CTNF 18/509,017 CTNF 92606 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Specification 07-29-04 The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA 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. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. 07-30-06 This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “acquisition unit”, “prediction unit”, and “output unit” in claim 10. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claim 2-6 and 10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 10 recites an “acquisition unit”, “prediction unit”, and “output unit”, however based on the specification there is no recited structure that defines these units beyond their recited functions. Looking at the specification, Fig. 1 shows individual units in a data processor, but it is unclear what said units are made of. Per MPEP 2181 (II.)(B.) “merely referencing a specialized computer (e.g., a “bank computer”), some undefined component of a computer system (e.g., “access control manager”), “logic,” “code,” or elements that are essentially a black box designed to perform the recited function, will not be sufficient because there must be some explanation of how the computer or the computer component performs the claimed function. Blackboard, Inc. v. Desire2Learn, Inc., 574 F.3d 1371, 1383-85, 91 USPQ2d 1481, 1491-93 (Fed. Cir. 2009); Net MoneyIN, Inc. v. VeriSign, Inc., 545 F.3d 1359, 1366-67, 88 USPQ2d 1751, 1756-57 (Fed. Cir. 2008); Ex parte Rodriguez, 92 USPQ2d 1395, 1405-06 (Bd. Pat. App. & Inter. 2009)”. Claims 2 and 5 recite several instances of “a model” it is unclear if these are the same or different models and further makes it unclear which model “the model” refers to. Claim 3-4 and 6 are rejected based on their inherited deficiencies. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to ineligible signals per se. The claim recites a “A computer-readable recording medium recording a program for causing an information processing device to execute” and the original disclosure does not provide a clear, deliberate and sufficient definition to exclude signals per se. Instead, the disclosure describes in par. 38 “a computer program can be distributed using a computer- readable non-transitory recording medium such as a CD-ROM, or via a communication network such as the Internet” and therefore, under the broadest reasonable interpretation, as “can be” does not limit it, "A computer-readable recording medium recording a program for causing an information processing device to execute" as recited in the claim covers transitory signals (see MPEP 2106.03(I): “Non-limiting examples of claims that are not directed to any of the statutory categories include: Transitory forms of signal transmission (often referred to as "signals per se"), such as a propagating electrical or electromagnetic signal or carrier wave”). Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Under step 1, claim 1-10 and 12 belongs to a statutory category. Claim 11 is not directed to a statutory category, see signals per se rejection above, however for the purpose of compact prosecution the claims are also considered under the abstract idea analysis below. Under Step 2A prong 1, the claims as a whole are identified as being directed to a judicial exception as claim 1 and similarly 11 recite(s) “A repair content prediction method” and “predicting a repair content candidate for the device to be repaired based on a learned repair content prediction model and the acquired operation history information and the failure state description information;” which are directed to mathematical concepts and/or mental processes per applicant’s specification, see for example, page 10-11. Under Step 2A prong 2, evaluating whether the claim as a whole integrates the exception into a practical application of that exception, the judicial exception is not integrated into a practical application because “comprising, by an information processing device:” are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The elements of “acquiring operation history information and failure state description information about a device to be repaired;” and “outputting the predicted repair content candidate” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity. Under Step 2B, evaluating additional elements to determine whether they amount to an inventive concept both individually and in combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “comprising, by an information processing device:” are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). The elements of “acquiring operation history information and failure state description information about a device to be repaired;” and “outputting the predicted repair content candidate” are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g)(i) and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d)(i also see prior art of record). Claim 10 recite(s) “predict a repair content candidate for the device to be repaired based on a learned repair content prediction model and the operation history information and the failure state description information acquired by the acquisition unit;” which are directed to mathematical concepts and/or mental processes per applicant’s specification, see for example, page 10-11. Under Step 2A prong 2, evaluating whether the claim as a whole integrates the exception into a practical application of that exception, the judicial exception is not integrated into a practical application because “A repair content prediction device comprising:”, “an acquisition unit configured to”, “a prediction unit configured to” and “an output unit configured to” are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The elements of “acquire operation history information and failure state description information about a device to be repaired;” and “output the repair content candidate predicted by the prediction unit” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity. Under Step 2B, evaluating additional elements to determine whether they amount to an inventive concept both individually and in combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “A repair content prediction device comprising:”, “an acquisition unit configured to”, “a prediction unit configured to” and “an output unit configured to” are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). The elements of “acquire operation history information and failure state description information about a device to be repaired;” and “output the repair content candidate predicted by the prediction unit” are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g)(i) and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d)(i also see prior art of record). Claim 12 recite(s) “A method for creating a repair content prediction model, the method comprising” and “creating a repair content prediction model to predict repair content based on at least one of operation history information and failure state description information on a faulty device by machine learning using, as teacher data, the operation history information, the failure state description information, and repair record information about each of a plurality of the faulty devices for which repair is previously executed” which are directed to mathematical concepts and/or mental processes per applicant’s specification, see for example, page 10-11. Under Step 2A prong 2, evaluating whether the claim as a whole integrates the exception into a practical application of that exception, the judicial exception is not integrated into a practical application because “by an information processing device” are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Under Step 2B, evaluating additional elements to determine whether they amount to an inventive concept both individually and in combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “by an information processing device” are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). Claim 12 recites the additional element(s) of using generic AI/ML technology, i.e. “machine learning”, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the “machine learning” merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of “machine learning” to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2. Claims 2-7 further describe the abstract ideas cited above. Further, the additional element(s) of using generic AI/ML technology, i.e. “machine learning”, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the “machine learning” merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of “machine learning” to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2. In claim 8, “wherein the repair content candidate further includes confidence of the measure content candidate and confidence of the part candidate” further describe the abstract ideas cited above. The judicial exception is not integrated into a practical application or include additional elements that are sufficient to amount to significantly more than the judicial exception because “the repair content prediction method further comprises transmitting data indicating the repair content candidate to a display device” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity and are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g)(i) and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d)(i also see prior art of record). In claim 9, the judicial exception is not integrated into a practical application or include additional elements that are sufficient to amount to significantly more than the judicial exception because “wherein the device to be repaired is a battery for driving a travel motor mounted on a vehicle” are considered to be generally linking the use of a judicial exception to a particular technological environment or field of use and are considered to be merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself per MPEP 2106.05(h) and are well-understood, routine, and conventional activities/elements previously known to the industry per MPEP 2106.05(d) (see prior art of record). Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA Claim (s) 1-12 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Takahashi (US 20200292620 A1) . In claim 1, Takahashi discloses a repair content prediction method (Fig. 1) comprising, by an information processing device (Fig. 1, 22, 44, 80): acquiring operation history information (Par. 64 “history”) and failure state description information (Par. 7, “end of life”) about a device to be repaired (Fig. 1, 12); predicting a repair content candidate for the device to be repaired (Fig. 5 130) based on a learned repair content prediction model (Fig. 5, 124, 126) and the acquired operation history information and the failure state description information (Fig. 5, 120); and outputting the predicted repair content candidate (Fig. 5, 132). In claim 2, Takahashi discloses selecting one of a first prediction model (Fig. 3, one month life remaining, Par. 54), a second prediction model (Fig. 3, two months life remaining, Par. 54), and a third prediction model (Fig. 3, three months life remaining, Par. 54) as the repair content prediction model, wherein the first prediction model is a model created by machine learning (Par. 54-55 “learning using the partial time-series” “neural network”) using, as teacher data, the failure state description information and repair record information about each of a plurality of faulty devices for which repair is previously executed (Par. 74 “deterioration characteristics at a predetermined point in time of the vehicle battery 14 that has reached the end of its life” Also see Fig. 6, Vehicles A-C), the model predicting a first repair content candidate based on the failure state description information (Fig. 5, 132), the second prediction model is a model created by machine learning using (Par. 54-55 “learning using the partial time-series” “neural network”), as teacher data, the operation history information and the repair record information about each of the plurality of faulty devices, the model predicting a second repair content candidate based on the operation history information (Par. 64 “history”), and the third prediction model is a model created by machine learning (Par. 54-55 “learning using the partial time-series” “neural network”) using, as teacher data, the failure state description information, the operation history information, and the repair record information about each of the plurality of faulty devices, the model predicting a third repair content candidate based on the failure state description information and the operation history information (Par. 7, “end of life” and Par. 64 “history”). In claim 3, Takahashi discloses all of claim 2. Takahashi further discloses wherein the third prediction model is created by combining the first prediction model and the second prediction model, and the third repair content candidate is predicted by inputting the first repair content candidate and the second repair content candidate into the third prediction model (Fig. 3, Par. 54 Examiner notes that the models use the same information but at different weights thus are considered to be functionally equivalent). In claim 4, Takahashi discloses all of claim 2. Takahashi further discloses calculating a degree of similarity between history data on a plurality of pieces of the failure state description information that is previously acquired and the failure state description information about the device to be repaired (Par. 62-64), wherein when the degree of similarity is less than a threshold, the second prediction model is selected as the repair content prediction model (Par. 64 “threshold”). In claim 5, Takahashi discloses all of claim 4. Takahashi further discloses calculating an accuracy evaluation value of each of the first prediction model, the second prediction model, and the third prediction model when the degree of similarity is equal to or greater than the threshold (Par. 62, “the probability value y of the remaining life of the prediction target vehicle battery 14 is calculated” and “possibility of remaining life of the vehicle battery” examiner notes the models and data are compared to figure out which model most accurately matches the situation presented by the data), wherein a model of the first prediction model, the second prediction model, and the third prediction model with the highest accuracy evaluation value is selected as the repair content prediction model (Par. 62, the model with the highest probability is chosen, which is considered to be the most accurate). In claim 6, Takahashi discloses all of claim 5. Takahashi further discloses wherein precision or recall is used as the accuracy evaluation value (Par. 62). In claim 7, Takahashi discloses wherein the repair content candidate includes a measure content candidate (Fig. 6 see measurements) and a part candidate (Fig. 8 “item”). In claim 8, Takahashi discloses all of claim 7. Takahashi further discloses wherein the repair content candidate further includes confidence of the measure content candidate and confidence of the part candidate (Par. 62), and the repair content prediction method further comprises transmitting data indicating the repair content candidate to a display device (Fig. 8, Par. 66). In claim 9, Takahashi discloses wherein the device to be repaired is a battery (Fig. 1, 14) for driving a travel motor (Par. 78, “electric vehicle”) mounted on a vehicle (Fig. 1, 12). In claim 10, Takahashi discloses a repair content prediction device (Fig. 1) comprising: an acquisition unit (Fig. 1, 16) configured to acquire operation history information (Par. 64 “history”) and failure state description information (Par. 7, “end of life”) about a device to be repaired (Fig. 1, 12); a prediction unit (Fig. 1, 22, 44, 80) configured to predict a repair content candidate for the device to be repaired (Fig. 5 130) based on a learned repair content prediction model (Fig. 5, 124, 126) and the operation history information and the failure state description information acquired by the acquisition unit (Fig. 5, 120); and an output unit (Par. 79 “display”) configured to output the repair content candidate predicted by the prediction unit (Fig. 8, Par. 66, Fig. 5, 132). In claim 11, Takahashi discloses a computer-readable recording medium (Fig. 1 26) recording a program (Par. 2 “program”) for causing an information processing device (Fig. 1, 22, 44, 80) to execute: acquiring operation history information (Par. 64 “history”) and failure state description information (Par. 7, “end of life”) about a device to be repaired (Fig. 1, 12); predicting a repair content candidate for the device to be repaired (Fig. 5 130) based on a learned repair content prediction model (Fig. 5, 124, 126) and the acquired operation history information and the failure state description information (Fig. 5, 120); and outputting the predicted repair content candidate (Fig. 8, Par. 66, Fig. 5, 132). In claim 12, Takahashi discloses a method for creating a repair content prediction model (Fig. 2), the method comprising, by an information processing device (Fig. 1, 22, 44, 80), creating a repair content prediction model to predict repair content (Fig. 2) based on at least one of operation history information (Par. 64 “history”) and failure state description information on a faulty device (Par. 7, “end of life”) by machine learning (Par. 54-55 “learning using the partial time-series” “neural network”) using, as teacher data, the operation history information, the failure state description information, and repair record information about each of a plurality of the faulty devices (Fig. 5, 124, 126) for which repair is previously executed (Par. 44 “already replaced”) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20190084657 A1, CONDITIONAL ONLINE-BASED RISK ADVISORY SYSTEM (COBRAS); US 20180276913 A1, REMOTE VEHICLE NETWORK MONITORING AND FAILURE PREDICTION SYSTEM; US 10042359 B1, Autonomous Vehicle Refueling; Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON J BECKER whose telephone number is (571)431-0689. The examiner can normally be reached M-F 9:30-5:30. 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, Shelby Turner can be reached at (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /B.J.B/ Examiner, Art Unit 2857 /SHELBY A TURNER/ Supervisory Patent Examiner, Art Unit 2857 Application/Control Number: 18/509,017 Page 2 Art Unit: 2857 Application/Control Number: 18/509,017 Page 3 Art Unit: 2857 Application/Control Number: 18/509,017 Page 4 Art Unit: 2857 Application/Control Number: 18/509,017 Page 5 Art Unit: 2857 Application/Control Number: 18/509,017 Page 6 Art Unit: 2857 Application/Control Number: 18/509,017 Page 7 Art Unit: 2857 Application/Control Number: 18/509,017 Page 8 Art Unit: 2857 Application/Control Number: 18/509,017 Page 9 Art Unit: 2857 Application/Control Number: 18/509,017 Page 10 Art Unit: 2857 Application/Control Number: 18/509,017 Page 11 Art Unit: 2857 Application/Control Number: 18/509,017 Page 12 Art Unit: 2857 Application/Control Number: 18/509,017 Page 13 Art Unit: 2857 Application/Control Number: 18/509,017 Page 14 Art Unit: 2857 Application/Control Number: 18/509,017 Page 15 Art Unit: 2857 Application/Control Number: 18/509,017 Page 16 Art Unit: 2857 Application/Control Number: 18/509,017 Page 17 Art Unit: 2857 Application/Control Number: 18/509,017 Page 18 Art Unit: 2857
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