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 .
Applicant(s) Response to Official Action
The response filed on 8/12/2026 has been entered and made of record.
Response to Arguments/Amendments
Applicant’s arguments filed 8/12/2026 have been fully considered but they are not persuasive. Examiner fully addresses below any arguments.
Claim Rejections - 35 USC § 101
Summary of Arguments:
Regarding claims 1, 8 and 15 Applicant argues the claims Do Not Recite a Judicial (mathematical relationships under MPEP § 2106.04(a)(2)(I)(A)) Exception The claims do not recite a mathematical formula, equation, or calculation. Rather, they recite a specific technical method and apparatus for searching a model knowledge base using a structured parameter that defines three levels of machine learning knowledge:
(1) knowledge in a machine learning task (e.g., a sample set and a model),
(2) an attribute of the machine learning task (e.g., a constraint and an application scope), and
(3) knowledge between a plurality of machine learning tasks (e.g., an association relationship between tasks).
This three-level knowledge parameter structure is not a mathematical concept, but a specific data structure and retrieval mechanism embedded within the knowledge base architecture.
Even if the claims were found to recite an abstract idea, the additional elements integrate the exception into a practical application. Specifically, the claims recite a model knowledge base organized in a multi-level index structure that is searchable according to the parameter, as now expressly recited in amended Claims 1, 8, and 15. This multi-level index structure is a specific technical improvement to knowledge base technology. The present claims specifically recite, in amended Claims 1, 8, and 15, the structural integration of the multi-level index structure with the three-level knowledge parameters (Spec [0005], [0057-0058]).
While generic processors and memories may be conventional hardware in isolation, the Examiner has provided no factual evidence (e.g., prior art citations or official notice) showing that the claimed COMBINATION- specifically, a model knowledge base configured with a multi-level index structure searchable by the three-level knowledge parameter- was well-understood, routine, or conventional in the art at the time of the invention.
As disclosed in the specification, this specific combination provides a non-conventional architecture including (a) a knowledge base initialization module ( ¶0074, ¶0081-0085), (b) multi- level knowledge and index extraction modules for intra-task, task, and inter-task knowledge (¶0096-0100), (c) a knowledge similarity measurement and filtering mechanism (¶0100-0103), and (d) an incremental knowledge maintenance module with four distinct update policies: knowledge inheritance, accumulation, merging, and remodeling (¶0104-0121, Tables 7-8). These are specific, non-conventional improvements to the functioning of model knowledge base systems that go far beyond generic computer components.
Examiner’s Response:
Examiner respectfully disagrees. Regarding claims 1, 3 and 5, Examiner contends nothing in the record states the claims were directed to either mathematical formula, equation, or calculation. The Applicant seems to be quoting MPEP § 2106.04(a)(2)(I)(B) and MPEP § 2106.04(a)(2)(I)(C). No judicial exception analysis was made based on § 2106.04(a)(2)(I)(B-C) portions of the MPEP.
¶0005 discloses “Therefore, how to implement accurate search in a model knowledge base becomes a problem to be urgently resolved”; this is merely a statement of a problem. ¶0055-0058 seems to acknowledge problem with an existing model knowledge base. Nowhere in ¶0005 and ¶0055-0058 is mentioned a “multi-level index structure with the three-level knowledge parameters”. The claims do not recite a “multi-level index structure with the three-level knowledge parameters”.
¶0028 of Applicant’s specification discloses “processor may be a general-purpose processor”. ¶0070-0074 describes the base (i.e. database) as existing cloud storage services. ¶0054 admitted to well-know “Google-developed TensorFlow Hub” which can be query using well-known Structured Query Language (SQL) which in turn is capable of accepting multiple parameters (primary key, secondary keys, columns, index etc.) for searching a database.
The claims do not recite: (a) a knowledge base initialization module (b) multi- level knowledge and index extraction modules for intra-task, task, and inter-task knowledge, (c) a knowledge similarity measurement and filtering mechanism, and (d) an incremental knowledge maintenance module with four distinct update policies: knowledge inheritance, accumulation, merging, and remodeling.
Accordingly, Examiner maintains the rejections.
Claim Rejections - 35 USC § 102
Summary of Arguments:
Regarding claims 1-6, 8-13 and 15-16Applicant argues that:
Masafumi Does Not Disclose a Model Knowledge Base Organized in a Multi-Level Index Structure. Masafumi discloses a conventional, flat database queried using general-purpose SQL, whereas the claimed invention employs a specific multi-level index structure (intra-task knowledge index, task knowledge index, and inter-task knowledge index)
Masafumi Does Not Disclose the Claimed Three-Level Parameter.
Masafumi's query does not include knowledge in a machine learning task in the claimed sense of a sample set and a model obtained through training.
Masafumi's query does not include an attribute of the machine learning task in the claimed sense of a constraint and an application scope.
Masafumi does not disclose or suggest knowledge between a plurality of machine learning tasks, such as an association relationship between tasks.
Examiner’s Response:
Examiner respectfully disagrees. Regarding claims 1-6, 8-13 and 15-16, Examiner contends:
The claims do not recite a specific multi-level index structure (intra-task knowledge index, task knowledge index, and inter-task knowledge index). The claimed “multi-level index structure” is not patentably indistinguishable from fig. 3 of Masafumi.
The claims do not recite “Three-Level Parameter”. The claim only requires “one or a combination of the following: knowledge in a machine learning task, an attribute of the machine learning task, and knowledge between a plurality of machine learning tasks; and providing the pieces of first knowledge to a user”. At least the “knowledge between the plurality of machine learning tasks comprises an association relationship between the plurality of machine learning tasks” is patentably indistinguishable from ¶0045 of Masafumi.
In response to applicant’s argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., intra-task knowledge index, task knowledge index, and inter-task knowledge index and Three-Level Parameter) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant’s arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Accordingly, Examiner maintains the rejections.
Claim Interpretation
Using claim 1 as exemplary, the table below represents the Examiner’s Interpretation in light of the original disclosure of independent claims 1 and 8 which are all commensurate in scope.
Table I
Independent Claim 1
Examiner’s Interpretation in Light of the Original Disclosure
1. A knowledge obtaining method performed by a computing device, comprising:
The preamble seems to be reciting a method that is capable of obtaining “knowledge” which can be any type of data/information.
(a) searching to obtain pieces of first knowledge, wherein the first knowledge is regarding a first machine-learning model,
No additional detail on the “pieces of first knowledge” was found in the original disclosure beyond what is recited in the body of claim.
(b) a model knowledge base based on a parameter, the model knowledge base stores data regarding attributes of a plurality of machine-learning models and the data are organized in a multi-level index structure that is searchable according to the parameter, and
The “model knowledge base” can be Google-developed TensorFlow Hub (¶0054) which is a database or repository (i.e. TensorFlow Hub is a repository of trained machine learning models ready for fine-tuning and deployable anywhere. Reuse trained models like BERT and Faster R-CNN with just a few lines of code- see https://www.tensorflow.org/hub). A database is known to be searchable by querying said database using well-known Structured Query Language(s) (SQL) command/statement
(i.e. searching a knowledge base (for example, the edge knowledge base 220) based on runtime data and a target knowledge type query command- ¶0088).
(c) wherein the parameter comprises one or a combination of the following: knowledge in a machine learning task, an attribute of the machine learning task, and knowledge between a plurality of machine learning tasks; and
The “parameter” is data (i.e. Fill in parameters required to create dataset, including “Dataset name” and “Dataset input location”- Fig. 6) used to search/query the database/repository (i.e. “model knowledge base”), this parameter can be entered by the user (i.e. obtaining the parameter inputted by the user- ¶0009).
(d) providing the pieces of first knowledge to a user.
displaying data (i.e. pieces of first knowledge) to the user (i.e. The display module is configured to provide the one or more pieces of first knowledge for a user- ¶0019… Step 120: Provide the one or more pieces of first knowledge for the user- ¶0064, fig. 1… The apparatus provided in this embodiment of this application may implement the method procedure shown in FIG. 1 in an embodiment of this application. The knowledge obtaining apparatus 1000 includes an obtaining module 1010 and a display module 1020- ¶0149, fig. 10… The display module 1020 is configured to provide the one or more pieces of first knowledge for a user- ¶0151)
The tables below represent the Examiner’s Interpretation in light of the original disclosure of independent claims 15.
Table 2
Independent Claim 15
Examiner’s Interpretation in Light of the Original Disclosure
15. A computing device comprising:
Any general-purpose computer (i.e. a computing device (which may be a personal computer, a server, or a network device) to perform all or a part of the steps of the methods described in embodiments of this application- ¶0218)
(h) a memory storing executable instructions; and a processor configured to execute the executable instructions to perform operations of:
Any generic component expected to be in a general-purpose computer (i.e. a processor, and a memory, where the processor is configured to control the input/output interface to receive and send information. The memory is configured to store a computer program. The processor is configured to invoke the computer program from the memory and run the computer program, so that the method in the first aspect or any one of the possible implementations of the first aspect is performed- ¶0039)
(i) maintaining a model knowledge base,
The “model knowledge base” can be Google-developed TensorFlow Hub (¶0054) which is a database or repository (i.e. TensorFlow Hub is a repository of trained machine learning models ready for fine-tuning and deployable anywhere. Reuse trained models like BERT and Faster R-CNN with just a few lines of code- see https://www.tensorflow.org/hub).
There is no further definition in the original disclosure for the maintaining a “model knowledge base”. As long as the database can be accessed, said database will be interpreted as “maintained”.
(j) wherein the model knowledge base stores data regarding attributes of a plurality of machine-learning models and the data are organized in a multi-level index structure that is searchable according to a parameter comprising one or a combination of: knowledge in a machine learning task, an attribute of the machine learning task, and knowledge between a plurality of machine learning tasks;
A database is known to be searchable by querying said database using well-known Structured Query Language(s) (SQL) command/statement
(i.e. searching a knowledge base (for example, the edge knowledge base 220) based on runtime data and a target knowledge type query command- ¶0088).
(k) receiving a query for model knowledge, where the query comprises the parameter;
The “parameter” is data (i.e. Fill in parameters required to create dataset, including “Dataset name” and “Dataset input location”- Fig. 6) used to search/query the database/repository (i.e. “model knowledge base”), this parameter can be entered by the user (i.e. obtaining the parameter inputted by the user- ¶0009).
(l) searching the model knowledge base based on the parameter to obtain pieces of first knowledge regarding a first machine-learning mode;
Querying a database will return search results (if any) based on the parameters (i.e. After the query command parsing module 3111 receives the target knowledge type query command shown in Table 6, the query command parsing module 3111 parses that user A needs to search for a task related to the No. 03 dataset in the knowledge base, where the receiving address is 192.162.10.12. The query command parsing module 3111 may transfer the information as a query knowledge item to the knowledge feedback module 3112- ¶0134)
(m) providing the pieces of the first knowledge in response to the query.
displaying data (i.e. pieces of first knowledge) to user (i.e. The display module is configured to provide the one or more pieces of first knowledge for a user- ¶0019… Step 120: Provide the one or more pieces of first knowledge for the user- ¶0064, fig. 1… The apparatus provided in this embodiment of this application may implement the method procedure shown in FIG. 1 in an embodiment of this application. The knowledge obtaining apparatus 1000 includes an obtaining module 1010 and a display module 1020- ¶0149, fig. 10… The display module 1020 is configured to provide the one or more pieces of first knowledge for a user- ¶0151)
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
ANALYSIS
Step 1 – Statutory Category
Claims 1, 8 and 15, as a method (process)/device claims, recite one of the enumerated categories of eligible subject matter in 35 U.S.C. § 101. Therefore, the issue is whether it is directed to a judicial exception without significantly more.
Step 2A(i): Does the Claim Recite a Judicial Exception?
Examiner concludes claims 1, 8 and 15 do not recite the judicial exceptions of either natural phenomena or laws of nature. Examiner evaluates whether claims 1, 8 and 15 recites an abstract idea based upon the Revised Guidance.
First, Examiner look to the Specification to provide context as to what the claimed invention is directed to (see Tables 1-2).
As detailed in Tables 3-4, below, Examiner determines that claims 1, 8 and 15, overall, recite mathematical relationship. This type of activity, i.e., searching to obtain knowledge as recited in each of limitations (a) through (d) (claims 1 and 8) and (h) through (m) (claim 15).
Thus, under Step 2A(i), and under the Revised Guidance, Examiner concludes that claim 1’s knowledge searching to obtain method performed recites a judicial exception of mathematical relationships, and thus recites an abstract idea.
In Tables 3-4 below, Examiner identifies in italics the specific claim limitations in claim 1 and 15 that Examiner concludes recite an abstract idea. Examiner, additionally identifies in bold the additional (non-abstract) claim limitations that are generic computer components and techniques.
Table 3
Independent Claim 1 as exemplary
Revised Guidance
1. A knowledge obtaining method performed by a computing device, comprising:
A process (method) is a statutory subject matter class. See 35 U.S.C. § 101 (“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.”) performed by a device (computer or computing device).
(a) searching to obtain pieces of first knowledge, wherein the first knowledge is regarding a first machine-learning model,
Mere data gathering (Insignificant Extra-Solution Activity- MPEP 2106.05(g)) of mathematical relationships (an abstract idea- See MPEP § 2106.04(a)(2)(I)(A)) and limiting a search result database by Selecting a particular data source or type of data to be manipulated using the described “parameter”; see also Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937. By way of example, in Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), the steps in the claims described "the creation of a dynamic document based upon ‘management record types’ and ‘primary record types.’" 850 F.3d at 1339-40; 121 USPQ2d at 1945-46. The claims were found to be directed to the abstract idea of "collecting, displaying, and manipulating data." 850 F.3d at 1340; 121 USPQ2d at 1946. MPEP2106.05 (f).
(b) from a model knowledge base based on a parameter, the model knowledge base stores data regarding attributes of a plurality of machine-learning models and the data are organized in a multi-level index structure that is searchable according to the parameter, and
(c) wherein the parameter comprises one or a combination of the following: knowledge in a machine learning task, an attribute of the machine learning task, and knowledge between a plurality of machine learning tasks; and
(d) providing the pieces of first knowledge to a user.
Table 4
Independent Claim 15
Revised Guidance
15. A computing device comprising:
A device (computer or computing device) is a statutory subject matter class. See 35 U.S.C. § 101 (“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.”).
(h) a memory storing executable instructions; and a processor configured to execute the executable instructions to perform operations of:
- As claimed, both processor and a memory storing executable instructions represent generic computer components and techniques.
(i) maintaining a model knowledge base,
- As claimed, database represents generic computer components and techniques.
(j) wherein the model knowledge base stores data regarding attributes of a plurality of machine-learning models and the data are organized in a multi-level index structure that is searchable according to a parameter comprising one or a combination of: knowledge in a machine learning task, an attribute of the machine learning task, and knowledge between a plurality of machine learning tasks;
Mere data gathering (Insignificant Extra-Solution Activity- MPEP 2106.05(g)) of mathematical relationships (an abstract idea- See MPEP § 2106.04(a)(2)(I)(A)) and limiting a search result database by Selecting a particular data source or type of data to be manipulated using the described “parameter”; see also Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937.
By way of example, in Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), the steps in the claims described "the creation of a dynamic document based upon ‘management record types’ and ‘primary record types.’" 850 F.3d at 1339-40; 121 USPQ2d at 1945-46. The claims were found to be directed to the abstract idea of "collecting, displaying, and manipulating data." 850 F.3d at 1340; 121 USPQ2d at 1946. MPEP2106.05 (f).
(k) receiving a query for model knowledge, where the query comprises the parameter;
(l) searching the model knowledge base based on the parameter to obtain pieces of first knowledge regarding a first machine-learning model;
(m) providing the pieces of the first knowledge in response to the query.
Under the broadest reasonable interpretation standard,1 limitations (a) through (c) and (h) through (l) recite steps or functions that would ordinarily occur when searching/querying a database.2
Thus, claims 1, 8 and 15 recite abstract ideas.
Step 2A(ii): Judicial Exception Integrated into a Practical Application?
If the claims recite a judicial exception, as Examiner conclude above, Examiner proceed to the “practical application” Step 2A(ii) in which Examiner determine whether the recited judicial exception is integrated into a practical application of that exception by: (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (b) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
As to the specific limitations, limitations (a)-(c) and recite abstract ideas.
Furthermore, the claims do not operate the recited generic computer components (limitation (a)) in an unconventional manner to achieve an improvement in computer functionality. See MPEP § 2106.05(a).
Examiner find each of the limitations of claims 1, 8 and 15 recite abstract ideas as identified in Step 2A(i), supra, and none of the limitations integrate the judicial exception of obtaining pieces of first knowledge regarding a first machine-learning model into a practical application as determined under one or more of the MPEP sections cited above. The claim as a whole merely uses instructions to implement the abstract idea on a computer or, alternatively, merely uses a computer as a tool to perform the abstract idea.
Under analogous circumstances, the Federal Circuit has held that “[t]his is a quintessential ‘do it on a computer’ patent: it acknowledges that [such] data . . . was previously collected, analyzed, manipulated, and displayed manually, and it simply proposes doing so with a computer. It was held such claims are directed to abstract ideas.” Univ. of Fla. Research Found., Inc. v. Gen. Elec. Co., 916 F.3d 1363, 1367 (Fed. Cir. 2019); see also Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1351 (Fed. Cir. 2016) (“Though lengthy and numerous, the claims do not go beyond requiring the collection, analysis, and display of available information in a particular field, stating those functions in general terms, without limiting them to technical means for performing the functions that are arguably an advance over conventional computer and network technology.”).
Therefore, the claim as a whole merely uses instructions to implement the abstract idea on a computer or, alternatively, merely uses a computer as a tool to perform the abstract idea. Thus, on this record, Applicant has not shown an improvement or practical application under the guidance of MPEP section 2106.05(a) (“Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field”) or section 2106.05I(“Other Meaningful Limitations”). Therefore, the abstract idea is not integrated into a practical application, and thus claims 1, 8 and 15 is directed to the judicial exception.
Step 2B – “Inventive Concept” or “Significantly More”
If the claims are directed to a judicial exception, and not integrated into a practical application, as noted above, Examiner proceed to the “inventive concept” step. For Step 2B Examiner must “look with more specificity at what the claim elements add, in order to determine ‘whether they identify an “inventive concept” in the application of the ineligible subject matter’ to which the claim is directed.” Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016).
In applying step two of the Alice analysis, reviewing court guides that Examiner must “determine whether the claims do significantly more than simply describe [the] abstract method” and thus transform the abstract idea into patentable subject matter. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014). Examiner looks to see whether there are any “additional features” in the claims that constitute an “inventive concept,” thereby rendering the claims eligible for patenting even if they are directed to an abstract idea. Alice, 573 U.S. at 221. Those “additional features” must be more than “well-understood, routine, conventional activity.” Mayo, 566 U.S. at 79.
Limitations referenced in Alice that are not enough to qualify as “significantly more” when recited in a claim with an abstract idea include, as non-limiting or non-exclusive examples: adding the words “apply it” (or an equivalent) with an abstract idea3; mere instructions to implement an abstract idea on a computer4; or requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry.5
Evaluating representative claim 1 under step 2 of the Alice analysis, Examiner concludes it lacks an inventive concept that transforms the abstract idea of obtain pieces of first knowledge regarding a first machine-learning model into a patent-eligible application of that abstract idea.
The patent eligibility inquiry may contain underlying issues of fact. Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324–25 (Fed. Cir. 2016). In particular, “[t]he question of whether a claim element or combination of elements is well-understood, routine and conventional to a skilled artisan in the relevant field is a question of fact.” Berkheimer v. HP Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018).
As evidence of the conventional nature of the recited a “microprocessor” and a “memory storing executable instructions” in method claim 8 and 9, the Specification discloses:
Optionally, the processor may be a general-purpose processor, and may be implemented using hardware or software. When implemented using the hardware, the processor may be a logic circuit, an integrated circuit, or the like. When implemented using software, the processor may be a general-purpose processor, and is implemented by reading software code stored in the memory. The memory may be integrated into the processor, or may be located outside the processor, and exist independently. Spec. ¶0028.
Thus, because the Specification describes the additional elements in general terms, without describing the particulars, Examiner concludes the claim limitations may be broadly but reasonably construed as reciting conventional computer components and techniques, particularly in light of Applicant’s Specification, as quoted above.6
The MPEP, based upon precedential guidance, provides additional considerations with respect to analysis of the well-understood, routine, and conventional nature of the recited computer-related components.
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, 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., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
. . . . In Alice Corp., the claim recited the concept of intermediated settlement as performed by a generic computer. The Court found that the recitation of the computer in the claim amounted to mere instructions to apply the abstract idea on a generic computer. 573 U.S. at 225-26, 110 USPQ2d at 1984. The Supreme Court also discussed this concept in an earlier case, Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), where the claim recited a process for converting binary-coded-decimal (BCD) numerals into pure binary numbers. The Court found that the claimed process had no meaningful practical application except in connection with a computer. Benson, 409 U.S. at 71-72, 175 USPQ at 676. The claim simply stated a judicial exception (e.g., law of nature or abstract idea) while effectively adding words that “apply it” in a computer. Id.
MPEP § 2106.05(f) (“Mere Instructions To Apply An Exception”).
With respect to the Step 2B analysis, Examiner concludes, similar to Alice, the recitation of a method that includes a “microprocessor” and a “memory storing executable instructions”, “maintaining a model base”, is simply not enough to transform the patent-ineligible abstract idea here into a patent-eligible invention under Step 2B. See Alice, 573 U.S. at 221 (“[C]laims, which merely require generic computer implementation, fail to transform [an] abstract idea into a patent-eligible invention.”).
Examiner concludes the claims fail the Step 2B analysis because claim 1, 8 and 15, in essence, merely recites computer-based elements along with no more than mere instructions to obtain pieces of first knowledge regarding a first machine-learning model using the computer-based elements.
Therefore, in light of the foregoing, Examiner concludes, under the Revised Guidance, that each of Applicant’s claims 1–16, considered as a whole, is directed to a patent-ineligible abstract idea that is not integrated into a practical application and does not include an inventive concept.
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.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-16 are 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. Applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation ‘the model knowledge base stores data regarding attributes of a plurality of machine-learning models and the data are organized in a multi-level index structure’ (claims 1, 8 and 15) in the application as filed.
When an amendment is filed in reply to an objection or rejection based on 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, a study of the entire application is often necessary to determine whether or not "new matter" is involved. Applicant should therefore specifically point out the support for any amendments made to the disclosure. MPEP 2163.06 I.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 8-13 and 15-16 are rejected under 35 U.S.C. 102(a) (2) as being anticipated by Masafumi Tsuyuki [US 20220284061 A1: already of record].
Regarding claim 1, Masafumi teaches:
1. A knowledge obtaining method performed by a computing device (i.e. FIG. 1 is a diagram showing a configuration example of a trained model search system according to an embodiment of the present disclosure- ¶0014), comprising:
searching a model knowledge base (i.e. a model information database (DB) 201 (see FIG. 3)- ¶0031) based on a parameter (i.e. query information 202 (see FIG. 4)- ¶0031) to obtain pieces of first knowledge (i.e. model information about each of trained models- ¶0031), wherein the first knowledge is regarding a first machine-learning model (i.e. The search unit 301 narrows down the model information from the model information DB 201- ¶0031), the model knowledge base stores data regarding attributes of a plurality of machine-learning models and the data are organized in a multi-level index structure that is searchable according to the parameter (i.e. see fig. 3), and wherein the parameter comprises one or a combination of the following: knowledge in a machine learning task (i.e. prediction execution method- fig. 3), an attribute of the machine learning task (i.e. predictable value range- fig. 3), and knowledge (i.e. test data- fig. 3) between a plurality of machine learning tasks (i.e. The search unit 301 narrows down the model information from the model information DB 201 based on search query information 202 (see FIG. 4) input from the user terminal 102 by the model search screen 212 and generates search result information 203 (see FIG. 5) including the narrowed-down model information- ¶0031); and
providing the pieces of first knowledge to a user (i.e. The selection unit 307 displays, on the user terminal 102, a model explanation screen 210 (see FIG. 12) including the group information 206 and the explanation information 208 and a model selection screen 209 (see FIG. 11)).
Regarding claim 2, Masafumi teaches all the limitations of claim 1 and Masafumi further teaches:
further comprising: obtaining the parameter inputted by the user; or obtaining the parameter from a system not comprising the computing device (i.e. query information 202 (see FIG. 4) input from the user terminal 102- ¶0031).
Regarding claim 3, Masafumi teaches all the limitations of claim 1 and Masafumi further teaches:
wherein the knowledge in the machine learning task comprises a sample set and a model of the machine learning task, and the model is obtained through training based on the sample set; or the attribute of the machine learning task comprises a constraint and an application scope of the machine learning task (i.e. The field 201f stores an inferable range, which is a range of explanatory variables that can be inferred by the trained model. Ranges of the explanatory variables that configure the test data are included in the inferable range- ¶0046); or the knowledge between the plurality of machine learning tasks comprises an association relationship between the plurality of machine learning tasks (i.e. The field 201d stores a parent model ID which is a model ID that identifies a related model that is related to the trained model and is a trained model. The parent model ID indicates a relevance of the learning models. The related model is, for example, a model having an algorithm the same as and a training data period different from the trained model. In the example of FIG. 3, a trained model generated by changing a period of training data from an original trained model is called a child model, the original trained model is called a parent model, and the parent model is the related model- ¶0045).
Regarding claim 4, Masafumi teaches all the limitations of claim 1 and Masafumi further teaches:
further comprising: obtaining second knowledge related to the first knowledge from the model knowledge base; and providing the second knowledge for the user (i.e. FIG. 5 is a diagram showing a configuration example of the search result information 203. The search result information 203 shown in FIG. 5 includes fields 203a to 203e. FIG. 5 is a diagram showing a configuration example of the search result information 203. The search result information 203 shown in FIG. 5 includes fields 203a to 203e- ¶0052).
Regarding claim 5, Masafumi teaches all the limitations of claim 1 and Masafumi further teaches:
further comprising: providing the user with configuration information of the first knowledge (i.e. FIG. 5 is a diagram showing a configuration example of the search result information 203. The search result information 203 shown in FIG. 5 includes fields 203a to 203e- ¶0052).
Regarding claim 6, Masafumi teaches all the limitations of claim 1 and Masafumi further teaches:
further comprising: obtaining target knowledge selected by the user, wherein the target knowledge is the first knowledge or the second knowledge (i.e. The selection unit 307 displays, on the user terminal 102, a model explanation screen 210 (see FIG. 12) including the group information 206 and the explanation information 208 and a model selection screen 209 (see FIG. 11) prompting the user to select a group and present the model explanation screens 210 and 209 to the operator, and prompts the user to select a group. When a group is selected, the selection unit 307 displays, on the user terminal 102, the model selection screen 209 and the model explanation screen 210 corresponding to a subgroup which is a subset of the target models belonging to the selected group, and prompts the user to select a subgroup. The selection unit 307 can repeat the above process until the number of the target models included in the group (subgroup) becomes one- ¶0038).
Regarding claims 8-13, apparatus claims 8-13 are drawn to the apparatus using/performing the same method as claimed in claims 1-6. Therefore, apparatus claims 8-13 correspond to method claims 1-6 are rejected for the same rationale as used above.
Regarding claim 15, Masafumi teaches:
15. A computing device comprising:
a memory storing executable instructions (i.e. a memory 402- ¶0040… The storage device 401 is configured with a non-volatile memory element such as a solid state drive (SSD) and a hard disk drive. The storage device 401 stores a program 406 that defines an operation of the computing device 403 and various kinds of information 201 to 208 used or generated by the computing device 403. The memory 402 is configured with a volatile memory element such as a random access memory (RAM)- ¶0041, fig. 2); and a processor (i.e. The computing device 403 is configured with a processor such as a central processing unit (CPU)- ¶0042) configured to execute the executable instructions to perform operations of:
maintaining a model knowledge base (i.e. a model information database (DB) 201- ¶0031),
wherein the model knowledge base stores data regarding attributes of a plurality of machine-learning models and the data are organized in a multi-level index structure that is searchable according to a parameter comprising one or a combination of: knowledge in a machine learning task, an attribute of the machine learning task, and knowledge between a plurality of machine learning tasks; receiving a query for model knowledge, where the quest comprises the parameter (i.e. see fig. 3);
searching the model knowledge base based on the parameter to obtain pieces of first knowledge regarding a first machine-learning mode(i.e. The search unit 301 narrows down the model information from the model information DB 201 based on search query information 202 (see FIG. 4) input from the user terminal 102 by the model search screen 212 and generates search result information 203 (see FIG. 5) including the narrowed-down model information- ¶0031l; and
providing the pieces of the first knowledge in response to the query (i.e. The selection unit 307 displays, on the user terminal 102, a model explanation screen 210 (see FIG. 12) including the group information 206 and the explanation information 208 and a model selection screen 209 (see FIG. 11)).
Regarding claim 16, Masafumi teaches all the limitations of claim 15 and Masafumi further teaches:
wherein the knowledge in the machine learning task comprises a sample set and a model of the machine learning task, and the model is obtained through training based on the sample set; or the attribute of the machine learning task comprises a constraint and an application scope of the machine learning task (i.e. The field 201f stores an inferable range, which is a range of explanatory variables that can be inferred by the trained model. Ranges of the explanatory variables that configure the test data are included in the inferable range- ¶0046); or the knowledge between the plurality of machine learning tasks comprises an association relationship between the plurality of machine learning tasks (i.e. The field 201d stores a parent model ID which is a model ID that identifies a related model that is related to the trained model and is a trained model. The parent model ID indicates a relevance of the learning models. The related model is, for example, a model having an algorithm the same as and a training data period different from the trained model. In the example of FIG. 3, a trained model generated by changing a period of training data from an original trained model is called a child model, the original trained model is called a parent model, and the parent model is the related model- ¶0045).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Masafumi Tsuyuki [US 20220284061 A1] in view of Biplab Pal [US 20190265687 A1].
Regarding claim 7, Masafumi teaches all the limitations of claim 1.
However, Masafumi does not teach explicitly:
synchronizing, by an edge device, the knowledge in the model knowledge base to a cloud device; or synchronizing, by a cloud device, the knowledge in the model knowledge base to an edge device.
In the same field of endeavor, Biplab teaches:
synchronizing, by an edge device, the knowledge in the model knowledge base to a cloud device; or synchronizing, by a cloud device, the knowledge in the model knowledge base to an edge device (i.e. This time series database is synchronized and backed up with the time series database of the public cloud so that in the event of damage to the particular localized edge cloud of interest, no data is lost- ¶0059).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Masafumi with the teachings of Biplab to perform back-up for all of the sensor time series database and asset database data generated in the edge cloud, for use in the event the edge cloud is damaged by hardware being damaged, or stolen (Biplab- ¶0065).
Conclusion
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 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 CLIFFORD HILAIRE whose telephone number is (571)272-8397. The examiner can normally be reached 5:30-1400.
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, SATH V PERUNGAVOOR can be reached at (571)272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CLIFFORD HILAIRE
Primary Examiner
Art Unit 2488
/CLIFFORD HILAIRE/Primary Examiner, Art Unit 2488
1 During prosecution, claims must be given their broadest reasonable interpretation when reading claim language in light of the specification as it would be interpreted by one of ordinary skill in the art. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004). Under this standard, Examiner interpret claim terms using “the broadest reasonable meaning of the words in their ordinary usage as they would be understood by one of ordinary skill in the art, taking into account whatever enlightenment by way of definitions or otherwise that may be afforded by the written description contained in the applicant’s specification.” In re Morris, 127 F.3d 1048, 1054 (Fed. Cir. 1997).
2 The categorization of each of limitations (a) through (d) and (h) through (m) are abstract ideas, or generic computer components or generic computer-implemented steps are provided in TABLE 3-4.
3 Alice, 573 U.S. at 221–23.
4 Alice, 573 U.S. at 222–23, e.g., simply describing a mathematical relashionship on a physical machine, namely a computer.
5 Alice, 573 U.S. at 225 (explaining using a computer to obtain data, adjust account balances, and issue automated instructions involves computer functions that are well-understood, routine, conventional activities).
6 Claim terms are to be given their broadest reasonable interpretation, as understood by those of ordinary skill in the art and considering whatever enlightenment may be had from the Specification. Morris, 127 F.3d at 1054.
Prosecution Insights