DETAILED ACTION
This action is in response to the application filed 5 October 2023, claiming benefit back to 22 June 2022.
Claims 1 – 16 are pending and have been examined.
This action is Non-Final.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5 October 2023 has been considered by the examiner.
Continuation
This application is a continuation application of U.S. application no.18/210,428 filed on 15 June 2023, (“Parent Application”). See MPEP §201.07. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to:
www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1 – 12, 15 and 16 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over corresponding claims of copending Application No. 18/210,428 (reference application).
Claim 1 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/210,428 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because, as shown in the comparison of the claims below, the only difference between claim 1 of the instant application and claim 1 of 18/210,428 is that the sample in 18/210,428 is replaced by trainee in the instant application, and the having an output error of a (t+1l)-th order machine learning model with respect to observation data at time t+1 in 18/210,428 is replaced with having an output error of a next timing machine learning model of a next timing with respect to observation data at the next timing being larger than a predetermined amount in the instant application. As per Applicant’s disclosure (in light of the 112(a) rejection, below), these are equivalent limitations, and as such claim 1 of the 18/210,428 would at least render obvious, if not anticipate, claim 1 of the instant application.
U.S. 18/210,428
1. A machine learning model generation apparatus comprising:
at least one memory; and
at least one processor configured to be constituted in such a way as to execute an instruction stored in the at least one memory, wherein the at least one processor executes:
movement processing of moving a sample, among a plurality of samples included in a target sample group, having an output error of a (t+1l)-th order machine learning model with respect to observation data at time t+1 (t is a natural number) being larger than a predetermined amount, from the target sample group to a source sample group,
processing of generating a plurality of weak learners by using at least observation data from time t to time T of at least one sample included in the target sample group after the movement processing and at least one sample included in the source sample group after the movement processing, and
processing of generating a t-th order machine learning model, based on at least each of the plurality of generated weak learners, and a classification error being evaluated, for each of the plurality of generated weak learners, by using 20 observation data at time t of the at least one sample included in the target sample group after the movement processing,
the observation data include at least a state and an action of a sample at a specific time until time T, and
the t-th order machine learning model outputs an action at time t by using at least a state at time t as an input.
Instant Application
1. A machine learning model generation apparatus comprising:
at least one memory; and
at least one processor configured to be constituted in such a way as to execute an instruction stored in the at least one memory, wherein the at least one processor executes:
movement processing of moving a trainee, among a plurality of trainees included in a target trainee group, having an output error of a next timing machine learning model of a next timing with respect to observation data at the next timing being larger than a predetermined amount, from the target trainee group to a source trainee group, the next timing being a timing next to a target timing;
processing of generating a plurality of weak learners by using at least observation data from the target timing to a last timing of at least one trainee included in the target trainee group after the movement processing and at least one trainee included in the source trainee group after the movement processing; and
processing of generating a target timing machine learning model of the target timing, based on at least each of the plurality of generated weak learners, and a classification error being evaluated, for each of the plurality of generated weak learners, by using observation data at the target timing of the at least one trainee included in the target trainee group after the movement processing, and
the observation data include at least a state and treatment of a trainee at a specific time until the last timing,
the target timing machine learning model outputs treatment at the target timing by using at least a state at the target timing as an input.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 2 – 12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2 – 11 and 14 of copending Application No. 18/210,428 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other using the same rationale as discussed in respect to claim 1.
Claim 15 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 12 of copending Application No. 18/210,428 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because, as discussed in respect to claim 1, the only differences between the claims are the equivalent limitations, and as such claim 12 of the 18/210,428 would at least render obvious, if not anticipate, claim 15 of the instant application.
Claim 16 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 13 of copending Application No. 18/210,428 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because, as discussed in respect to claim 1, the only differences between the claims are the equivalent limitations, and as such claim 13 of the 18/210,428 would at least render obvious, if not anticipate, claim 16 of the instant application.
Claims 1 – 16 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over corresponding claims of copending Application No. 18/481,426 (reference application).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 1 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/481,426 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because, as shown in the comparison of the claims below, the only difference between claim 1 of the instant application and claim 1 of 18/481,426 is that the patient in 18/481,426 is replaced by in the instant application. As per Applicant’s disclosure, these are equivalent limitations, and as such claim 1 of the 18/481,426 would at least render obvious, if not anticipate, claim 1 of the instant application.
18/481,426
1. A machine learning model generation apparatus comprising:
at least one memory; and
at least one processor configured to be constituted in such a way as to execute an instruction stored in the at least one memory, wherein the at least one processor executes:
movement processing of moving a patient, among a plurality of patients included in a target patient group, having an output error of a next timing machine learning model of a next timing with respect to observation data at the next timing being larger than a predetermined amount, from the target patient group to a source patient group, the next timing being a timing next to a target timing;
processing of generating a plurality of weak learners by using at least observation data from the target timing to a last timing of at least one patient included in the target patient group after the movement processing and at least one patient included in the source patient group after the movement processing; and
processing of generating a target timing machine learning model of the target timing, based on at least each of the plurality of generated weak learners, and a classification error being evaluated, for each of the plurality of generated weak learners, by using observation data at the target timing of the at least one patient included in the target patient group after the movement processing, and
the observation data include at least a state and treatment of a patient at a specific time until the last timing,
the target timing machine learning model outputs treatment at the target timing by using at least a state at the target timing as an input.
Instant Application
1. A machine learning model generation apparatus comprising:
at least one memory; and
at least one processor configured to be constituted in such a way as to execute an instruction stored in the at least one memory, wherein the at least one processor executes:
movement processing of moving a trainee, among a plurality of trainees included in a target trainee group, having an output error of a next timing machine learning model of a next timing with respect to observation data at the next timing being larger than a predetermined amount, from the target trainee group to a source trainee group, the next timing being a timing next to a target timing;
processing of generating a plurality of weak learners by using at least observation data from the target timing to a last timing of at least one trainee included in the target trainee group after the movement processing and at least one trainee included in the source trainee group after the movement processing; and
processing of generating a target timing machine learning model of the target timing, based on at least each of the plurality of generated weak learners, and a classification error being evaluated, for each of the plurality of generated weak learners, by using observation data at the target timing of the at least one trainee included in the target trainee group after the movement processing, and
the observation data include at least a state and an action of a trainee at a specific time until the last timing,
the target timing machine learning model outputs an action at the target timing by using at least a state at the target timing as an input.
Claims 2 – 14 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2 – 14 of copending Application No. 18/481,426 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other using the same rationale as discussed in respect to claim 1.
Claim 15 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 12 of copending Application No. 18/481,426 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because, as discussed in respect to claim 1, the only differences between the claims are the equivalent limitations, and as such claim 15 of the 18/481,426 would at least render obvious, if not anticipate, claim 15 of the instant application.
Claim 16 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 13 of copending Application No. 18/481,426 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because, as discussed in respect to claim 1, the only differences between the claims are the equivalent limitations, and as such claim 16 of the 18/481,426 would at least render obvious, if not anticipate, claim 16 of the instant application.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
It has been held that when the written description does not explicitly disclose a limitation added to a claim, “it must be shown that a person of ordinary skill in the art would have understood, at the time the application was filed, that the description requires that limitation” (Hyatt v. Boone, 47 USPQ2d 1128). While exact wording of claim (“in haec verba”) is not required, limitations must be supported through express, implicit or inherent disclosure (MPEP 2163.I.B. and II.A.3(b)).
Claim 1 recites the limitations of movement processing of moving a trainee, among a plurality of trainees included in a target trainee group …from the target trainee group to a source trainee group…to a last timing of at least one trainee included in the target trainee group after the movement processing and at least one trainee included in the source trainee group after the movement processing …at the target timing of the at least one trainee included in the target trainee group after the movement processing, and the observation data include at least a state and an action of a trainee at a specific time until the last timing…; however Examiner is unable to find support for the newly amended limitation of trainee in Applicant’s originally filed specification. As such, this is considered to be new matter, and is rejected under 35 USC 112(a) for failure to show possession.
Claims 2 – 14 depend on claim 1, and are rejected using the same rationale.
Claims 15 and 16 recite the same new matter as discussed in claim 1, and is rejected using the same rationale.
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(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 pre-AIA the applicant regards as the invention.
Claim 1 recites movement processing of moving a trainee, among a plurality of trainees included in a target trainee group, however this limitation is indefinite, as it is unclear if a trainee (i.e., a actual human) is being moved, and if so, how the computer is moving said person, or if this limitation is referring to data about a trainee that is being moved by the computer. Clarification and / or correction is requested.
Claims 2 – 14 are rejected as being dependent from claim 1 and for having the same deficiencies under 35 USC 112(b).
Claims 15 and 16 recite substantially similar limitations as to those found in claim 1, and are rejected using the same rationale.
Claim 2 further recites the limitation wherein the at least one processor moves, after discarding a trainee included in the source trainee group, a trainee having an output error…, however this limitation is indefinite, as it is unclear if a trainee (i.e., a actual human) is being moved and discarded, and if so, how the computer is moving and discarding said person, or if this limitation is referring to data about a trainee that is being moved by the computer. Clarification and / or correction is requested.
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 non-statutory subject matter. The claimed invention, when the claims are taken as a whole, is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 2A – 1: The claims recite a Judicial Exception. Exemplary independent claim 15 recites the limitations of:
executing movement processing of moving a trainee, among a plurality of trainees included in a target trainee group, having an output error of a next timing machine learning model of a next timing with respect to observation data at the next timing being larger than a predetermined amount, from the target trainee group to a source trainee group, the next timing being a timing next to a target timing;
generating a plurality of weak learners by using at least observation data from the target timing to a last timing of at least one trainee included in the target trainee group after the movement processing and at least one trainee included in the source trainee group after the movement processing; and
generating a target timing machine learning model of the target timing, based on at least each of the plurality of generated weak learners, and a classification error being evaluated, for each of the plurality of generated weak learners, by using observation data at the target timing of the at least one trainee included in the target trainee group after the movement processing, wherein
the observation data include at least a state and treatment of a trainee at a specific time until the last timing, and
the target timing machine learning model outputs treatment at the target timing by using at least a state at the target timing as an input.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation, covers the performance of mathematical algorithms and calculations. The movement processing, generation of weak learners, and the generating a target timing machine learning model are all mathematical calculations (see, e.g., Applicant’s disclosure, FIG. 18 and pages 6 – 9, Mathematical 1; pages 10 – 17, Mathematical 2, Mathematical 3, Mathematical 4, Mathematical 5, Mathematical 6, Mathematical 7, Mathematical 8, and Mathematical 9, etc.). See MPEP 2106.04(a)(2).
Step 2A – 2: This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. While independent claim 15 does not recite any additional elements other than the judicial exception, independent claim 1 recites the additional elements of at least one memory and at least one processor, however these are recited at a high level of generality, the computer is used as a tool to perform the abstract idea. See MPEP 2106.05(f). Claim 16 recites the additional limitation of a non-transitory computer readable medium, however this amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Further, the claims do not provide for or recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The claim is directed to the abstract idea.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims, and it has been held that “[i]n defining the excluded categories, the Court has ruled that the exclusion applies if a claim involves a natural law or phenomenon or abstract idea, even if the particular natural law or phenomenon or abstract idea at issue is narrow.” (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350. )
Turning to the dependent claims, none of the claimed features of the dependent claims further limit the claimed invention in such a way to direct the claimed invention to statutory subject matter (e.g. change the scope of the claimed invention as to no longer be directed towards an abstract idea, or include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements or combination of elements in the claims other than the abstract idea per se), nor do they add limitations that, when taken as a combination, result in the claim as a whole amounting to significantly more than the judicial exception. In respect to dependent claims 2 – 11 and 14;
Claim 2 merely further describes the judicial exception, in this case moving trainees from one group to another;
Claims 3 – 6 merely further describe the judicial exception, in this case the generation of weak learners by using weights;
Claim 7 merely describes the data used in the mathematical calculations;
Claim 8 merely further describes the judicial exception;
Claim 9 merely further describes the judicial exception, in this case changing a reduction amount;
Claim 10 merely further describes the judicial exception, in this case an additional mathematical calculation;
Claim 11 merely further describes the judicial exception, in this case an additional mathematical calculations;
Claim 12 merely describes the meaning of the variables or results output by a model.
Claims 13 and 14 merely further describe the judicial exception, in this case an additional mathematical calculations.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, explained with respect to Step 2A, Prong Two, the additional elements or combination of elements in the claims other than the abstract idea per se amount to no more than mere instructions to implement the idea on a computer, or the recitation of generic computer structure that serves to perform generic computer functions previously known to the industry1 [e.g. performing repetitive calculations; receiving, processing, and storing data; electronically scanning or extracting data from a physical document; electronic recordkeeping; automating mental tasks; receiving or transmitting data over a network, e.g., using the Internet to gather data] .
Applicant’s specification, at, e.g., pages 26 – 28, provides evidence of generic computer hardware performing generic, well-known, computer functions.
Viewed as a whole, these additional claim elements, both individually and in combination, do not provide meaningful limitations to transform the above identified abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more (e.g. improvements to another technology or technical fields, improvements to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment) than the abstract idea itself. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation2.
Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, 573 U.S. No. 13–298.
Allowable Subject Matter
Claims 1 – 16 would be allowable if the rewritten or amended to overcome the rejections under 35 U.S.C. 112(b) and 35 U.S.C. 101, set forth in this Office action.
The closest prior art of record includes Htike, Kyaw Kyaw. "Efficient determination of the number of weak learners in AdaBoost." Journal of Experimental & Theoretical Artificial Intelligence 29.5 (2017): 967-982.; Abe et al. (U.S. 2010/0042561), which discloses and is directed to methods and systems for cost-sensitive boosting; Skogstad (U.S.2022/0327422 ), which discloses and is directed to a system and method for real-time artificial intelligence situation determination based on distributed device event data; Kim et al. (U.S.2021/0073036), which discloses and is directed to computational resource allocation in ensemble machine learning systems; Movellan et al. (U.S.2005/0102246), which discloses and is directed to a weak hypothesis generation apparatus and method, learning apparatus and method, detection apparatus and method, facial expression learning apparatus and method, facial expression recognition apparatus and method, and robot apparatus; and Sato (JP 2022135733), which discloses and is directed to a system, device, and method for managing progress and program.
However, with respect to exemplary independent claim 1, none of the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN S MILLER whose telephone number is (571)270-5288. The examiner can normally be reached on M-F 10am-6pm. Examiner’s fax phone number is (571) 270-6288.
Examiner interviews are available via telephone 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, Beth Boswell can be reached at (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALAN S MILLER/Primary Examiner, Art Unit 3625
1 “It is well-settled that mere recitation of concrete, tangible components is insufficient to confer patent eligibility to an otherwise abstract idea. Rather, the components must involve more than performance of “‘well understood, routine, conventional activit[ies]’ previously known to the industry.” Alice, 134 S. Ct. at 2359 (quoting Mayo, 132 S.Ct. at 1294)”. Id, pages 10-11. “Likewise, the server fails to add an inventive concept because it is simply a generic computer that “administer[ s]” digital images using a known “arbitrary data bank system.” Id. at col. 5 ll. 45–46. But “[f]or the role of a computer in a computer-implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of ‘well-understood, routine, [and] conventional activities previously known to the industry.’” Content Extraction, 776 F.3d at 1347–48 (quoting Alice, 134 S. Ct at 2359). “These steps fall squarely within our precedent finding generic computer components insufficient to add an inventive concept to an otherwise abstract idea. Alice, 134 S. Ct. at 2360 (“Nearly every computer will include a ‘communications controller’ and a ‘data storage unit’ capable of performing the basic calculation, storage, and transmission functions required by the method claims.”); Content Extraction, 776 F.3d at 1345, 1348 (“storing information” into memory, and using a computer to “translate the shapes on a physical page into typeface characters,” insufficient confer patent eligibility); Mortg. Grader, 811 F.3d at 1324–25 (generic computer components such as an “interface,” “network,” and “database,” fail to satisfy the inventive concept requirement); Intellectual Ventures I, 792 F.3d at 1368 (a “database” and “a communication medium” “are all generic computer elements”); BuySAFE v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (“That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.”)”. TLI Communications LLC v. AV Automotive L.L.C., (No. 15-1372, (Fed. Cir. May 17, 2016)), at *12-13.
See additionally MPEP 2106.05(d).
2 “Nor, in addressing the second step of Alice, does claiming the improved speed or efficiency inherent with applying the abstract idea on a computer provide a sufficient inventive concept. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”); CLS Bank, Int’l v. Alice Corp., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) aff’d, 134 S. Ct. 2347 (2014) (“[S]imply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.” (citations omitted))”. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 115 U.S.P.Q.2d 1636 (Fed. Cir. 2015).