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 .
DETAILED ACTION
This Office Action is sent in response to Application’s Communication received on 09/22/2022 for application number 17/913643. The Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawing, Abstract, Oath/Declaration, and Claims.
Claims (1-7) and (8-14) are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/20/2024, 05/09/2024 and 09/22/2022 was filed prior to current Office Action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Independent claim 1 describes claim limitation “the system being configured to cause the teacher data for learning to be randomly…” which have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder “unit” coupled with functional language “perform and/or configured” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitations invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, independent claim 1 has been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: Para. [0029] of the specification describes the functional language as connected to physical hardware when each of the units are performing its duty.
By this interpretation, the examiner is satisfied with the description of the functional language and the use of sufficient structure as described in the specification of the application.
Thus, it appears that claim 1 is properly invoking 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may amend the claim so that they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9,2011).
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 therefore, subject to the conditions and requirements of this title.
Claims (1-7) and (8-14) are rejected under 35 U.S.C. 101 because the claimed
invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an
abstract idea) without significantly more.
Step 1: Claims (1-7) and (8-14) are drawn to a method each of which is within the four statutory categories (e.g., a process, a machine).
Step 2A - Prong One: In prong one of step 2A, the claims are analyzed to evaluate whether they recite a judicial exception.
Claim 1.
A system for causing an Al (Artificial Intelligence) process to be performed on teacher data for Al learning in which events of objects identified by identifying codes are divided into classes, and data pertaining to an object for which a class determination of the event is required, and receiving class determination data obtained by the Al process, the system being configured to
cause the teacher data for learning to be randomly inputted to, and learned by, each of a plurality of Al processes; input the data pertaining to the object to each of the plurality of Al processes; and receive the class determination data corresponding to each learning from each Al process,
wherein the class of the event of the object identified by each identifying code is thus determined based on each class determination data corresponding to the identifying code.
The limitations recite “…events of objects identified by identifying codes are divided into classes, and data pertaining to an object for which a class determination of the event is required…” which can be defined as concepts that can practically be performed in the human mind, or by a human using pen and paper as a physical aid. Examples of mental processes include observations, evaluations, judgments, and opinions. For example, the claimed “identifying” and “dividing” under its broadest reasonable interpretation when read in light of the specification encompasses using codes to classify data into different classes. Thus, the limitation is a mental process.
The limitations recite “…cause the teacher data for learning to be randomly inputted to …” which can be defined as concepts that can practically be performed in the human mind, or by a human using pen and paper as a physical aid. Examples of mental processes include observations, evaluations, judgments, and opinions. For example, the claimed “causing” under its broadest reasonable interpretation when read in light of the specification encompasses using processing the teacher data as input. Thus, the limitation is a mental process.
The limitations recite “…identifying code is thus determined based on each class determination data…” which can be defined as concepts that can practically be performed in the human mind, or by a human using pen and paper as a physical aid. Examples of mental processes include observations, evaluations, judgments, and opinions. For example, the claimed “identifying” and “determining” under its broadest reasonable interpretation when read in light of the specification encompasses using codes to determine data by classification. Thus, the limitation is a mental process.
Step 2A Prong 2:
Claim 1 recites additional elements such as “Artificial Intelligence” and “receiving class determination data” which are recited at a high level, the elements are merely reciting the words that pertain to a generic computer (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The “applying” is an additional element amount to merely the words “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. The limitation does not integrate the judicial exception into a practical application.
Dependent claims (2-7) and (9-14) fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims (2-6) and 17 are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
Step 2B: The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
The “Artificial Intelligence” and “receiving” steps are considered insignificant extra solution activity. The limitations are mere data gathering and output using Artificial Intelligence that is recited at a high level of generality and amount to processing input data using Artificial Intelligence that recited at high level of generality using a generic computer. Even when considered in combination, the additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept.
Dependent claims (2-7) and (9-14) fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims (2-6) and 17 are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
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.
Claims 1-14 are rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Matthew Hansjuergen et al. Foreign Patent Application Publication JP 201321482 A (hereinafter Matthew) in view of Osamu Nonaka et al. Foreign Patent Application Publication WO 2021181634 A1 (hereinafter Nonaka).
Regarding claim 1, Matthew teaches A system for causing … to be performed on teacher data for Al learning in which events of objects identified by identifying codes are divided into classes, and data pertaining to an object for which a class determination of the event is required, and receiving class determination data obtained by the Al process, the system being configured to (Abstract, page. 2, paragraphs 7-9 wherein Matthew describes identification classifiers and determination part for classifying input data based on feature vector according to a predetermined feature descriptor).
Matthew does not teach using Al (Artificial Intelligence) process to be performed on teacher data; cause the teacher data for learning to be randomly inputted to, and learned by, each of a plurality of Al processes; input the data pertaining to the object to each of the plurality of Al processes; and receive the class determination data corresponding to each learning from each Al process.
However in analogous art of determining events class, Nonaka teaches using Al (Artificial Intelligence) process to be performed on teacher data (Abstract, page. 2, paragraphs 3-6, page. 3, paragraph 1, page. 13, paragraph 4, page. 16, paragraph 5 wherein Nonaka describes collecting teacher data using medical examination information and classifies the information into categories using artificial intelligence) cause the teacher data for learning to be randomly inputted to, and learned by, each of a plurality of Al processes; input the data pertaining to the object to each of the plurality of Al processes; and receive the class determination data corresponding to each learning from each Al process (Abstract, page 2, paragraphs 2-4, page. 15, paragraph 7 wherein Nonaka collects data from multiple devices and performs inference using an inference model generated by learning the collected data as teacher data. And wherein Nonaka classifies the teacher data into categories using artificial intelligence and based on objects such as symptoms for specific patient) wherein the class of the event of the object identified by each identifying code is thus determined based on each class determination data corresponding to the identifying code (FIGS. 3, 4a-4b, Abstract, page. 12, paragraph 2, page. 13, paragraph 3, page. 16, paragraph 6 wherein Nonaka incorporates patient information as a code and classifies and identifies for each code based on an identification as illustrated in FIGS. 3, 4a-4b).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Matthew with Nonaka by incorporating the method of using Al (Artificial Intelligence) process to be performed on teacher data; cause the teacher data for learning to be randomly inputted to, and learned by, each of a plurality of Al processes; input the data pertaining to the object to each of the plurality of Al processes; and receive the class determination data corresponding to each learning from each Al process; wherein the class of the event of the object identified by each identifying code is thus determined based on each class determination data corresponding to the identifying code of Nonaka into the method generating a first message for cooperation with at least one counterpart computing device based on a local observation and a priority of Matthew for the purpose of incorporating a method of creating teacher data for generating an inference model (Nonaka: Page. 2, paragraph 1).
Regarding claim 2, Matthew as modified by Nonaka teaches wherein the teacher data corresponding to two or more pieces of class determination data pertinent to a predetermined condition is deleted from the entire teacher data to attain teacher data for Al learning (Page. 21, paragraph 8 wherein Nonaka describes the steps of deleting the teacher data).
Regarding claim 3, Matthew as modified by Nonaka teaches wherein the predetermined condition is at least based on dispersion of the two or more pieces of class determination data obtained from each Al process, or at least based on whether or not the dispersion of the two or more pieces of class determination data obtained from each Al process is a predetermined degree or lower (page. 3, paragraphs 1-5, page. 6, paragraph 2, page. 8, paragraph 4, page. 21, paragraph 3 wherein Nonaka describes the determination unit for classifying timeseries biometric information data processed via AI. Wherein the information pertains to a specific patient for heath condition and provides the risk state such as developing or worsening).
Regarding claim 4, Matthew as modified by Nonaka teaches wherein, further, for the predetermined condition, a condition is that it is a case where the number of inconsistencies between the class indicated by the teacher data and the class indicated by the class determination data by the plurality of Al processes corresponding to the teacher data is a predetermined number or more (FIGS. 3, 4a-4b page. 3, paragraphs 1-5, page. 6, paragraph 2, page. 8, paragraph 4, page. 21, paragraph 3 wherein Nonaka describes the determination unit for classifying timeseries biometric information data processed via AI. Wherein the information pertains to a specific patient for heath condition and provides the risk state such as developing or worsening), (page. 2, paragraph 5 wherein Matthew determines a class of the input data and outputs a class classification result wherein each identification classifier generates a predetermined number of clusters by learning data and acquires a representative vector (also referred to as a prototype) defined by the centroid of each cluster. Each identification classifier generates a feature vector from input data according to a predetermined feature descriptor (also referred to as a local descriptor or a local feature descriptor), calculates a distance between the feature vector and a prototype of each cluster, and select one or more prototypes closest to. Each prototype is associated with information on a class label, a cluster label, and the center of gravity position of the cluster. Each identification classifier 10 is referred to as a class label associated with a selected prototype (hereinafter, sometimes referred to as “winner”) and a distance between the winner and the feature vector (hereinafter referred to as “winner distance”). Is output as the classification result of the input data).
Regarding claim 5, Matthew as modified by Nonaka teaches wherein the class of the event of the object is the class of the event that changes according to a lapse of a time period, and is the class of the event for which a class determination of the event to be obtained by the Al process is predicted after the lapse of the time period (page. 6, paragraph 7, page. 9, paragraph 6 wherein Nonaka describes a numerical value indicating the health condition changes depending on various factors, wherein the numerical value is based on data that is a time-series pattern of the user for a specific period. This time-series pattern is not simply data obtained by one measurement, but is composed of individual inspection data acquired by measurement at a plurality of different timings, and even changes in the inspection data pattern are used as information. By using a time-series pattern consisting of multiple inspection data, it is less susceptible to errors caused by changes in the measurement environment and conditions. Furthermore, it infers the health condition from the end of the specific period to the future period (when the specific period is extended), and makes it possible to predict the future).
Regarding claim 6, Matthew as modified by Nonaka teaches wherein the class of the event of the object identified by the identifying code is determined to be the class to which the largest number of the pieces of class determination data obtained from each Al process belong code (FIGS. 3, 4a-4b, Abstract, page. 12, paragraph 2, page. 13, paragraph 3, page. 16, paragraph 6 wherein Nonaka incorporates patient information as a code and classifies and identifies for each code based on an identification as illustrated in FIGS. 3, 4a-4b).
Regarding claim 7, Matthew as modified by Nonaka teaches wherein the object is a person and the event is a severity degree of the person (page. 3, paragraphs 1-6, page. 5, paragraph 6, page. 6 paragraph 1-2, page. 13, paragraph 4, wherein Nonaka describes data that includes patient information and the health condition information).
Regarding claim 8, the claim is similar in scope to claim 1 therefore the claim is rejected under similar rationale.
Regarding claim 9, the claim is similar in scope to claim 2 therefore the claim is rejected under similar rationale.
Regarding claim 10, the claim is similar in scope to claim 3 therefore the claim is rejected under similar rationale.
Regarding claim 11, the claim is similar in scope to claim 4 therefore the claim is rejected under similar rationale.
Regarding claim 12, the claim is similar in scope to claim 5 therefore the claim is rejected under similar rationale.
Regarding claim 13, the claim is similar in scope to claim 6 therefore the claim is rejected under similar rationale.
Regarding claim 14, the claim is similar in scope to claim 7 therefore the claim is rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/HASSAN MRABI/Examiner, Art Unit 2144