DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Argument:
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Response:
Even if one of ordinary skill understood what was being referred to, there is no basis to understand the scope of “the degree and directness” as figure 5 shows simple connections.
What would make the connectivity of figure 5 “increased”?
Argument:
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Response:
The scope conveyed by the language is unclear. That’s true in this application and the any other application using the language.
Argument:
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Response:
The examiner has withdrawn this aspect of the rejection.
Argument:
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Response:
The exact language from the specification is unclear to begin with, so adding it to the claim doesn’t help.
The 112(b) rejection has revised based on amendment. The language that caused the previous rejection has been removed.
Argument:
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Response:
There is no disclosure of a “specific improvement”, beyond the assertion of improved properties. Furthermore, MCM architecture with increased connectivity is well known in the art.
See McLellan (US 20180096938 A1) paragraph 3:
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MPEP 2106.05(f) states: “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components”
Given as disclosed by McLellan that MCMs with increased interconnects are conventional, it would be unreasonable that their use in their ordinary capacity
Argument:
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Response:
The specification gives no clear explanation as to how the benefits are achieved. Moreover, as established above, the MCM architectures with increased interconnects are regarded as conventional.
So their use in their ordinary capacity does not provide a practical application or significantly more.
Argument:
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Response:
Again as established above, the alleged technical improvement is not clearly described. And if it were, the particular architecture in question is regarded as conventional and its usage in the claimed invention is in its ordinary capacity.
Argument:
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Response:
Again as established above, the alleged technical improvement is not clearly described. And if it were, the particular architecture in question is regarded as conventional and its usage in the claimed invention is in its ordinary capacity.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 16-25 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. All claims contain the language “single semiconductor platform”. This is based on applicant’s paragraph 55, which states the “term single semiconductor platform may also refer to multi-chip modules with increased connectivity which simulate on-chip modules with increased connectivity which simulate on-chip operation term single semiconductor platform may also refer to multi-chip modules with increased connectivity which simulate on-chip modules with increased connectivity which simulate on-chip operation”. The examiner is interpreting this as a special definition. Paragraph 55 is based on boilerplate that has been used 1000+ times since the year 2000 (see US 20100123729 A1 and US-20200175392-A1 US-20190147884-A1 US-20180292825-A1 US-20150355996-A1 US-20150194157-A1 US-20140126275-A1 US-20130297631-A1 US-20130038702-A1 US-20120101871-A1 US-20110181720-A1 US-20090259827-A1). The language about multi-chip modules simulating on-chip modules on its face does not make sense. There is no explanation of what the on-chip modules are and what exactly is being simulated. It is also noted that the multi-chip modules are supposed to provide various improvements per [55], but the improvements are not described. Paragraph 55 also only refers “modules” in the context of the single semiconductor platform.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 16-25 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. All claims contain the language “increased connectivity”. This is a term of degree and there is no basis given in the specification to determine what constitutes “increased” connectivity relative to normal connectivity.
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 16-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
All claims recite subject matter falling within one of the four categories of invention (step 1).
Claims 16-25 recite
16. (New) A computer system that estimates metric forecasts associated with a plurality of related entities including at least a primary entity and a secondary entity with more accuracy by training and applying a metric forecast entity relationship machine learning model, the computer system comprising:
a data communication network;
a primary entity metric device that generates a first primary entity metric forecast for a related entity;
a secondary entity metric device that generates a first secondary entity metric forecast for a related entity;
a tertiary entity metric device that generates a first tertiary entity metric forecast for a related entity;
a plurality of historical data storages, wherein each of the plurality of historical data storages correspond to historical data associated with a related entity for a respective entity metric device and each of the plurality of historical data storages includes historical and planned values of internal and external factor groups at different levels for each related entity, wherein each of the primary entity metric device, the secondary entity metric device, and the tertiary entity metric device interact with the plurality of historical data storages to generate respective the first primary entity metric forecast, the first second entity metric forecast and the first third entity metric forecast for a corresponding entity; and
a server comprising:
at least one memory;
a data receiving module that receives via the data communication network data indicating the first primary entity metric forecast and the first secondary entity metric forecast based on historical data of a forecast variable at different instances of time and also receives data values associated with corresponding ones of the applicable forecast rules or constraints;
a data storage;
a learning module that interacts with the data receiving module to access historical values of associated variable elements included in the first primary entity metric forecast and historical values of associated variable elements included in the first secondary entity metric forecast at the different instances of time to train the metric forecast entity relationship machine learning model to obtain the trained metric entity relationship machine learning model that accounts for a relationship between the first primary entity metric forecast and the first secondary entity metric forecast;
a constraints/rules module that interacts with the data storage while storing and/or retrieving data values associated with the forecast rules or constraints and with the learning module; and
an estimation module that calculates measurable values of each variable element included in the first primary entity forecast and the first secondary entity forecast based on the received values associated with each of the specific applicable forecast rules or constraints,
wherein the at least one memory, data receiving module, the learning module, the constraints/rules module, and the estimation module are provided in a multi-chip module having increased connectivity between the chips of the multi-chip module, the increased connectivity simulating on-chip operation of the at least one memory, the data receiving module, the constraints/rules module and the estimation module in lieu of a conventional CPU and bus implemementation
wherein the server is further configured to estimate metric forecasts associated with the plurality of related entities by training and applying the metric forecast entity relationship machine learning model by
interfacing with the primary entity metric device, the secondary entity metric device, and the tertiary entity metric device to obtain the first primary entity metric forecast, the first secondary entity metric forecast, and the first tertiary entity metric forecast respectively at different instances of time;
training the metric forecast entity relationship machine learning model based on a relationship between the first primary entity metric forecast, the first secondary entity metric forecast, and the first tertiary entity metric forecast to obtain the trained metric entity relationship machine learning model that accounts for the relationship between the first primary entity metric forecast, the first secondary entity metric forecast, and the first tertiary entity metric forecast; and
estimating a second primary entity metric forecast, a second secondary entity metric forecast, and a second tertiary entity metric forecast based on the trained metric forecast entity relationship machine learning model,
wherein the primary entity is a retailer and the secondary entity is a distributor.
17. (New) The system of claim 16, wherein the server is further configured to estimate metric forecasts associated with the plurality of related entities by training and applying the metric forecast entity relationship machine learning model by: applying at least one independent forecast rule or constraint on the first primary entity metric forecast, the first secondary entity metric forecast, and the first tertiary entity metric forecast to obtain a first primary entity metric forecast, a first secondary entity metric forecast, and a first tertiary entity metric forecast.
18. (New) The system of claim 17, wherein the applying at least one independent forecast rule or constraint further comprises: receiving values associated with the at least one independent forecast rule or constraint of the corresponding first primary entity, first secondary entity, and first tertiary entity of the plurality of related entities; and calculating the first primary entity metric forecast, the first secondary entity metric forecast, and the first tertiary entity metric forecast based on the values of the at least one independent forecast rule or constraint in obtaining the first primary entity metric forecast, the first secondary entity metric forecast, and the first tertiary entity metric forecast.
19. (New) The system of claim 16, wherein the trained metric entity relationship machine learning model indicates the specific ones of the forecast rules or constraints to use from the at least one independent forecast rule or constraint in performing the estimating, the dependency between the first primary entity metric forecast, the first secondary entity metric forecast, and the first tertiary entity metric forecast.
20. (New) The system of claim 16, wherein the estimating further comprises: receiving values associated with specific applicable ones of forecast rules or constraints; and calculating the first primary entity forecast, the first secondary entity forecast, andthe first tertiary entity forecast based on the received values.
21. (New) The system of claim 20, wherein the calculating is based on the dependency existing between the first primary entity forecast, the first secondary entity forecast, and the first tertiary entity forecast.
22. (New) A method of estimating metric forecasts associated with a plurality of related entities including at least a primary entity and a secondary entity with more accuracy by training and applying a metric forecast entity relationship machine learning model, the method comprising:
interfacing with a primary entity metric device and a secondary entity metric device to obtain a first primary entity metric forecast for a related entity and a first secondary entity metric forecast for a related entity respectively at different instances of time;
receiving at a data receiving module, via a data communication network, data indicating the first primary entity metric forecast and a first secondary entity metric forecast based on historical data of a forecast variable at different instances of time, the historical data stored in a plurality of historical data storages, wherein each of the plurality of historical data storages correspond to historical data associated with a related entity for a respective entity metric device and each of the plurality of historical data storages includes historical and planned values of internal and external factor groups at different levels for each related entity, wherein each of the primary entity metric device and the secondary metric device interact with the plurality of historical data storages to generate the first primary entity metric forecast and the second entity metric forecast for a corresponding entity;
through interaction between a learning module and a data receiving module, accessing historical values of associated variable elements included in the first primary entity metric forecast and historical values of associated variable elements included in the first secondary entity metric forecast at the different instances of time to train the metric forecast entity relationship machine learning model to obtain the trained metric entity relationship machine learning model that accounts for a relationship between the first primary entity metric forecast and the first secondary entity metric forecast;
using an estimation module, calculating measurable values of each variable element included in the first primary entity forecast and the first secondary entity forecast based on the received values associated with each of specific applicable forecast rules and constraints; and
estimating a second primary entity metric forecast and a second secondary entity metric forecast based on the trained metric forecast entity relationship machine learning model,
wherein the data receiving module, the estimation module, and the learning module are are provided in a multi-chip module on a semiconductor platform having increased connectivity between chips of the MCM, the increased connectivity simulating on-chip operation of the data receiving module, the estimation module and the learning module in lieu of a conventional CPU and bus implementation,
wherein the primary entity is a retailer and the secondary entity is a distributor.
23. (New) The method of claim 22, the method further comprising: applying at least one independent forecast rule or constraint on the first primary entity metric forecast and the first secondary entity metric forecast to obtain a first primary entity metric forecast and a first secondary entity metric forecast.
24. (New) The method of claim 23, the applying step further comprising: receiving values associated with the at least one independent forecast rule or constraint of the corresponding first primary entity and first secondary entity of the plurality of related entities; and calculating the first primary entity metric forecast and the first secondary entity metric forecast based on the values of the at least one independent forecast rule or constraint in obtaining the first primary entity metric forecast and the first secondary entity metric forecast.
25. (New) The method of claim 22, wherein the plurality of related entities comprises a first tertiary entity metric forecast based on historical data of a tertiary entity metric obtained from a tertiary entity metric device wherein: the obtaining comprises a first primary entity metric forecast, a first secondary entity metric forecast and a first tertiary entity metric forecast based on historical data of a primary entity metric obtained from the primary entity metric device, historical data of a secondary entity metric obtained from the secondary entity metric device, and historical data of a tertiary entity metric obtained from the tertiary entity metric device at different instances of time; the training a metric forecast entity relationship machine learning model is based on a relationship between the first primary entity metric forecast, the first secondary entity metric forecast and the first tertiary entity metric forecast to obtain a trained metric entity relationship machine learning model that accounts for the relationship between the first primary entity metric forecast, the first secondary entity metric forecast and the first tertiary entity metric forecast; and the estimating the second primary entity metric forecast, the second secondary entity metric forecast and the third secondary entity metric forecast is based on the trained metric entity relationship machine learning model.
All claims recite subject matter falling within one of the four categories of invention (step 1)
Regarding claims 16-25, the limitations as drafted, but for the recitations of underlined additional elements, recite a process that, under its broadest reasonable interpretation, is a mathematical concept (i.e. a construct that processes provided data). That is, other than reciting the underlined limitations, nothing in the claim element precludes the step from practically being performed by manual calculation. Thus claims 16-25 recite an abstract idea (Step 2A_1).
The claims recite various hardware elements (referring to system, devices, machine learning model, server, server components, software modules. Multi-chip module with increased connectivity) for performance of data processing. These elements are recited at a high-level of generality, such that they amount to no more than mere instructions to apply the exception using a generic computer components. Per MPEP 2106.05(f) the elements therefore do not provide a practical application or significantly more. Furthermore, see https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf for guidance on why machine learning models are treated as “apply it” level computer implementations.
The claims recite various applications of transferring data using computer components. Per MPEP 2106.05(g) necessary data gathering is insignificant extra-solution activity that does not provide a practical application or significantly more. Furthermore, receiving/transmitting data is well-understood routine and conventional activity that therefore does not provide significantly more. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015).
The claims are generally linked to multi-chip modules. Per MPEP 2106.05(h), generally linking claims to a technological environment does not provide a practical application or significantly more. Multi-chip modules with increased connectivity is conventional (McLellan [3] US 20180096938 A1).
Thus claims 16-25 are ineligible.
Prior art status
Claims 16-25 are considered to distinguish over the cited art. Examiner agrees with arguments in response dated 7/07/2025
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHAN A MITCHELL whose telephone number is (571)270-3117. The examiner can normally be reached M-F 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Zeender can be reached on 571-272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NATHAN A MITCHELL/Primary Examiner, Art Unit 3627
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