DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Amendment
Applicant’s amendment and remarks filed January 22, 2026, are responsive to the office action mailed December 18, 2024, and the notice of non-responsive amendment mailed October 22, 2025. Claims 2-11 were previously pending. Claims 2, 4, and 7, have been amended and claims 3 and 5-6 have been cancelled. Claims 21-27 are new. Claims 2, 4, 7-11, and 21-27, are therefore currently pending and considered in this office action.
Pertaining to rejection under 35 USC § 102 in the previous office action
Claims 2-11 were rejected under 35 U.S.C. 102(a)(2) as being anticipated by Flinn (Paper No. 20241211; Pub. No. US 2020/0286009 A1). The amendment has traversed this ground of rejection of the claims.
Response to Arguments
Pertaining to rejection under 35 USC § 101 in the previous office action
Applicant's arguments filed January 22, 2026, have been fully considered but they are not persuasive. Claims 2, 4, 7-11, and 21-27, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Applicant argues the claims include “limitations describing the topological structure of the neural network nodes.” Remarks p.7. Examiner responds that merely describing the data being stored is not sufficient to integrate the abstract idea into a practical application because it does not recite the neural network topology in a way that clearly claims its function in response to the query. As presently claimed it appears to simply recite passive storage of data in relation to other data and updating (i.e., reconciling) said data in other topologies. This is the abstract idea and there is no recitation sufficient to integrate it in a practical application. This is explained in the updated rejection.
Pertaining to rejection under 35 USC § 102 in the previous office action
Applicant’s argument, see remarks filed January 22, 2026, with respect to the rejection of claims 2-11 under 35 U.S.C. 102(a)(2) as being anticipated by Flinn (Paper No. 20241211; Pub. No. US 2020/0286009 A1), have been fully considered and are persuasive because they indicate that the amendment has traversed this ground of rejection. The rejection has therefore been withdrawn. However, upon further consideration, a new ground of rejection is made under 35 USC 103.
With regard to claim 7 applicant's arguments have been fully considered but they are not persuasive. In order to include one or more filtered clusters the prior art by necessity must include at least one filter and since those filtered clusters are used to generate usage behavior patterns that are used to generate recommendations, at least the previous version of claim 7 is disclosed by the prior art. Applicants argument that the filter is used for
“"error correction and data verification" to provide "accurate recommendations". Original Specification at page 55, line 29 - page 56, line 2 ("It is likely that the recommendations will already be tailored somewhat towards these feature sets based on the nodal links formed by the matrix builder 126 based on user-expressed affinity but the error correction and data verification processing provides an extra filter of protection to ensure a more accurate recommendation to the user.")” (Remarks p.12)
indicates that applicant intends the filter of claim to be applied to a set of recommendations after the set has been generated to achieve an additional round of filtering. This is not clearly recited in the claim. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
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 2, 4, 7-11, and 21-27, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter) (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception (step 2B). Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 189 L. Ed. 2d 296, 2014 U.S. LEXIS 4303, 110 U.S.P.Q.2D (BNA) 1976, 82 U.S.L.W. 4508, 24 Fla. L. Weekly Fed. S 870, 2014 WL 2765283 (U.S. 2014); MPEP 2106.
Step 1:
In the instant case claims 2, 4, 7-11, and 21-27, are directed to a process. All claims are therefore within statutory categories. See MPEP 2106.03, Eligibility Step 1.
Step 2A, Prong 1:
These claims also recite, inter alia,
“receiving, on behalf of a business, a data set comprising a plurality of items; by ... a recommendation system, generating and/or updating, based at least in part on the data set, a personalized neural network topology associated with the business, the personalized neural network topology comprising a plurality of nodes, each node of a portion of the plurality of the nodes representing i) a product of a plurality of products, wherein each product represents a product offered by the business, ii) a purchaser of a plurality of purchasers, wherein each purchaser represents a reviewer of one or more products of the portion of the plurality of products or a customer who previously bought at least one product of the portion of the plurality of products, and iii) an item of a plurality of items, each item having a relationship with one or more of the plurality of products of a purchaser of the plurality of purchasers, and a plurality of nodal links between the plurality of nodes, at least one of the plurality of nodal links representing a positive affinity among the plurality of items and at least one of the plurality of nodal links representing a negative affinity among the plurality of items, wherein generating the personalized neural network topology comprises augmenting the data set with additional information previously gathered by the recommendation system, wherein a plurality of other neural network topologies of the recommendation system comprise portions of the additional information; storing, to a ... data storage system, the personalized neural network topology in association with the business; determining ... whether one or more relationships exist between at least a portion of the data set and one or more nodes of each of at least a portion of the plurality of other neural network topologies; responsive to determining the one or more relationships exist, updating ... each respective neural network topology of the portion of the plurality of other neural network topologies, thereby enhancing interrelationships within each respective neural network topology; receiving, from ... the business, a request identifying at least one item of the plurality of items; ... responsive to receiving the request, applying the personalized neural network topology to generate a set of one or more recommended items of a population of items including the plurality of items; and providing ... information regarding the set of one or more recommended items.” Claim 2.
A careful analysis of the above limitations, each on its own and all together combined, results in the conclusion that each on its own recites an abstract idea and in combination they altogether simply recite a more detailed abstract idea. The recited abstract ideas fall within the groupings of abstract ideas described as mental processes such as concepts performed in the human mind (including an observation, evaluation, judgment) and certain methods of organizing human activity, for example commercial interactions (including advertising, marketing or sales activities or behaviors; business relations). See MPEP 2106.04(a); Eligibility Step 2A1. The claims must therefore be analyzed under the second prong of Eligibility Step 2 (Step 2A2; MPEP 2106.04(d)).
Step 2A, Prong 2:
In order to address prong 2 (MPEP 2106.04(d), Eligibility Step2A2) we must identify whether there are any additional elements beyond the abstract ideas and determine whether those additional elements (if there are any) integrate the abstract idea into a practical application. MPEP 2106.04(d), Eligibility Step 2A2. The additional elements in the present claims are processing circuitry and a remote computing system. The recitation of a machine-readable data storage system is also noted however this system is not only generic passive data storage, but it is also understood as nonstatutory transitory media within its broadest reasonable interpretation. The neural network topology is recited in the present claims as passive data storage and therefore is not currently being interpreted as an additional element. The additional elements have been considered individually, in combination, and altogether as a whole together with the functions they perform, e.g., the processing circuitry is broadly and generally recited as performing all steps in terms of the intended results of functionally nonspecific activities described as abstractions, and the remote computing system serves only as a field of use stand-in for the user’s input and output to the user. The additional elements do not integrate the judicial exception into a practical application because the claims lack any indication that any additional element practically applies any of the abstract limitations. The claim is almost entirely a recitation of abstract ideas. The substantive process is recited only by descriptions of intended results as abstractions without indicating any particular functional acts performed by any device or structural element to perform the steps or otherwise obtain the intended results. The additional elements do not improve the functioning of any computer or other technology or technical field, they do not apply the judicial exception with or by use of a particular machine, they do not transform or reduce a particular article to a different state or thing, and they fail to apply or use the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05.
If the disclosure describes any improvements to the functioning of a computer or to any other technology or technical field this improvement would need to be identifiable as the subject matter appearing in the claims. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies technical improvements realized by the claim over the prior art. The disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. MPEP 2106.05(a).
Claim limitations can integrate a judicial exception into a practical application by implementing the judicial exception with or using it in conjunction with a particular machine or manufacture that is integral to the claim. A general purpose computer that applies a judicial exception by use of generic computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, (Fed. Cir. 2014); MPEP 2106.05(b),(f). There are no particular machines or manufactures identified in the present claims. Any claimed elements that are not abstract are identified broadly and generally as applying the method, and the method itself is described only by way of the intended functional results of unidentified activities, without reference to any particular functional acts or specific functions performed by any particularly identified machines, and without reference to its use in conjunction with any particular item of manufacture.
The claims do not affect the transformation or reduction of a particular article to a different state or thing. Changing to a different state or thing means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which data, thoughts, impressions, or human based actions are "changed" are not considered a transformation. MPEP 2106.05(c).
The claims do not apply or use the judicial exception in any other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. As a result the claim as a whole appears to be a drafting effort designed to monopolize the exception. MPEP 2106.05(e),(h).
The additional elements have not been found to integrate the abstract idea into a practical application.
Step 2B:
Although the additional elements have not been found to integrate the abstract idea into a practical application the claims could still be eligible if they recite additional elements that amount to an inventive concept (“significantly more” than the judicial exception). MPEP 2106.05, Eligibility Step 2B.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the sparse additional elements of the claim are mere props supporting instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f). The claims invoke computers or other machinery merely as tools to perform an abstract process. Simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. MPEP 2106.05(f)(2); see also OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 2015 U.S. App. LEXIS 9721, 115 U.S.P.Q.2D (BNA) 1090 (Fed. Cir. 2015) (“relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.”). The elements are recited at a high level of generality, merely implement abstract ideas using generic computers, and fail to present a technical solution to a technical problem created by the use of the surrounding technology. Limitations that amount to 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. See Ret. Capital Access Mgmt. Co. v. U.S. Bancorp, 611 Fed. Appx. 1007, 2015 U.S. App. LEXIS 14351 (Fed. Cir. 2015) (“It may be very clever; it may be very useful in a commercial context, but they are still abstract ideas,” said Circuit Judge Alan Lourie.). MPEP 2106.05(h).
No technical problem is indicated and the claims are not directed to a technical solution to such a problem. The method claimed is a series of steps taken to practice an entrepreneurial activity. This conclusion is supported by applicant's disclosure, which only incidentally or tangentially explains the preexisting (prior art) computer equipment, and does not identify any technical problem that arises within said equipment and does not offer a technical solution to any such problem. It ultimately only describes the abstract idea while indicating the intention to “apply it.” The claimed subject matter merely takes advantage of an opportunity created by computers to use them as a tool for implementing a business plan, rather than solving a problem created by the computers. An equivalent business plan could be implemented without a computer (though it might be more cumbersome), and in any case merely confining the abstract idea to a particular field is insufficient to render it eligible subject matter. The claimed invention is patent ineligible because the innovative aspect (if there is one) is an entrepreneurial rather than a technological one. Bilski v. Kappos, 130 S. Ct. 3218, 3245; 177 L. Ed. 2d 792, 822; 2010 U.S. LEXIS 5521, 73; 95 U.S.P.Q.20 (BNA) 1001 (2010) (citing Merges, Property Rights for Business Concepts and Patent System Reform, 14 Berkeley Tech. L. J. 577, 585 (1999)); Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709 (Fed. Cir. Nov. 14, 2014) (“A rule holding that claims are impermissibly abstract if they are directed to an entrepreneurial objective, such as methods for increasing revenue, minimizing economic risk, or structuring commercial transactions, rather than a technological one, would comport with the guidance provided in both Alice and Bilski.” Mayer, J, concurring).
Finally, dependent claims 4, 7-11, and 21-27, do not add "significantly more" to establish eligibility because they merely recite additional abstract ideas that further describe the identification and manipulation of data used in implementing the abstract idea. A more detailed abstract idea is still abstract. PricePlay.com, Inc. v. AOL Adver., Inc., 627 Fed. Appx. 925, 2016 U.S. App. LEXIS 611, 2016 WL 80002 (Fed. Cir. Jan. 7, 2016) (in addressing a bundle of abstract ideas stacked together during oral argument, U.S. Circuit Judge Kimberly Moore said, "All of these ideas are abstract…. It’s like you want a patent because you combined two abstract ideas and say two is better than one.").
All of the above leads to the conclusion that additional claim elements do not provide meaningful limitations to transform the claimed subject matter into significantly more than an abstract idea. MPEP 2106.05; Eligibility Step 2B. As a result the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter because they recite an abstract idea without being directed to a practical application, and they do not amount to significantly more than the abstract idea. MPEP 2106.05, supra..
The preceding analysis applies to all statutory categories of invention. Accordingly, claims 2, 4, 7-11, and 21-27, are rejected as ineligible for patenting under 35 USC 101 based upon the same analysis.
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 2, 4, 7-11, and 21-27, are rejected under 35 U.S.C. 103 as being unpatentable over Flinn (Paper No. 20241211; Pub. No. US 2020/0286009 A1) in view of Igoe et al. (Paper No. 20241211; Patent No.: US 8,930,204 B1).
Flinn teaches a method for building a personalized neural network topology connected to a larger neural network comprising a plurality of third party neural network topologies. Flinn also teaches pertaining to
Claim 2. A method for building a personalized neural network topology connected to a larger neural network comprising a plurality of third party neural network topologies, the method comprising: ● receiving, on behalf of a business, a data set comprising a plurality of items (see at least Flinn fig. 12, ¶0091 “supplier may include information on products or services,” ¶0127 “objects 212 may include or reference items of content, such as text, graphics, audio, video, interactive content, or embody any other type or item of information”);
● by processing circuitry of a recommendation system, generating, based at least in part on the data set, a personalized neural network topology associated with the business (see at least Flinn fig. 34, ¶0242 “it should be understood the following described process and process network topologies can apply to any plurality of organizations”),
● generating the personalized neural network topology comprises augmenting the data set with additional information previously gathered by the recommendation system, wherein a plurality of other neural network topologies of the recommendation system comprise portions of the additional information (see at least Flinn figs. 32-33, ¶0148 “augmented by automated inferences and interpretations about the content within individual and sets of objects,” 0178 “may augment the preference inferencing algorithm 242 with considerations related to enhancing the revelation of user preferences,” ¶0223 “This approach may be augmented ... such that the adaptive recombinant system 800 has options for using more recent versions of an object 212 when networks are combined”); ● storing, to a machine-readable data storage system, the personalized neural network topology in association with the business (see at least Flinn ¶¶0128, 0348 “systems may utilize database management systems, including relational database management systems, to manage to manage associated data and information”); ● determining, by the processing circuitry, whether one or more relationships exist between at least a portion of the data set and one or more nodes of each of at least a portion of the plurality of other neural network topologies (see at least Flinn figs. 3, 6, 9, 12, 19, 34, 51, ¶¶0008, 0013, 0028, 0060); ● responsive to determining the one or more relationships exist, updating, by the processing circuitry, each respective neural network topology of the portion of the plurality of other neural network topologies, thereby enhancing interrelationships within each respective neural network topology (see at least Flinn figs. 4, 9, 12, 14-17, 24, 28-29, 39-43, 45-47); ● receiving, from a remote computing system of the business, a request identifying at least one item of the plurality of items (see at least Flinn abstract, fig. 43, ¶0069 “Participants in a process ... provide input to, ... a process or sub-process,” ¶¶0073, 0157 “recommendations 250 may also be generated in response to direct user requests or queries,” ¶0173 “recommendations 250 may be in response to explicit requests from the user. For example, a user may be able to explicitly designate one or more objects”. Please note: the prior art describes the input (request identifying item) as either automatic or user initiated. The limitation does not specify but is interpreted here as being automatic on both sides (receiving and sending the request).); ● by the processing circuitry, responsive to receiving the request, applying the personalized neural network topology to generate a set of one or more recommended items of a population of items including the plurality of items (see at least Flinn ¶0157 “recommendations 250 may also be generated in response to direct user requests or queries,” ¶0173 “recommendations 250 may be in response to explicit requests from the user”); and ● providing, to the remote computing system, information regarding the set of one or more recommended items (see at least Flinn abstract “Recommended objects are generated ... and are delivered to users”).
Flinn teaches all of the above as noted, and further discloses
● a plurality of nodes, each node of a portion of the plurality of the nodes representing i) a product of a plurality of products, wherein each product represents a product offered by the business (see at least Flinn fig. 43, ¶0091 “a potential supplier may include information on products or services offered in his or her profile”), ii) a purchaser of a plurality of purchasers, wherein each purchaser represents a reviewer of one or more products of the portion of the plurality of products or a customer who previously bought at least one product of the portion of the plurality of products (see at least Flinn fig. 45-46, ¶0069 “a process participant in a sales process may include … customers … that interact with the sales process, including the review and consideration of, and/or the purchasing of goods or services,” and iii) an item of a plurality of items, each item having a relationship with one or more of the plurality of products of a purchaser of the plurality of purchasers (see at least ¶0080 “relationships among … items of content … may be modified 905 based on inferred preferences or interests of one or more process participants. … derived from process usage behaviors,”), and nodal links representing affinities, including positive affinities (see at least Flinn ¶0142 “likes” represent a positive affinity).
Flinn does not however explicitly disclose the personalized neural network topology comprising a plurality of nodal links between the plurality of nodes, at least one of the plurality of nodal links representing a positive affinity among the plurality of items and at least one of the plurality of nodal links representing a negative affinity among the plurality of items.
Igoe also teaches the personalized neural network topology comprising
● a plurality of nodes, each node of a portion of the plurality of the nodes representing i) a product of a plurality of products, wherein each product represents a product offered by the business (see at least Igoe fig. 4, c61:1-18 “analysis performed by the advertising system 150 may include the calculation related to the adoption of items (i.e., products, goods, services, media content, etc.) by participants in the NDSN 100”), ii) a purchaser of a plurality of purchasers, wherein each purchaser represents a reviewer of one or more products of the portion of the plurality of products or a customer who previously bought at least one product of the portion of the plurality of products (see at least Igoe c25:3-12 “individual users, members, entities, communities, or other aggregations of entities within the NDSN 100 are associated with the nodes,” c61:1-18 “analysis performed by the advertising system 150 may include the calculation related to the adoption of items (i.e., products, goods, services, media content, etc.) by participants in the NDSN 100. … analysis may include the calculation of adoption statistics for music content items that have been purchased and consumed by the participants included in the projection of the NDSN 100 along the Music dimension”) and iii) an item of a plurality of items, each item having a relationship with one or more of the plurality of products of a purchaser of the plurality of purchasers (see at least Igoe c33:39-63 “information used to generate the personalized recommendations may be associated with content (e.g., content metadata or the adoption statistics associated with content items…. content items (music, video, etc.) by persons that have similar demographics or consumption habits to those of the user,” c61:1-18 “analysis performed by the advertising system 150 may include the calculation related to the adoption of items (i.e., products, goods, services, media content, etc.) by participants in the NDSN 100”), and
Igoe further discloses wherein the method also comprises the personalized neural network topology comprising
● a plurality of nodal links between the plurality of nodes, at least one of the plurality of nodal links representing a positive affinity among the plurality of items and at least one of the plurality of nodal links representing a negative affinity among the plurality of items (see at least Igoe figs. 17, 37, c45:37-47 “Feedback or rating of content can be performed by a feedback/rating mechanism 1106, either explicitly or implicitly, … recommending the content to other users (or providing a negative recommendation) … or other actions which do not require the user to rate the content but which themselves provide an indication as to how much the user likes/dislikes the content,” c46:15-34 “pair of buttons 173 for assigning a "star" rating to a video program and a second pair of buttons 174 for indicating a general recommendation or a dislike of a video program,” 50-62 “indicating a positive implicit rating/feedback. Alternatively, the user may listen to only a portion of the recording and immediately delete it, providing a negative implicit rating/feedback”).
Therefore it would have been obvious to one of ordinary skill in the art at the time of invention (for pre-AIA applications) or filing (for applications filed under the AIA ) to modify the method of Flinn to include the personalized neural network topology comprising a plurality of nodal links between the plurality of nodes, at least one of the plurality of nodal links representing a positive affinity among the plurality of items and at least one of the plurality of nodal links representing a negative affinity among the plurality of items, as taught by Igoe since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable and would result in an improvement. This is because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features even from a variety of technical fields into methods and systems implemented using similar technological structures (i.e., generic computer and/or network hardware such as processors, servers, etc.). In this case the areas of technical endeavor are nonetheless similar and overlapping.
Applicant has not disclosed that the added feature solves any stated problem or is for any particular purpose beyond the performance of the functions they performed separately and since each element and its function are shown in the prior art the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would therefore have been an obvious matter of design choice to include the feature from Igoe in the method of Flinn. Furthermore the combination solved no long felt need. Incorporating cumulative known features is additionally obvious to one of ordinary skill in the art because doing so increases commercial use of a method by attracting users that previously might have chosen between one of the previously known methods.
Flinn in view of Igoe further teaches
Claim 4. The method of claim 2, wherein updating the personalized neural network topology comprises scaling a strength of connection of at least one nodal link of the plurality of nodal links of the personalized neural network topology (see at least Flinn figs. 4, 6, 9, 12, 14-17, 24, 28-29, 39-43, 45-47, ¶0080 “adaptive recommendations 910 may be applied to automatically or semi-automatically self-modify 905 the structure, elements, objects, content, information, or software of a subset,” ¶0113 “relationship value may be scaled as in FIG. 6,” ¶¶0219, 0252).
Claim 7. The method of claim 2, wherein:
● the recommendation system is comprised of at least one filter parameter (Flinn discloses a filter but does not explicitly disclose at least one filter parameter, however see at least Igoe fig. 24, c9:5-10 “filtered data, i.e., data that generated by "filtering out" specific data elements without alteration from one or more input data feeds,” c17:40-51 “selecting the filtered healthcare information for the output data feed. For example, the user might select that only height, weight and cholesterol level are to be contained in the output data feed”); ● the request is limited by the at least one filter parameter (see at least the immediately preceding rationale.); and ● generating the set of one or more recommended items comprises applying, to a plurality of recommended items derived from the personalized neural network topology, the at least one filter parameter to discard one or more items of the population of items from inclusion in the set of one or more recommended items (see at least the preceding rationale. In addition see at least Flinn ¶¶0137, 0177 “to delete existing objects,” ¶0181 “recommendations ... may identify objects 212 that are candidates for deletion. The adaptive recommendations function 240 may also automatically delete the object,” ¶0187 “Advertising or promotional elements may be added, deleted, or adjusted within information,” in view of Igoe figs. 12-13, 17, c10:5-10 “purchase input data feeds 823a, 823b, 823c and 824 from K-Mart, Target, Circuit City and iTunes are also combined into a composite purchase feed 836 that is filtered to generate an output data feed 837 that contains music purchases”).Claim 8. The method of claim 2, wherein: ● each node of a subset of the plurality of nodes represents a respective attribute of a set of attributes (see at least Flinn ¶0199 “a relationship translates to either a "O" or a "1"-"0," for example if there is not a relationship, and "1" if there is a relationship. For fuzzy networks, the relationships between any two nodes, when normalized, may have values along a continuum between 0 and 1 inclusive, where O implies no relationship between the nodes, and 1 implies the maximum possible relationship between the nodes,” ¶0200 “A-directional relationships between nodes (no arrows), directed relationships between nodes (whether single- or double-arrow), and multiple types of relationships between nodes, are supported by the adaptive system,” ¶0217); and ● a second portion of the plurality of nodal links defines content-based interrelationships representing commonalities between two or more items of the plurality of items based on at least one attribute of the set of attributes that is shared in common among the two or more items (see at least Flinn figs. 9, 12, ¶0080 “elements, objects, or items of content ... or the relationships among elements, objects, or items of content ... may be modified 905 based on inferred preferences or interests of one or more process participants”).Claim 9. The method of claim 8, wherein generating the set of one or more recommended items comprises: ● determining at least one item category most similar to the request (see at least Flinn ¶¶0088-0089 “interactions with online computer applications and content such as documents, Web pages, images, videos, audio, multi-media, interactive content, interactive computer applications, e-commerce applications, or any other type of information item or system "object.”... capture and categorization of sequences of information or system object,” ¶0127 “objects 212 may include or reference items of content, such as text, graphics, audio, video, interactive content, or embody any other type or item of information”); and ● identifying at least a portion of the set of one or more recommended items based on one or more category attributes of the set of attributes (see at least Flinn abstract “recommended objects may be further generated based upon inferences of preferences,” fig. 39, ¶0079 “recommendations 910 delivered by the adaptive computer-based application 925 are informational or computing elements or subsets of the adaptive computer-based application 925, and may take the form of text, graphics, Web sites, audio, video, interactive content, other computer applications, or embody any other type or item of information”).Claim 10. The method of claim 2, wherein: ● each node of a subset of the plurality of nodes represents a respective individual of a plurality of individuals (see at least Flinn ¶0196 “each individual item of information may be related to any other individual item of information”); and ● each nodal link of a second portion of the plurality of nodal links defines a respective collaborative interrelationship representing a respective affinity of a respective individual of the plurality of individuals for one or more respective items of the plurality of items (see at least Flinn abstract “objects may be further generated based upon inferences of preferences from usage behaviors,” figs. 9, 12).Claim 11. The method of claim 10, wherein at least a portion of the plurality of individuals are customers of the business (see at least Flinn figs. 45-46, ¶0068 “process participant 200 for a sales process might be a prospective customer”).
Claim 21. (New) The method of claim 7, wherein the at least one filter parameter provides error correction to prevent the risk of an erroneous recommendation (see at least Igoe c42:22-34 “feedback (not shown) indicating the correctness of the recommendations; … is considered to be the target (i.e. desired) output (i.e. recommendation 125) of the neural network. The error (i.e. difference) between the actual output of the neural network and the target output is calculated, and the weights 122 and the bias (w0) 126 are adjusted according to the error value”).
Claim 22. (New) The method of claim 7, wherein the at least one filter parameter is the distance of at least one purchaser of the plurality of purchasers from the business (see at least Flinn figs. 44-46, ¶¶0053-0056 “location aware collectively adaptive systems,” ¶0098 “Proximity of a process participant to a second process participant” in view of Igoe figs. 4, 18, c53:60-67 “GPS tracking data stored in a PIA may indicate that a user traveled a road upon which a billboard was placed. Similarly, data indicating presence at locations within a shopping mall could be used to infer exposure to advertisements at
those locations,” c60:40-50 “participants may have a group characteristic identifying them as located in a particular geographic area,” c65:3-10 “based on the user's itinerary, which can include the user's current location, travel plans”).
Claim 23. (New) The method of claim 7, wherein each item of the plurality of items has a respective timestamp, and the at least one filter parameter is a filter parameter timestamp (see at least Flinn ¶0135 “time period in which these interactions occurred (e.g., timestamps), as captured usage information,” Igoe figs. 4, 25-28).
Claim 24. (New) The method of claim 7, wherein the at least one filter parameter is resonance quantifier threshold, representing the resonance of at least one recommended item of the set of one or more recommended items with past recommendations validated by at least one purchaser of the plurality of purchasers (see at least Flinn ¶0172, ¶0316, in view of Igoe c20:1-10).
Claim 25. (New) The method of claim 7, wherein the at least one filter parameter is a congruency factor threshold, representing a required minimum nodal interconnectivity (see at least Flinn ¶0325 “interconnected information may be syndicated to potential customers or individuals or institutions for whom influence is sought” in view of Igoe c4:5-20).
Claim 26. (New) The method of claim 2, wherein the set of one or more recommended items of a population of items including the plurality of items is constrained by a recommendation limit (see at least Flinn ¶0109 “selected limitation may be specified to apply only to particular user communities or individual process participants,” ¶0282 “there may be limits on the number of unique media instances generated”).
Claim 27. (New) The method of claim 2, wherein the each item in the set of one or more recommended items of a population of items including the plurality of items includes a respective diversity factor, representing the similarity between each item in the set of one or more recommended items of a population of items including the plurality of items and the other items in the set of one or more recommended items of a population of items including the plurality of items (see at least Flinn ¶0109 “variety of limitation variables may be selected by the process participant,” ¶0270 “variable is the degree to which the greatest diversity of human attention to be applied, and applied in the right places”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
● XIANG ET AL. "Modeling Relationship Strength in Online Social Networks", 2010, pages 981-990; 2010: teaches various link types and strengths co-existing in a network and various tactics for estimating link strength/weight by inference.
● Lee et al., Patent No.: US 8,572,129 B1: teaches registering "like" in registering relationship between nodes.
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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/ADAM L LEVINE/Primary Examiner, Art Unit 3689 May 24, 2026
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