DETAILED ACTION
This action is responsive to papers filed on 4/27/2026.
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 .
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 and 3-20 are rejected under 35 U.S.C. 101 because, while the claims herein are directed to a method and/or system, which could be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes), 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.
Regarding claims 1, 14, 19, the claims recite, in part, receiving user information that describes at least one of user preferences and personality characteristics for multiple users; transforming the user information and coach information in a vector space; establishing a vector index that includes the transformed coach information and the transformed user information; for a unique user, filtering the established vector index to generate a set of candidates, in which filtering includes identifying the set of candidates for the unique user; determining a score for each of the candidates; refining the set of candidates using a processing environment to identify a subset of candidates; and transmitting the identified subset of candidates to the unique user.
The limitations, as drafted and detailed above, recites identification and transmission of candidate coach recommendations to a user, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, and more specifically can be considered as commercial interactions or managing personal behavior or relationships or interactions between people. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of remote determination system (claims 1, 14, 19), artificial intelligence subsystem (claims 1, 14, 19, merely used in an “apply it” manner), ranking model trained with one or more pointwise ranking objectives (claims 1, 14, 19, merely used in an “apply it” manner), hard-coded environment that uses a point-based ordering system (claims 1, 14, 19, merely used in an “apply it” manner); one or more processors (claims 1, 19), one or more hardware-based memory devices (claim 1), subsystems (claims 1, 14, 19), and one or more hardware-based non-transitory computer-readable memory devices (claim 19). The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of receiving, transforming, establishing, filtering, determining, refining, and transmitting) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. There are no additional functional limitations to be considered under prong two.
Accordingly, the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) 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 (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the
judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes).
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using remote determination system (claims 1, 14, 19), artificial intelligence subsystem (claims 1, 14, 19, merely used in an “apply it” manner), ranking model trained with one or more pointwise ranking objectives (claims 1, 14, 19, merely used in an “apply it” manner), hard-coded environment that uses a point-based ordering system (claims 1, 14, 19, merely used in an “apply it” manner); one or more processors (claims 1, 19), one or more hardware-based memory devices (claim 1), subsystems (claims 1, 14, 19), and one or more hardware-based non-transitory computer-readable memory devices (claim 19) to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. 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 the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent- eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat' l Ass' n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general purpose computer (see Applicant specification Figures 2, 15, Paragraphs 0070-0075, “general purpose computing system”, software alone is not enough to “transform” a general purpose computer into a “special purpose computer”); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility.
The dependent claims claims 2-13, 15-18, and 20 appear to merely limit specifics of the subsystems, specifics of the identified set of candidates, “apply it” use of an artificial intelligence engine within a machine learning environment, removal of a candidate that doesn’t meet a threshold, inclusion of a “hard-coded environment” leveraging a rule based ordering system, interoperability of the hard-coded and AI/ML environments, specifying that the vector index and filtration steps operating in distinct “containers”, an order of use of the subsystems, and specifics of the set of candidates, and therefore only limit the application of the idea, and not add significantly more than the idea (i.e. “PEG” Step 2B=No).
The remote determination system (claims 1, 14, 19), artificial intelligence subsystem (claims 1, 14, 19, merely used in an “apply it” manner), ranking model trained with one or more pointwise ranking objectives (claims 1, 14, 19, merely used in an “apply it” manner), hard-coded environment that uses a point-based ordering system (claims 1, 14, 19, merely used in an “apply it” manner); one or more processors (claims 1, 19), one or more hardware-based memory devices (claim 1), subsystems (claims 1, 14, 19), and one or more hardware-based non-transitory computer-readable memory devices (claim 19) are each functional generic computer components that perform the generic functions of receiving, transforming, establishing, filtering, determining, refining, and transmitting, all common to electronics and computer systems.
Applicant's specification does not provide any indication that the remote determination system (claims 1, 14, 19), artificial intelligence subsystem (claims 1, 14, 19, merely used in an “apply it” manner), ranking model trained with one or more pointwise ranking objectives (claims 1, 14, 19, merely used in an “apply it” manner), hard-coded environment that uses a point-based ordering system (claims 1, 14, 19, merely used in an “apply it” manner); one or more processors (claims 1, 19), one or more hardware-based memory devices (claim 1), subsystems (claims 1, 14, 19), and one or more hardware-based non-transitory computer-readable memory devices (claim 19) are anything other than generic, off-the-shelf computer components. Therefore, the claims do not amount to significantly more than the abstract idea (i.e. “PEG” Step 2B=No).
Thus, based on the detailed analysis above, claims 1 and 3-20 are not patent eligible.
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, 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, 3-8, 12-14, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Thompson (U.S. Pub No. 2015/0154721) in view of Ma (U.S. Pub No. 2018/0150464).
EXAMINER NOTE: Applicant never defines the term “coach” in the specification. According to Paragraph 0002 of the instant specification, Applicant refers to the user as a “patient”, and uses the terms physician and therapist as being synonymous to “coach”. Therefore, a wellness professional, as taught by Thompson, is believed to represent a “coach”.
Regarding claims 1, 14, 19, Thompson teaches receiving user information that describes at least one of user preferences and personality characteristics for multiple users (Paragraphs 0076, 0088, collection of wellness data from patient, 0048, patient database, system is not limited to one patient and collects data from multiple users); transforming the user information and coach information in a vector space (Paragraph 0135, data is collected through the front stage and is used to create the assigned vector of indices), establishing a vector index that includes coach information and the received user information (Paragraphs 0109, 0135-0136, the comparison of wellness indices for user information and vector indices for wellness professional or “coach” information operate as a “vector index”); for a unique user, using an artificial intelligence subsystem to filter the established vector index to generate a set of candidates, in which filtering includes leveraging subsystems to identify an appropriate set of candidates for the unique user (Paragraphs 0127, artificial intelligence, 0135-0136, comparing wellness indices to vector indices to determine a group of candidate professionals represents the claimed filtering); refining the generated set of candidates using a point-based ordering system to identify a subset of candidates (Paragraphs 0104, refining to identify only professionals who are available, 0118-0119, point-based ordering, 0137, match according to groups and then identify a subset based on individual matching); and transmitting the identified subset of candidates to the unique user (Paragraph 0102).
While Thompson teaches pointwise ranking objectives (Paragraphs 0118-0119), Thompson does not appear to specify using a ranking model trained with one or more pointwise ranking objectives to determine a score for each of the candidates. However, Ma teaches using a ranking model trained with one or more pointwise ranking objectives to determine a score for each of the candidates (Paragraphs 0044, user-engagement model represents a “ranking model” that scores candidates and ranks them, 0046, training using past success metrics which represent objectives). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention use a ranking model since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Thompson does not appear to specify a hard-coded environment. However, hard coding has been old and well known long before the filing of Applicant’s invention. Hard coding is a fundamental concept in software development that has been around as long as programming languages themselves. It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to hard code any ordering system since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 3, Thompson teaches the subsystems include an NLP (natural language processing) subsystem or user-defined policies and criteria subsystem (Paragraphs 0100, user enters information and this information is used in the filtering step, the software used to analyze and apply this information using programmed criteria is considered to be a “user-defined policies and criteria subsystem”, 0128-0129, natural language processing may be used as well, and this software is considered to be an “NLP subsystem”).
Regarding claim 4, Thompson teaches the identified set of candidates for the unique user includes identifying candidate sets or a single set of top candidates (Paragraphs 0047, 0100, 0106, 0108-0109).
Regarding claim 5, Thompson teaches the processing environment for refining the generated set of candidates includes an Al (artificial intelligence) engine operating within an AI/ML (machine learning) environment to determine the subset of candidates (Paragraphs 0127, 0134, 0138).
Regarding claim 6, Thompson does not appear to specify when a candidate does not satisfy a threshold score associated with the unique user, the candidate is removed from the set of candidates by the point-based ordering system. However, Ma teaches when a candidate does not satisfy a threshold score associated with the unique user, the candidate is removed from the set of candidates by the point-based ordering system (Paragraphs 0037, 0042). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to remove users that are below a threshold score since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 7, Thompson teaches the processing environment further includes a rule-based ordering system to determine the subset of candidates (Paragraphs 0118-0119).
Thompson does not appear to specify a hard-coded environment. However, hard coding has been old and well known long before the filing of Applicant’s invention. Hard coding is a fundamental concept in software development that has been around as long as programming languages themselves. It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to hard code any ordering system since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 8, Thompson teaches the ordering system and AI/ML environments interoperate with each other to identify the subset of candidates (Paragraphs 0118-0119, 0127, 0134, 0138).
Thompson does not appear to specify a hard-coded environment. However, hard coding has been old and well known long before the filing of Applicant’s invention. Hard coding is a fundamental concept in software development that has been around as long as programming languages themselves. It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to hard code any ordering system since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 12, 17, Thompson teaches the set of candidates comprise top rated candidates based on the filtering (Paragraphs 0047, 0100, 0106, 0108-0109).
Regarding claims 13, 18, Thompson teaches the subset of candidates comprise top candidates that satisfy a threshold (Paragraph 0135).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Thompson (U.S. Pub No. 2015/0154721) in view of Ma (U.S. Pub No. 2018/0150464), and further in view of Containerization Wiki (https://en.wikipedia.org/w/index.php?title=Containerization_(computing)&oldid=1074594483, 3/1/2022).
Regarding claim 9, Thompson does not appear to specify the vector index and filtration steps operate in two distinct and independent containers within the remote determination system. However, Containerization Wiki teaches that implementing separate computer processes within separate containers has been old and well known long before the filing of Applicant’s invention. It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to operate any of the claimed processes, including the vector index and filtration steps, in separate containers since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 10, 11, 15, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Thompson (U.S. Pub No. 2015/0154721) in view of Ma (U.S. Pub No. 2018/0150464), and further in view of Bao (U.S. Patent No. 11/556,836).
Regarding claims 10, 15, 20, Thompson teaches a NLP (natural language processing) subsystem (Paragraphs 0128-0129, natural language processing may be used as well, and this software is considered to be an “NLP subsystem”), a user-defined policies and criteria subsystem to apply criteria to the coach information and the user information to determine the set of candidates (Paragraphs 0100, user enters information and this information is used in the filtering step, the software used to analyze and apply this information using programmed criteria is considered to be a “user-defined policies and criteria subsystem”), and a machine learning/artificial intelligence subsystem to receive the coach information and the user information to determine the set of candidates (Paragraphs 0127, 0134, 0138).
Thompson does not appear to specify a NLP (natural language processing) subsystem to parse the coach information and the user information to determine the set of candidates. However, Bao teaches a NLP (natural language processing) subsystem to parse the coach information and the user information to determine the set of candidates (Column 10 Lines 61-67, match business user data to specialist data, Column 17 Lines 30-35, Column 20 Lines 15-20, NLP used to identify business user attributes and specialist attributes). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to parse the data with NLP since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 11, 16, Thompson teaches the NLP subsystem is used first, the user-defined policies and criteria subsystem and the machine learning/artificial intelligence subsystem are used after the NLP subsystem (Paragraphs 0128-0129, any language data will need to be recognized before it’s able to be analyzed).
Response to Arguments
Applicant argues “none of the claims recite a commercial interaction. Also, the claim limitations involve transforming information and transmitting candidates, for example. The claims do not recite operations that alter the user's behavior or manage relationships or interactions between candidates and the user”. However, the entire system is designed to target a list of coaches to a specific user. This act of targeting and delivery of the list of coaches is a step of advertising those coaches to the user. Further, providing contacts to a user qualifies as managing relationships. Finally, the claims draw a direct parallel to the abstract idea identified in Electric Power Group, “collecting information, analyzing it, and displaying certain results of the collection and analysis”, and Int. Ventures v. Cap One Bank ‘382 Patent, “tailoring content based on information about the user”. Therefore, the claims do indeed recite an abstract idea, as outlined above.
Applicant references the transforming language added to the claims and argues “Claim 1 improves data such as the coach information and user information and improves how the artificial intelligence subsystem operates”. However, the converting of one data type into another does not produce an improvement to the additional elements. Rather, this conversion merely provides the data in a form useable by the AI subsystem. The operation of the AI subsystem is not improved, as it still operates as intended to apply the abstract idea. It is also not believed that the data is actually improved either. However, even if it could be argued that the data is improved, the data is not an additional element. The data itself is part of the abstract idea, and any improvement to the abstract idea is non-statutory. In the SAP decision (See SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018)), the courts found that an improvement made to the abstract idea is not patent eligible. SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.
Applicant argues “Like Desjardins, the specification of the instant application identifies improvements as to how an artificial intelligence (AI) subsystem, such as a machine learning model, operates. Transforming the user and coach information and establishing a structured vector index with the transformed information makes the data more useful and streamlined for ingestion by the AI subsystem. Moreover, transforming the data and establishing the structured vector index allows the AI subsystem to perform complex operations to filter data in the structured vector index to identify the set of candidates and improves subsequent analysis such as determining a score for each of the candidates and refining the candidates. Transforming the data and establishing the structured vector index provide an improvement to the AI subsystem itself” and “Transforming the user and coach information and establishing a structured vector index with the transformed information is significant more than just using the user and coach information. Transforming the information and establishing the structured vector index improves the data ingested by the AI subsystem and is significant more than just providing untransformed and unstructured data”. However, with regard to Desjardin, the additional element of the machine learning itself was deemed to be improved based on discussion and evidence from the specification. There is no such improvement to the additional elements recited in the instant specification for the current claim set. Applicant cites paragraph 0040 for evidence of an improvement. However, a human user being unable to process a large amount of information is a natural biproduct of machine learning, artificial intelligence, and computer automation. The AI itself is not improved, but rather is operating as intended and is merely being applied to the abstract idea.
All other arguments are believed to have been addressed by the new grounds of rejection above.
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.
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/MICHAEL BEKERMAN/ Primary Examiner, Art Unit 3621
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