereas free text fields may need additional post-processing, given that data may be entered inconsistently by customers (e.g. address fields). [0012 - applying the patterns, trends, relationships and correlations that have lain undiscovered within large amounts of data] Retailers should, in theory, be able to improve their businesses by applying the patterns, trends, relationships and correlations that have lain undiscovered within large amounts of data. [0184 - Expert system 11 systematically computes the correlation (grade of relation) between pairs of input variables in order to identify variables that have a low correlation with other variables in the dataset] Expert system 11 systematically computes the correlation (grade of relation) between pairs of input variables in order to identify variables that have a low correlation with other variables in the dataset, for the user to possibly eliminate. [0320 - Association rule finds interesting correlation relationships among a large set of data items] Association rule finds interesting correlation relationships among a large set of data items. A typical application would be market basket analysis, which analyzes customers' buying habits by finding associations between the different items that customers place in their shopping baskets.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Toyoshima et al. 2005/0187737 to include the features as taught by Duncan et al. 2017/0220943. One of ordinary skill in the art would have been motivated to do so to utilize well known data analysis and ML techniques for analyzing data which should prove to improve user experience, maximize profits, and optimize revenue.
18/908,603 – Claim 10. The computer program product of claim 8, wherein the machine-learning performed on the combined set of the data from both the development ecosystem and the non-development ecosystem comprises automatic processing of the data to generate a database comprising both raw data and categorized data, where correlation is performed against the raw data and the categorized data from both the development ecosystem and the non-development ecosystem.
18/908,603 – Claim 23. (New) The system of claim 21, wherein the machine-learning performed on the combined set of the data from both the development ecosystem and the non-development ecosystem comprises automatic processing of the data to generate a database comprising both raw data and categorized data, where correlation is performed against the raw data and the categorized data from both the development ecosystem and the non-development ecosystem.
Claims 10 and 23, have similar limitations as of Claim 3, therefore they are REJECTED under the same rationale as Claim 3.
Claims 4, 11, 24 are rejected under 35 U.S.C. 103 as being unpatentable over: Toyoshima et al. 2005/0187737; in view of Bui et al. 2021/0200760; in further view of Sarferaz 2021/0342738; in even further view of Natarajan et al. 2020/0174774.
18/908,603 – Claim 4. Toyoshima et al. 2005/0187737 further teaches The method of claim 1, wherein the first data from the development ecosystem and the second data from the non-development ecosystem (Toyoshima et al. 2005/0187737 [0006 – performing data analysis on data gathered…] An embodiment of the present invention is a method for performing data analysis on data gathered in an electronic device manufacturing process. The method includes collecting production data, collecting non-production data, keying the production data, keying the non-production data, combining the production data and the non-production data into a single data set, and analyzing single data-set. An embodiment of the present invention further includes performing calculations on the production data and the non-production data. Collection of production data includes at least one of collecting parametric production data, collecting film thickness data, critical dimension data, and any other data that is relevant to the production process and its condition. Collection of non-production data includes at least one of collecting non-production data from a single data source at a single source location or from a plurality of locations, and collecting non-production data from a single data source with some temporal periodicity. In an embodiment, the temporal periodicity is fixed. In an embodiment, the temporal periodicity is not fixed. [0037 – Data is collected from both production and non-production sources…] FIG. 2 presents a method, according to an embodiment of the present invention, for combining the data taken during the manufacturing process for the purposes of data analysis. Data is collected from both production and non-production sources, at 201, 202, 203. Non-production sources may include, but not be limited to, thermometers providing temperature readings 201a, barometers providing pressure measurements 201c, staff productivity recording systems, electrical measurements 201b, equipment control data, metrology tool calibration data, etc. Non-production sources also include sources that provide any other data relevant to the production environment, where the production environment may include, but not be limited to, the facility where the production is performed, a larger physical gathering of multiple production facilities in a single geographical location, etc. Production sources may further be divided into online production sources and offline production sources. Online production sources may include, but not be limited to, critical dimension readings 202a, chemical sampling 202b, surface readings, 202c, etc. Offline production sources may include, but not be limited to, offline circuit testing 203a, photomask inspection 203b, operating temperature readings 203c, etc. At 204 and 205 the non-production data and the production data are keyed to some value, respectively. In an embodiment, the data is keyed to a temporal, or date-time, value. In an embodiment, at 204, that data is keyed with a date-time value. In an embodiment, at 205, that data is keyed with production lot data, which may be further keyed with date-time values associated with the time that a particular production lot began a manufacturing process as well as the time that the particular production lot completed the manufacturing process. In an embodiment, non-production data is measured at a single location, such as depicted in FIG. 3. In an embodiment, the sampling location for the data measurement 310 is separated some distance 320 from the process equipment 301. In an embodiment, production data is measured at a plurality of locations, such as depicted in FIG. 4. The process equipment 301 is separated by a variable distance from the locations where data is being measured. For sampling location 1 at 310, the distance 320 can be defined as d.sub.1. For sampling location 2 at 410, the distance 420 can be defined as d.sub.2. There may be many locations where the data is being measured. For all sampling locations I at 411, the distance 421 can be defined as d.sub.i. Such multiple sampling locations 310, 410, 411 allow statistical operations on a similar type of sampled data from a plurality of different sample locations. For example, the different sample locations allow a similar type of data to be collected at a plurality of locations in a lot.) correspond to event hierarchies, the event hierarchies comprise stages of funneling for receiving and analyzing the first and second data (Toyoshima et al. 2005/0187737 [0006 - a method for performing data analysis on data gathered] An embodiment of the present invention is a method for performing data analysis on data gathered in an electronic device manufacturing process. The method includes collecting production data, collecting non-production data, keying the production data, keying the non-production data, combining the production data and the non-production data into a single data set, and analyzing single data-set. An embodiment of the present invention further includes performing calculations on the production data and the non-production data. Collection of production data includes at least one of collecting parametric production data, collecting film thickness data, critical dimension data, and any other data that is relevant to the production process and its condition. Collection of non-production data includes at least one of collecting non-production data from a single data source at a single source location or from a plurality of locations, and collecting non-production data from a single data source with some temporal periodicity. In an embodiment, the temporal periodicity is fixed. In an embodiment, the temporal periodicity is not fixed.), where a first event hierarchy corresponds to the first data from the development ecosystem and a second event hierarchy corresponds to the second data from the non-development ecosystem (Toyoshima et al. 2005/0187737 [0037 – Data is collected from both production and non-production sources…] FIG. 2 presents a method, according to an embodiment of the present invention, for combining the data taken during the manufacturing process for the purposes of data analysis. Data is collected from both production and non-production sources, at 201, 202, 203. Non-production sources may include, but not be limited to, thermometers providing temperature readings 201a, barometers providing pressure measurements 201c, staff productivity recording systems, electrical measurements 201b, equipment control data, metrology tool calibration data, etc. Non-production sources also include sources that provide any other data relevant to the production environment, where the production environment may include, but not be limited to, the facility where the production is performed, a larger physical gathering of multiple production facilities in a single geographical location, etc. Production sources may further be divided into online production sources and offline production sources. Online production sources may include, but not be limited to, critical dimension readings 202a, chemical sampling 202b, surface readings, 202c, etc. Offline production sources may include, but not be limited to, offline circuit testing 203a, photomask inspection 203b, operating temperature readings 203c, etc. At 204 and 205 the non-production data and the production data are keyed to some value, respectively. In an embodiment, the data is keyed to a temporal, or date-time, value. In an embodiment, at 204, that data is keyed with a date-time value. In an embodiment, at 205, that data is keyed with production lot data, which may be further keyed with date-time values associated with the time that a particular production lot began a manufacturing process as well as the time that the particular production lot completed the manufacturing process. In an embodiment, non-production data is measured at a single location, such as depicted in FIG. 3. In an embodiment, the sampling location for the data measurement 310 is separated some distance 320 from the process equipment 301. In an embodiment, production data is measured at a plurality of locations, such as depicted in FIG. 4. The process equipment 301 is separated by a variable distance from the locations where data is being measured. For sampling location 1 at 310, the distance 320 can be defined as d.sub.1. For sampling location 2 at 410, the distance 420 can be defined as d.sub.2. There may be many locations where the data is being measured. For all sampling locations I at 411, the distance 421 can be defined as d.sub.i. Such multiple sampling locations 310, 410, 411 allow statistical operations on a similar type of sampled data from a plurality of different sample locations. For example, the different sample locations allow a similar type of data to be collected at a plurality of locations in a lot.), and the first event hierarchy has different stages compared to the second event hierarchy (Toyoshima et al. 2005/0187737 [0037; 0054]).
Toyoshima et al. 2005/0187737 may not expressly disclose the “hierarchical structure” and “stages” features, however, Natarajan et al. 2020/0174774 teaches these features as follows (Natarajan et al. 2020/0174774 [0032 – first data and second data] In some implementations, the metric platform may select one or more of the data processing techniques to process the historical application creation data based on a source of the data. For example, if the historical application creation data is received from a first source, the metric platform may utilize a first data processing technique to process the historical application creation data, if the historical application creation data is received from a second source, the metric platform may utilize a second data processing technique to process the historical application creation data, and/or the like. As shown in FIG. 1E, and by reference number 125, the metric platform may receive new application creation data associated with a new application (e.g., data associated with creation of the new application). In some implementations, the new application may be newly written and the new application data may include data identifying source code of the new application, a development environment in which the new application may be developed, a testing environment in which the new application may be tested, a production environment in which the new application may be deployed, and/or the like. [0028 - hierarchical structure] In some implementations, the metric platform may utilize a natural language processing technique, a computational linguistics technique, a text analysis technique, and/or the like, with the historical application creation data in order to make the historical application creation data (e.g., the processed historical application creation data) analyzable. For example, the metric platform may apply natural language processing to interpret the historical application creation data and generate additional data associated with the potential meaning of data within the historical application creation data. Natural language processing involves techniques performed (e.g., by a computer system) to analyze, understand, and derive meaning from human language in a useful way. Rather than treating text like a mere sequence of symbols, natural language processing considers a hierarchical structure of language (e.g., several words can be treated as a phrase, several phrases can be treated as a sentence, and the words, phrases, and/or sentences convey ideas that can be interpreted). Natural language processing can be applied to analyze text, allowing machines to understand how humans speak, enabling real world applications such as automatic text summarization, sentiment analysis, topic extraction, named entity recognition, parts-of-speech tagging, relationship extraction, stemming, and/or the like. [0032 – first data and second data] In some implementations, the metric platform may select one or more of the data processing techniques to process the historical application creation data based on a source of the data. For example, if the historical application creation data is received from a first source, the metric platform may utilize a first data processing technique to process the historical application creation data, if the historical application creation data is received from a second source, the metric platform may utilize a second data processing technique to process the historical application creation data, and/or the like. [0042 - development systems] In some implementations, the metric platform may receive updated historical application creation data in real time and/or periodically (e.g., from one or more software development systems associated with the applications currently being developed). In such implementations, the metric platform may update the trained machine learning model based on the updated historical application creation data. [0044 - a development environment in which the new application may be developed, a testing environment in which the new application may be tested, a production environment in which the new application may be deployed] As shown in FIG. 1E, and by reference number 125, the metric platform may receive new application creation data associated with a new application (e.g., data associated with creation of the new application). In some implementations, the new application may be newly written and the new application data may include data identifying source code of the new application, a development environment in which the new application may be developed, a testing environment in which the new application may be tested, a production environment in which the new application may be deployed, and/or the like. [0062 - several different stages…] In this way, several different stages of the process for predicting metrics associated with an application development process may be automated with a machine learning model, which may remove human subjectivity and waste from the process, and which may improve speed and efficiency of the process and conserve computing resources (e.g., processing resources, memory resources, and/or the like). Furthermore, implementations described herein use a rigorous, computerized process to perform tasks or roles that were not previously performed, or were previously performed using subjective human intuition or input. For example, currently there does not exist a technique that utilizes a machine learning model to predict metrics for an application development process in the manner described herein. Further, automating the process for predicting metrics associated with an application development process conserves computing resources (e.g., processing resources, memory resources, and/or the like) that would otherwise be wasted in using a less efficient technique to predict such metrics. In some implementations, the metric platform may utilize a natural language processing technique, a computational linguistics technique, a text analysis technique, and/or the like, with the historical application creation data in order to make the historical application creation data (e.g., the processed historical application creation data) analyzable. For example, the metric platform may apply natural language processing to interpret the historical application creation data and generate additional data associated with the potential meaning of data within the historical application creation data. Natural language processing involves techniques performed (e.g., by a computer system) to analyze, understand, and derive meaning from human language in a useful way. Rather than treating text like a mere sequence of symbols, natural language processing considers a hierarchical structure of language (e.g., several words can be treated as a phrase, several phrases can be treated as a sentence, and the words, phrases, and/or sentences convey ideas that can be interpreted). Natural language processing can be applied to analyze text, allowing machines to understand how humans speak, enabling real world applications such as automatic text summarization, sentiment analysis, topic extraction, named entity recognition, parts-of-speech tagging, relationship extraction, stemming, and/or the like.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Toyoshima et al. 2005/0187737 to include the features as taught by Natarajan et al. 2020/0174774. One of ordinary skill in the art would have been motivated to do so to utilize well known data analysis and ML techniques for analyzing data which should prove to improve user experience, maximize profits, and optimize revenue. 18/908,603 – Claim 11. The computer program product of claim 8, wherein the first data from the development ecosystem and the second data from the non-development ecosystem correspond to event hierarchies, the event hierarchies comprise stages of funneling for receiving and analyzing the first and second data, where a first event hierarchy corresponds to the first data from the development ecosystem and a second event hierarchy corresponds to the second data from the non-development ecosystem, and the first event hierarchy has different stages compared to the second event hierarchy. 18/908,603 – Claim 24. (New) The system of claim 21, wherein the first data from the development ecosystem and the second data from the non-development ecosystem correspond to event hierarchies, the event hierarchies comprise stages of funneling for receiving and analyzing the first and second data, where a first event hierarchy corresponds to the first data from the development ecosystem and a second event hierarchy corresponds to the second data from the non-development ecosystem, and the first event hierarchy has different stages compared to the second event hierarchy.
Claims 11 and 24, have similar limitations as of Claim 4, therefore they are REJECTED under the same rationale as Claim 4.
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over: Toyoshima et al. 2005/0187737; in view of Bui et al. 2021/0200760; in further view of Sarferaz 2021/0342738; in even further view of White et al. 2020/0126117.
18/908,603 – Claim 5. Toyoshima et al. 2005/0187737 further teaches The method of claim 1, further comprising: receiving third data from a user ecosystem (Toyoshima et al. 2005/0187737 [0006 – performing data analysis on data gathered…] An embodiment of the present invention is a method for performing data analysis on data gathered in an electronic device manufacturing process. The method includes collecting production data, collecting non-production data, keying the production data, keying the non-production data, combining the production data and the non-production data into a single data set, and analyzing single data-set. An embodiment of the present invention further includes performing calculations on the production data and the non-production data. Collection of production data includes at least one of collecting parametric production data, collecting film thickness data, critical dimension data, and any other data that is relevant to the production process and its condition. Collection of non-production data includes at least one of collecting non-production data from a single data source at a single source location or from a plurality of locations, and collecting non-production data from a single data source with some temporal periodicity. In an embodiment, the temporal periodicity is fixed. In an embodiment, the temporal periodicity is not fixed. [0037 – Data is collected from both production and non-production sources…] FIG. 2 presents a method, according to an embodiment of the present invention, for combining the data taken during the manufacturing process for the purposes of data analysis. Data is collected from both production and non-production sources, at 201, 202, 203. Non-production sources may include, but not be limited to, thermometers providing temperature readings 201a, barometers providing pressure measurements 201c, staff productivity recording systems, electrical measurements 201b, equipment control data, metrology tool calibration data, etc. Non-production sources also include sources that provide any other data relevant to the production environment, where the production environment may include, but not be limited to, the facility where the production is performed, a larger physical gathering of multiple production facilities in a single geographical location, etc. Production sources may further be divided into online production sources and offline production sources. Online production sources may include, but not be limited to, critical dimension readings 202a, chemical sampling 202b, surface readings, 202c, etc. Offline production sources may include, but not be limited to, offline circuit testing 203a, photomask inspection 203b, operating temperature readings 203c, etc. At 204 and 205 the non-production data and the production data are keyed to some value, respectively. In an embodiment, the data is keyed to a temporal, or date-time, value. In an embodiment, at 204, that data is keyed with a date-time value. In an embodiment, at 205, that data is keyed with production lot data, which may be further keyed with date-time values associated with the time that a particular production lot began a manufacturing process as well as the time that the particular production lot completed the manufacturing process. In an embodiment, non-production data is measured at a single location, such as depicted in FIG. 3. In an embodiment, the sampling location for the data measurement 310 is separated some distance 320 from the process equipment 301. In an embodiment, production data is measured at a plurality of locations, such as depicted in FIG. 4. The process equipment 301 is separated by a variable distance from the locations where data is being measured. For sampling location 1 at 310, the distance 320 can be defined as d.sub.1. For sampling location 2 at 410, the distance 420 can be defined as d.sub.2. There may be many locations where the data is being measured. For all sampling locations I at 411, the distance 421 can be defined as d.sub.i. Such multiple sampling locations 310, 410, 411 allow statistical operations on a similar type of sampled data from a plurality of different sample locations. For example, the different sample locations allow a similar type of data to be collected at a plurality of locations in a lot.), wherein the user ecosystem corresponds to one or more users that use the technology product; receiving fourth data from a customer relations ecosystem, wherein the customer relations ecosystem corresponds to a customer relations system related to the technology product (Toyoshima et al. 2005/0187737 [0067 - invention may be implemented as a program product for use with an electronic device] As was described in detail above, aspects of an embodiment pertain to specific apparatus and method elements implementable on a computer or other electronic device. In another embodiment, the invention may be implemented as a program product for use with an electronic device. The programs defining the functions of this embodiment may be delivered to an electronic device via a variety of signal-bearing media, which include, but are not limited to:); and wherein the combined set of data comprises the first data, the second data, the third data, and the fourth data (Toyoshima et al. 2005/0187737 [0005 - a technique to quickly combine data from production and non-production sources into a combined set of data for quicker analysis] What is needed is a technique to quickly combine data from production and non-production sources into a combined set of data for quicker analysis. [0006-0008]).
Toyoshima et al. 2005/0187737 may not expressly disclose the “customer relations” features, however, White et al. 2020/0126117 teaches these features as follows (White et al. 2020/0126117 [0025 - customer relationship management system…] Furthermore, detection component 110 can extract another subset of first data representing a user browsing an e-commerce website application for mini-van vehicle accessories. For instance, detection component 110 can extract data from data stores that store dealership website data, third party shopping data, or any tangential or related online or offline consumer behavioral data or indicator of intent to purchase a relevant good or service. In an aspect, processor 112 can execute matching component 120 to integrate the subsets of first data and correlate them (based on identification data) as corresponding to the same user. Furthermore, matching component 120 can compare the combined subsets of first data to second data representing existing customers of an automotive dealership stored in a data management system implemented on a dealership data store and determine that the subsets of first data (e.g., newly generated online and offline data) correspond to an existing customer (e.g., user device or user account of an existing customer). For instance, offline data can include wireless access point data, offline geographic positioning data, consumer data, public record data, credit data, credit score data, predictive income data, vehicle ownership data, pre-screen offer data, and other information that is not online data. Furthermore, in an aspect, online data can include data associated with web browsing, click-through data, click stream data, cookies, mobile ad identifiers (MAIDs), e-mail account information, online registration data, online site usage data (e.g., social media usage data), transaction data, mobile app data and other such data. Accordingly, processor 112 can execute notification component 130 to transmit notification data representing the detection and identification of an existing customer having interest in shopping for a vehicle. Furthermore, notification component 130 can transmit notification data to one or more device (e.g., dealership sales personnel smart phone) or one or more data store (e.g., dealership management system data store or dealership customer relationship management system data store). In another aspect, notification component 130 can transmit notification data to one or more device or data store that represents a matching event has not occurred, such that an indication that a subset of first data does not match an existing customer data has occurred.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Toyoshima et al. 2005/0187737 to include the features as taught by White et al. 2020/0126117. One of ordinary skill in the art would have been motivated to do so to utilize well known data analysis and ML techniques for analyzing data which should prove to improve user experience, maximize profits, and optimize revenue.
18/908,603 – Claim 12. The computer program product of claim 8, further comprising: receiving third data from a user ecosystem, wherein the user ecosystem corresponds to one or more users that use the technology product; receiving fourth data from a customer relations ecosystem, wherein the customer relations ecosystem corresponds to a customer relations system related to the technology product; and wherein the combined set of data comprises the first data, the second data, the third data, and the fourth data.
Claim 12, has similar limitations as of Claim 5, therefore it is REJECTED under the same rationale as Claim 5.
Claims 6, 7, 13, 14, 25, 26 are rejected under 35 U.S.C. 103 as being unpatentable over: Toyoshima et al. 2005/0187737; in view of Bui et al. 2021/0200760; in further view of Sarferaz 2021/0342738; in even further view of Sachan et al. 2020/0293946.
18/908,603 – Claim 6. Toyoshima et al. 2005/0187737 further teaches The method of claim 1, wherein events from the combined set of data are analyzed to identify an incident (Toyoshima et al. 2005/0187737 [0006 – performing data analysis on data gathered…] An embodiment of the present invention is a method for performing data analysis on data gathered in an electronic device manufacturing process. The method includes collecting production data, collecting non-production data, keying the production data, keying the non-production data, combining the production data and the non-production data into a single data set, and analyzing single data-set. An embodiment of the present invention further includes performing calculations on the production data and the non-production data. Collection of production data includes at least one of collecting parametric production data, collecting film thickness data, critical dimension data, and any other data that is relevant to the production process and its condition. Collection of non-production data includes at least one of collecting non-production data from a single data source at a single source location or from a plurality of locations, and collecting non-production data from a single data source with some temporal periodicity. In an embodiment, the temporal periodicity is fixed. In an embodiment, the temporal periodicity is not fixed. [0037 – Data is collected from both production and non-production sources…] FIG. 2 presents a method, according to an embodiment of the present invention, for combining the data taken during the manufacturing process for the purposes of data analysis. Data is collected from both production and non-production sources, at 201, 202, 203. Non-production sources may include, but not be limited to, thermometers providing temperature readings 201a, barometers providing pressure measurements 201c, staff productivity recording systems, electrical measurements 201b, equipment control data, metrology tool calibration data, etc. Non-production sources also include sources that provide any other data relevant to the production environment, where the production environment may include, but not be limited to, the facility where the production is performed, a larger physical gathering of multiple production facilities in a single geographical location, etc. Production sources may further be divided into online production sources and offline production sources. Online production sources may include, but not be limited to, critical dimension readings 202a, chemical sampling 202b, surface readings, 202c, etc. Offline production sources may include, but not be limited to, offline circuit testing 203a, photomask inspection 203b, operating temperature readings 203c, etc. At 204 and 205 the non-production data and the production data are keyed to some value, respectively. In an embodiment, the data is keyed to a temporal, or date-time, value. In an embodiment, at 204, that data is keyed with a date-time value. In an embodiment, at 205, that data is keyed with production lot data, which may be further keyed with date-time values associated with the time that a particular production lot began a manufacturing process as well as the time that the particular production lot completed the manufacturing process. In an embodiment, non-production data is measured at a single location, such as depicted in FIG. 3. In an embodiment, the sampling location for the data measurement 310 is separated some distance 320 from the process equipment 301. In an embodiment, production data is measured at a plurality of locations, such as depicted in FIG. 4. The process equipment 301 is separated by a variable distance from the locations where data is being measured. For sampling location 1 at 310, the distance 320 can be defined as d.sub.1. For sampling location 2 at 410, the distance 420 can be defined as d.sub.2. There may be many locations where the data is being measured. For all sampling locations I at 411, the distance 421 can be defined as d.sub.i. Such multiple sampling locations 310, 410, 411 allow statistical operations on a similar type of sampled data from a plurality of different sample locations. For example, the different sample locations allow a similar type of data to be collected at a plurality of locations in a lot.), and the incident is analyzed to identify a ticket that is assigned within a software organization to address the ticket (Toyoshima et al. 2005/0187737 [0061] The computer 702 may be implemented using any suitable hardware and/or software, such as a personal computer or other electronic computing device. Portable computers, laptop or notebook computers, PDAs (Personal Digital Assistants), two-way alphanumeric pagers, keypads, portable telephones, appliances with a computing unit, pocket computers, and mainframe computers are examples of other possible configurations of the computer 702. The hardware and software depicted in FIG. 7 may vary for specific applications and may include more or fewer elements than those depicted. For example, other peripheral devices such as audio adapters, or chip programming devices, such as EPROM (Erasable Programmable Read-Only Memory) programming devices may be used in addition to or in place of the hardware already depicted.).
Toyoshima et al. 2005/0187737 may not expressly disclose the “incident ticket” features, however, Sachan et al. 2020/0293946 teaches these features as follows (Sachan et al. 2020/0293946 [0029 - If the incident ticket is an actionable incident ticket, a machine learning based incident ticket creation and routing model may be utilized to determine appropriate routing information for the incident ticket] The apparatuses, methods, and non-transitory computer readable media disclosed herein may operate in a reactive mode and/or a proactive mode. In the reactive mode, an indication of an incident (or an issue associated with an incident) that is being experienced by a user may be received. Metadata associated with the incident may be analyzed to generate an incident ticket, and to determine a state (e.g., new or existing) of the incident. If the incident is new, a determination may be made as to whether the incident is actionable or non-actionable. This determination may be made by utilizing a machine learning based incident classification model that is trained on historical incident tickets, where each such historical incident tickets may be labeled as actionable or non-actionable. Thus, the machine learning based incident classification model may be utilized to determine a type of an incident ticket, and to take action such as closure of the incident ticket in the event of a non-actionable ticket, as well as prediction of a nature of the incident ticket. If the incident ticket is an actionable incident ticket, a machine learning based incident ticket creation and routing model may be utilized to determine appropriate routing information for the incident ticket. In this regard, the machine learning based incident ticket creation and routing model may learn incident ticket routing patterns from historical incident ticket assignments, and determine a correct assignment group for a new incident ticket based on prior assignment of similar incident tickets for the assigned group. [0171 - incident ticket router 112 may determine whether a new incident ticket will be actionable or non-actionable type of ticket, for example by using the machine learning based incident classification model] The incident ticket router 112 may provide for the reduction of time consumed and maintenance of incident tickets that require no user intervention for their resolution. The incident ticket router 112 may determine whether a new incident ticket will be actionable or non-actionable type of ticket, for example by using the machine learning based incident classification model 114. The machine learning based incident classification model 114 may be trained by utilizing labeled historical incident tickets, where such historical incident tickets may be labeled as actionable or non-actionable. In real time, the machine learning based incident classification model 114 may be utilized to determine the type of incident ticket, and take action such as closure of the incident ticket in the event of a non-actionable incident ticket, and to further predict the nature of the associated incident ticket such as category, subcategory, configuration item, severity, assignment group in the event of an actionable incident ticket. An actionable incident ticket may include an issue that requires some human (e.g., manual) intervention to fix an issue. A non-actionable incident ticket may include an issue/incident that requires no human intervention. Therefore, if a given incident ticket is of a non-actionable nature, then the incident ticket may not need to be logged in. However, an actionable incident ticket may need to be logged. While logging or creating an incident ticket, some mandatory information specific to the incident ticket may need to be completed. Since the incident ticket logging (or creation) process may be automated as disclosed herein, the mandatory information of the incident ticket may be predicted, and may include a “category” of the incident ticket, a “subcategory” of the incident ticket, an “impacted application” which may also be referred to as a configuration item, a “severity” of the incident ticket, etc.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Toyoshima et al. 2005/0187737 to include the features as taught by Sachan et al. 2020/0293946. One of ordinary skill in the art would have been motivated to do so to utilize well known data analysis and ML techniques for analyzing data which should prove to improve user experience, maximize profits, and optimize revenue.
18/908,603 – Claim 13. The computer program product of claim 8, wherein events from the combined set of data are analyzed to identify an incident, and the incident is analyzed to identify a ticket that is assigned within a software organization to address the ticket.
18/908,603 – Claim 25. (New) The system of claim 21, wherein events from the combined set of data are analyzed to identify an incident, and the incident is analyzed to identify a ticket that is assigned within a software organization to address the ticket.
Claims 13 and 25, have similar limitations as of Claim 6, therefore they are REJECTED under the same rationale as Claim 6.
18/908,603 – Claim 7. Toyoshima et al. 2005/0187737 may not expressly teach the following, however, Sachan et al. 2020/0293946 teaches The method of claim 6, wherein the ticket is created based upon clustering of multiple incidents (Sachan et al. 2020/0293946 [0196 - incident ticket router … may identify a similar behavior or pattern that exists between the new incident identified in the new incident ticket and historical incidents (that are member of the clusters…). The pattern may include a set of one or more features of an incident, such as name, severity, application, issue type, etc. In order to find similar behavior existing between the incident and the identified cluster members, a determination may be made as to how many incidents have similar severity…] At block 1406, the incident ticket router 112 may identify a similar behavior or pattern that exists between the new incident identified in the new incident ticket and historical incidents (that are member of the clusters identified at block 1404). The pattern may include a set of one or more features of an incident, such as name, severity, application, issue type, etc. In order to find similar behavior existing between the incident and the identified cluster members, a determination may be made as to how many incidents have similar severity (e.g., impact of the incident such as Sev1, Sev2, Sev3, etc.), how many incidents are impacting similar applications such as App1, App2, App3, etc., how many incidents have similar issue type such as network issues, database issues, etc. Further, all incident attributes in the repository may be compared to find common patterns. The top three most occurring common behaviors may be considered as the dominant patterns.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Toyoshima et al. 2005/0187737 to include the features as taught by Sachan et al. 2020/0293946. One of ordinary skill in the art would have been motivated to do so to utilize well known data analysis and ML techniques for analyzing data which should prove to improve user experience, maximize profits, and optimize revenue.
18/908,603 – Claim 14. The computer program product of claim 13, wherein the ticket is created based upon clustering of multiple incidents.
18/908,603 – Claim 26. (New) The system of claim 25, wherein the ticket is created based upon clustering of multiple incidents.
Claims 14 and 26, have similar limitations as of Claim 7, therefore they are REJECTED under the same rationale as Claim 7.
Examiner’s Response to Arguments
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §112
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §101
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping 35 USC 101 rejection including Applicant’s amendments, arguments, lack of abstract idea, and practical integration.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claims 1-15, on page(s) 6-12 of Applicant’s Remarks (dated 12/27/2016), Applicants traverse the 35 USC §101 rejections arguing the following:
Examiner’s Response: Claim Rejections – 35 USC § 102 / § 103
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping prior-art rejection including Applicant’s amendments and arguments and unique combination of features and elements not taught by the prior-art without hindsight reasoning.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claim X, on page(s) 8-9 of Applicant’s Remarks / After Final Amendments (dated 07/15/2011), Applicant(s) argues that the cited reference(s) (Ellis and Vandermolen) fails to teach, describe, or suggest the amended features. Specifically, Applicant(s) argues that cited reference(s) do not teach, describe, or suggest the following: . With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
PERTINENT PRIOR ART – Patent Literature
The prior-art made of record and considered pertinent to applicant's disclosure.
Venkataraman et al. US 10,459,951: Abstract - The present disclosure relates to a method and system for determining automation sequences for resolution of an incident ticket by an automation system. The automation system retrieves data associated with plurality of incident tickets received from a ticketing system during predefined time duration and groups the plurality of incident tickets into one or more clusters based on the data. The automation system receives a plurality of user actions associated with the plurality of incident tickets performed across a plurality of user devices and identifies similarity among sequences of the plurality of user actions for each ticket cluster. Based on the similarity, the automation system groups the sequences of the plurality of user actions into one or more bucket and determines automation sequences for resolution of the incident ticket by correlating the data associated with plurality of incident tickets with one or more buckets of the sequences.
PERTINENT PRIOR ART – Non-Patent Literature (NPL)
The NPL prior-art made of record and considered pertinent to applicant's disclosure.
E. F. Coutinho, I. Santos and C. I. Moreira Bezerra, "A Software Ecosystem for a Virtual Learning Environment: SOLAR SECO," 2017 IEEE/ACM Joint 5th International Workshop on Software Engineering for Systems-of-Systems and 11th Workshop on Distributed Software Development, Software Ecosystems and Systems-of-Systems (JSOS), Buenos Aires, Argentina, 2017, pp. 41-47, doi: 10.1109/JSOS.2017.2.
THIS ACTION IS MADE FINAL
Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
THIS ACTION IS MADE FINAL
Applicant’s amendment necessitated new grounds of rejection and FINAL Rejection.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/MATTHEW T SITTNER/
Primary Examiner, Art Unit 3629b