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 .
Status of the Claims
Claims 1-5, 7-15, 17-24 and 26-31 are pending in the instant patent application. Claims 6, 16 and 25 are cancelled.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and per guidelines for 101 analysis.
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §103 rejections. The rejections remain pending and are updated and addressed below in light of newly cited art.
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered, but are not persuasive.
Regarding Applicant’s claims that they are not directed towards abstract ideas, Examiner respectfully disagrees.
Applicant asserts that since the claim language involves the use of AI, that it doesn’t recite Mental Processes due to the USTPO Memo issued in August, 2025, Examiner respectfully disagrees. As noted by the Deputy Commissioner for Patents, “limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within this grouping”. Examiner has made note of two limitations of Claim 1 that encompass AI, the “calculating” step and “updating” step. The “calculating” step is merely reciting the use of a machine learning model as a tool. The “calculating” step as currently written in Claim 1, still recites Mental Processes for it is merely determining similarity scores between profiles. Furthermore, the “updating” step of Claims 1, 11 and 20 were addressed in Prong 2 of the 101 analysis in the previous Office Action and was noted as not integrating the judicial exception into a practical application. Examiner will further remind Applicant that the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Regarding Applicant’s arguments that the pending claims provide a practical application and are similar to DDR Holdings, Examiner respectfully disagrees. Regarding DDR, in DDR Holdings, the court found the claims to be patent eligible because the claims recites the solution of a hybrid webpage that co-displays the look and feel of the first website with the desired content from the second website. The court found such solution to be rooted in computer technology because there was no other way to accomplish such solution. The computer was an essential part of performing the solution. Although the current claims recite functions performed by a computer system and machine learning model, such functions are not found to be significantly more when recited in their generic manner.
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.
Regarding Claims 1-5, 7-10 and 29, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 1- 5, 7-10 and 29 are directed to the abstract idea of determining similarities between two or more trademark portfolios.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites determining a first trademark portfolio comprising trademarks owned by the first entity; determining a first trademark portfolio profile for the first entity based on the first trademark portfolio; calculating a first portfolio similarity score indicating the similarity between the first trademark portfolio profile and a second trademark portfolio profile corresponding to a second entity; including, based on the first portfolio similarity score meeting a predetermined criterion, the second entity in a first set of output entities; providing the first set of output entities to the user; receiving, from the user, feedback on the first set of output entities; receiving monitoring criteria from the user, the monitoring criteria comprising a similarity score threshold; monitoring trends in a second portfolio similarity score indicating a similarity between the first trademark portfolio profile and a third trademark portfolio profile corresponding toa third entity; and providing an alert to the user when the monitored trends in the second portfolio similarity score satisfies the similarity score threshold, the alert identifying the third entity.
These claim limitations fall within the Mental Processes grouping of abstract ideas. Examiner finds that both “determining” limitations, the “calculating”, “monitoring” and “including” limitations are concepts that can be practically performed in the human mind and/or with pen/paper (including an observation, evaluation, judgment, Opinion). Furthermore, in light of the amendments, the claim language falls within the Certain Methods of Organizing Human Activity grouping of abstract ideas due to the managing personal behavior or relationships or interactions between people (including following rules or instructions).
Furthermore, the recitation of a machine learning model does not take the claim out of the certain methods of organizing human activity and mental processes.
Accordingly, the claim recites an abstract idea and dependent claims 2-5, 7- 10 and 29 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a machine learning model and updating the machine learning model based on the feedback. The machine learning model and updating the machine learning model based on the feedback are merely generic computing devices and do not integrate the judicial exception into a practical application. In addition, the “receiving” and “providing” limitations add insignificant extra-solution activity to the judicial exception.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 1 includes various elements that are not directed to the abstract idea under 2A. These elements include a machine learning model, updating the machine learning model based on the feedback and the generic computing elements described in the Applicant's specification in at least Para 0097. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 1 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 11-15, 17-19 and 30, they are directed to a system, however the claims are directed to a judicial exception without significantly more. Claims 11-15, 17-19 and 30 are directed to the abstract idea of determining similarities between two or more trademark portfolios.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 11, claim 11 recites determining a first trademark portfolio comprising trademarks owned by the first entity; determining a first trademark portfolio profile for the first entity based on the first trademark portfolio; calculating a first portfolio similarity score indicating the similarity between the first trademark portfolio profile and a second trademark portfolio profile corresponding to a second entity; including, based on the first portfolio similarity score meeting a predetermined criterion, the second entity in a first set of output entities; providing the first set of output entities to the user; receiving, from the user, feedback on the first set of output entities; receiving monitoring criteria from the user, the monitoring criteria comprising a similarity score threshold; monitoring trends in a second portfolio similarity score indicating a similarity between the first trademark portfolio profile and a third trademark portfolio profile corresponding to a third entity; and providing an alert to the user when the monitored trends in the second portfolio similarity score satisfies the similarity score threshold, the alert identifying the third entity.
These claim limitations fall within the Mental Processes grouping of abstract ideas. Examiner finds that both “determining” limitations, the “calculating”, “monitoring” and “including” limitations are concepts that can be practically performed in the human mind and/or with pen/paper (including an observation, evaluation, judgment, Opinion). Furthermore, in light of the amendments, the claim language falls within the Certain Methods of Organizing Human Activity grouping of abstract ideas due to the managing personal behavior or relationships or interactions between people (including following rules or instructions).
Furthermore, the recitation of a machine learning model does not take the claim out of the certain methods of organizing human activity and mental processes.
Accordingly, the claim recites an abstract idea and dependent claims 12-19, and 30 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a processing circuit, memory, a machine learning model and updating the machine learning model based on the feedback. The processing circuit, memory, a machine learning model and updating the machine learning model based on the feedback are merely generic computing devices and do not integrate the judicial exception into a practical application. In addition, the “receiving” and “providing” limitations add insignificant extra-solution activity to the judicial exception.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 11 includes various elements that are not directed to the abstract idea under 2A. These elements include a processing circuit, memory, a machine learning model, updating the machine learning model based on the feedback and the generic computing elements described in the Applicant's specification in at least Para 0097. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 11 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 20-24, 26-28 and 31, they are directed to a computer- readable storage medium, however the claims are directed to a judicial exception without significantly more. Claims 20-24, 26-28 and 31 are directed to the abstract idea of determining similarities between two or more trademark portfolios.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 20, claim 20 recites determining a first trademark portfolio comprising trademarks owned by the first entity; determining a first trademark portfolio profile for the first entity based on the first trademark portfolio; calculating a first portfolio similarity score indicating the similarity between the first trademark portfolio profile and a second trademark portfolio profile corresponding to a second entity; including, based on the first portfolio similarity score meeting a predetermined criterion, the second entity in a first set of output entities; providing the first set of output entities to the user; receiving, from the user, feedback on the first set of output entities; receiving monitoring criteria from the user, the monitoring criteria comprising a similarity score threshold; monitoring trends in a second portfolio similarity score indicating a similarity between the first trademark portfolio profile and a third trademark portfolio profile corresponding toa third entity; and providing an alert to the user when the monitored trends in the second portfolio similarity score satisfies the similarity score threshold, the alert identifying the third entity.
These claim limitations fall within the Mental Processes grouping of abstract ideas. Examiner finds that both “determining” limitations, the “calculating”, “monitoring” and “including” limitations are concepts that can be practically performed in the human mind and/or with pen/paper (including an observation, evaluation, judgment, Opinion). Furthermore, in light of the amendments, the claim language falls within the Certain Methods of Organizing Human Activity grouping of abstract ideas due to the managing personal behavior or relationships or interactions between people (including following rules or instructions).
Furthermore, the recitation of a machine learning model does not take the claim out of the certain methods of organizing human activity and mental processes.
Accordingly, the claim recites an abstract idea and dependent claims 21-24, 26-28 and 31 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of at least one processor, a machine learning model and updating the machine learning model based on the feedback. The at least one processor, machine learning model and updating the machine learning model based on the feedback are merely generic computing devices and do not integrate the judicial exception into a practical application. In addition, the “receiving” and “providing” limitations add insignificant extra-solution activity to the judicial exception.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 1 includes various elements that are not directed to the abstract idea under 2A. These elements include at least one processor, a machine learning model, updating the machine learning model based on the feedback and the generic computing elements described in the Applicant's specification in at least Para 0097. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 20 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Response to 35 U.S.C. §103 Arguments
Applicant’s arguments regarding 35 U.S.C. §103 rejection of the claims have been fully considered, but are not persuasive due to newly cited art in light of Waerniers being excepted as prior art.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-5, 7-15, 17-24 and 26-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nefedov et al. (US 2016/0350294 A1) in view of Malik et al. (US 2019/0278777 A1) in view of Keren et al. (US 2021/0248624 A1) in view of Tan (US 2013/0318177 A1) further in view of Keyngnaert et al. (US 2015/0324469 A1).
Regarding Claim 1, Nefedov teaches the limitations of Claim 1 which state
receiving a user request that identifies a first entity (Nefedov: Para 0062 via a user may input a company name and a set of text terms with an indicia, such as title, of which the user is aware relates to the company of interest, e.g., the query may be “Microsoft & ttl(software).” Using this exemplary query, the user is interested in finding a set of peers having patent portfolios similar to Microsoft in the area of “software.” In this example Microsoft is the target company and its portfolio of patents having “software” in the title is the target patent portfolio);
determining a first trademark portfolio comprising trademarks owned by the first entity (Nefedov: Para 0062 via in this example Microsoft is the target company and its portfolio of patents having “software” in the title is the target patent portfolio. The search is broadly structured and involves searching for Microsoft patents having “software” in the title. Initially the PDSE may identify a set of patents owned by Microsoft that include the term “software” in the title of the invention. Based on this information, the PDSE may, in one exemplary manner, next identify a set of IPC codes extracted from or associated with the resulting Microsoft “software” patents). In addition, Para 0011, 0012 and 0053 state that the invention may use the structural properties of an ontology (e.g., hierarchical classifications of patents, trademarks, legal documents, scientific papers, citations etc) to identify object peers... that it can be applied to other domains that include hierarchical classifications such as trademarks, legal documents, scientific papers, lawsuits etc... and allows comparing and merging information from different domains into an overall similarity measure; dimensions might include patents, trademarks, products, lawsuits and others);
determining a first trademark portfolio profile for the first entity based on the first trademark portfolio (Nefedov: Para 0062-0063 via in this example Microsoft is the target company and its portfolio of patents having “software” in the title is the target patent portfolio. The search is broadly structured and involves searching for Microsoft patents having “software” in the title. Initially the PDSE may identify a set of patents owned by Microsoft that include the term “software” in the title of the invention. Based on this information, the PDSE may, in one exemplary manner, next identify a set of IPC codes extracted from or associated with the resulting Microsoft “software” patents);
However, Nefedov does not explicitly disclose the limitation of Claim 1 which states calculating, using a machine learning model, a first portfolio similarity score indicating the similarity between the first trademark portfolio profile and a second trademark portfolio profile corresponding to a second entity.
Malik though, with the teachings of Nefedov, teaches of
calculating, using a machine learning model, a first portfolio similarity score indicating the similarity between the first trademark portfolio profile and a second trademark portfolio profile corresponding to a second entity (Malik: Para 0036, 0103, 0144, 0270 via The system combines Machine Learning and/or deep learning models to identify sentences mentioning or referencing or representing a supply chain connection between two companies (evidence). The present invention also applies an aggregation layer to take into account the evidence found and assign a confidence score to the relationship between companies…The non-volatile memory 20 is configured to include a fingerprint extraction module 26 for computing and comparing entity fingerprints to one another. As used herein, the term ‘fingerprint’ refers to an abstract representation of an entity based on a number of its attributes and/or characteristics. Once a fingerprint is computed for an entity, the entity fingerprint may be compared to other entity fingerprints to understand similarities and differences that may exist. In one embodiment, computed entity fingerprints are used to generate feature vectors to be used in classification and clustering tasks… at step 88, the comparison module 32 computes a similarity score for the first and second entity fingerprints by applying similarity functions to corresponding normalized attributes of each fingerprint and aggregates the results. Example similarity functions that may be applied to corresponding normalized attributes include, but are not limited to, cosine similarity, Euclidean distance, Manhattan distance, and the like…the SCAR system computes a similarity score between each of the candidate nodes and the given entity using an Support Vector Machine (SVM) classifier that is trained using a surrogate learning technique. Surrogate learning allows the automatic generation of training data from the datasets being matched).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nefedov with the teachings of Malik in order to have calculating, using a machine learning model, a first portfolio similarity score indicating the similarity between the first trademark portfolio profile and a second trademark portfolio profile corresponding to a second entity. The motivations behind this being to incorporate the teachings of natural language processing, information extraction, information retrieval and text mining and more particularly to entity associations and to systems and techniques for identifying and measuring entity relationships and association significance, and more particularly to systems and techniques for computing and analyzing entity fingerprints. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
The combination of Nefedov/Malik further teaches the limitations of Claim 1 which state
including, based on the first portfolio similarity score meeting a predetermined criterion, the second entity in a first set of output entities (Nefedov: Para 0014, 0067 via One embodiment of the invention provides an asymmetric measure approach having the following advantages: a user can use IPC hierarchical structure in patent similarity assessment, compare directly patent portfolios without intermediate aggregation, apply non-symmetric peer measures (Super- and sub- activities), dynamically set threshold on different dimensions with visual feedback, replace/augment peer list by peer graph visualization, revealing structure, use patent structure view to select sub/super activity as query, explore evolution over time, and explore the companies in a specific domain. PDS Network allows comparing and merging information from different domains into an overall similarity measure. Dimensions might include patents, trademarks, products, lawsuits and others...based on the normalized similarity, PDS 104 creates a network with nodes, e.g., each node representing an entity such as a company, and the network being a network of peer companies in a given industry. This may be done by generating a set of feature scores from which a similarity determination may be made. The system may also normalize the set of scores using different techniques. The method may include delimiting the set of documents using a threshold scoring requirement);
providing the first set of output entities to the user (Nefedov: Para 0064 via the PDS may present a default graphical interface for display to the user via the remote device. The graphical interface may, for example, include the entity of interest included in the search, e.g., Microsoft, with textual or other accompanying notation, along with the set of identified peers. The peers may be indicated by name or by cluster of similar patents owned by each respective peer entity and may be shown with connecting lines or the like to graphically depict similarity. The graphical representation of peers may include indicia indicative of degree of similarity. The visualization may be presented in the form of a network of connected nodes representing similarity between nodes, which may represent entities or IPCs).
The combination of Nefedov/Malik further does not explicitly disclose the limitations of Claim 1 which state receiving, from the user, feedback on the first set of output entities; updating the machine learning model based on the feedback.
Keren though, with the teachings of Nefedov/Malik, teaches of
receiving, from the user, feedback on the first set of output entities and updating the machine learning model based on the feedback (Keren: Para 0180, 0201 via The Evaluation Algorithm (and/or other algorithms or modules of the system) may be a learning algorithm. The user can change the level of importance of the web-site, based on his/her own perception. As a result the system will incorporate the user preference into the algorithm for future analysis of results. The evaluation module may allow the user to evaluate the relative values of his domain name and websites. As a result the user can decide to drop (e.g., delete or not renew) domain names that are in a low value, and therefore have small contribution to the online activity of the company. Other algorithms and/or modules of the system may be implemented as learning algorithms, which may gradually learn from feedback of the user, which risks are more important to the user, which opportunities are more attractive to the user, which parameters or metrics the user is more interested in, or other decisions or preferences that may be learned by using machine-learning algorithms…Some actions of the user indicate its dissatisfaction from the current scoring function; user interaction such as cease-and-desist or risk level adjustment, are human indications that may be used as a training set for a machine learning algorithm. The algorithm may take into account the functional form of the scoring function).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nefedov/Malik with the teachings of Keren in order to have receiving, from the user, feedback on the first set of output entities and updating the machine learning model based on the feedback. The motivations behind this being to incorporate the teachings of protecting brand names and domain names. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
However, Nefedov/Malik/Keren does not explicitly disclose the limitations of Claim 1 which state receiving monitoring criteria from the user and monitoring trends in a second portfolio similarity score indicating a similarity between the first trademark portfolio profile and a third trademark portfolio profile corresponding to a third entity.
Tan though, with the teachings of Nefedov/Malik/Keren, teaches of
receiving monitoring criteria from the user (Tan: Para 0023, 0047 via a portfolio monitoring system can provide change warnings about various domain names along with historical information about the domains. For example, a user can set an alert threshold and add domain names to be monitored. The system may suggest even more domain names (e.g., based on WHOIS of an initial domain name). A WHOIS search can return a variety of useful data which can be monitored. For example, the system can monitor for changes in Creation Date, Registration Date, Expiry Date, Last Update Organization Name, Organization Address, Admin Name, Admin E-mail, Admin Phone, and other fields of information... GUI generation module 215 generates a graphical user interface allowing submission of trademarks. The trademarks may be submitted to the portfolio monitoring system for evaluation. The portfolio monitoring system can return one or more domain names (which may or may not be owned by the user) that may be of interest. The user is then able to select which domains to monitor. In some cases, the user can also associate notification criteria with each domain name. Examples of notification criteria include, maximum price the user is willing to pay for the domain name, an expiration notification buffer time (e.g., a time period, such as one month, before the expiration of the domain name), and others)
and monitoring trends in a second portfolio similarity score indicating a similarity between the first trademark portfolio profile and a third trademark portfolio profile corresponding to a third entity (Tan: Para 0023, 0042, 0047 via a portfolio monitoring system can provide change warnings about various domain names along with historical information about the domains. For example, a user can set an alert threshold and add domain names to be monitored. The system may suggest even more domain names (e.g., based on WHOIS of an initial domain name). A WHOIS search can return a variety of useful data which can be monitored. For example, the system can monitor for changes in Creation Date, Registration Date, Expiry Date, Last Update Organization Name, Organization Address, Admin Name, Admin E-mail, Admin Phone, and other fields of information... GUI generation module 215 generates a graphical user interface allowing submission of trademarks. The trademarks may be submitted to the portfolio monitoring system for evaluation. The portfolio monitoring system can return one or more domain names (which may or may not be owned by the user) that may be of interest. The user is then able to select which domains to monitor. In some cases, the user can also associate notification criteria with each domain name. Examples of notification criteria include, maximum price the user is willing to pay for the domain name, an expiration notification buffer time (e.g., a time period, such as one month, before the expiration of the domain name), and others...Any detection of changes in the one or more of these fields can result in the system generating alerts or warnings, which can be delivered via e-mail, phone, etc. In addition, the system can create a change history of all changes for each domain name in the portfolio. In addition, some embodiments of the present invention collect screenshots of the websites on a regular basis, predetermined schedule, or upon detection of an event. Comparisons can be run, in one or more embodiments, between the screenshots. The changes detected can trigger alerts or warnings. In addition, these screenshots can be compared to screenshots from other domains to determine a similarity score).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nefedov/Malik/Keren with the teachings of Tan in order to have receiving monitoring criteria from the user and monitoring trends in a second portfolio similarity score indicating a similarity between the first trademark portfolio profile and a third trademark portfolio profile corresponding toa third entity. The motivations behind this being to incorporate the teachings of monitoring a portfolio of domain names as taught by Tan. The teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention. Furthermore, simple substitution of one known element for another to obtain predictable results.
Furthermore, Nefedov/Malik/Keren/Tan does not explicitly disclose the limitations of Claim 1 which state comprising a similarity score threshold and providing an alert to the user when the monitored trends in the second portfolio similarity score satisfies the similarity score threshold, the alert identifying the third entity.
Keyngnaert though, with the teachings of Nefedov/Malik/Keren/Tan, teaches of
comprising a similarity score threshold and providing an alert to the user when the monitored trends in the second portfolio similarity score satisfies the similarity score threshold, the alert identifying the third entity (Keyngnaert: Para 0086 via upon completion of the comparisons, the candidate filtering module 254 can determine which of the trademarks it considers to be confusingly similar to the order. For example, the candidate filtering module 254 can determine whether the trademarks satisfy a filtering criteria. In exemplary embodiments, the filtering criteria can include a specified similarity score threshold, and the filtering module 254 can be executed to compare the similarity scores, generated by the scoring module 252 for each of the trademarks, to the specified similarity score threshold. Trademarks having a similarity score that exceeds the specified threshold can satisfy the filtering criteria and can be deemed to be confusingly similar by the candidate filtering module 254. The trademarks that are deemed to be confusingly similar to the order can be output as a filtered set of results 270 from the candidate presentation engine 250, and can be reported to a user by transmitting the filtered set of results to a user device via a communication network. Trademarks having scores that do not exceed the similarity score threshold are not transmitted to the user devices. Upon receiving the filtered results set, the user device can render the filtered results set in a GUI displayed on a display unit associated with the user device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nefedov/Malik/Keren/Tan with the teachings of Keyngnaert in order to have comprising a similarity score threshold and providing an alert to the user when the monitored trends in the second portfolio similarity score satisfies the similarity score threshold, the alert identifying the third entity. The motivations behind this being to incorporate the teachings of searching similar trademarks. The teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention. Furthermore, simple substitution of one known element for another to obtain predictable results.
Regarding Claim 2, Nefedov/Malik/Keren/Tan/Keyngnaert teaches the limitations of Claim 2 which state
wherein determining the first trademark portfolio profile comprises generating a first feature vector by extracting features related to a first trademark from the first trademark portfolio (Nefedov: Para 0011, 0016 via in one manner of implementation the invention uses taxonomy as a tree and defines a similarity measure based on a distance on a graph. For example, a patent portfolio may be presented as a vector containing weighted patent classifications codes (IPC). Each IPC in turn is formed by groups of characters corresponding to different hierarchy levels. Then we link hierarchy levels to a function of graph distances and recursively calculate similarity between feature IPC vectors... The system of this first embodiment may be further characterized with the following features and operations. The extracted data may include a hierarchical set of features and the portfolio comparison module may be adapted to determine the set of similarity scores based on a set of weights assigned, respectively, to the hierarchical set of features and to generate a set of feature vectors).
Regarding Claim 3, Nefedov/Malik/Keren/Tan/Keyngnaert teaches the limitations of Claim 3 which state
wherein calculating a first portfolio similarity score comprises: providing the first feature vector as input to a portfolio similarity score calculator, and receiving as output from the portfolio similarity score calculator an identifier of the second entity and the first portfolio similarity score, the first portfolio similarity score indicating a similarity between the first feature vector and a second feature vector corresponding to the second entity (Nefedov: Para 0134-0135, 0016 via FIG. 21 illustrates one example of the aggregate approach using cosine similarity for comparison purposes. As shown in FIG. 21, two companies, X and Y, each have the same aggregated profile, namely three (3) patents containing IPC code IPC_1; five (5) patents containing IPC code IPC_2; seven (7) patents containing IPC code IPC_3; and three (3) patents containing IPC code IPC_4. The IPC fingerprint (=the aggregated view) can be represented as a vector [3,5,7,3]... FIG. 22 represents mathematical analysis performed with respect to the portfolio of company X. Assuming, for example, that the patent portfolio generating this profile contained 11 patents, there are 701 different combination of IPC assignments that result all in exactly the same fingerprint, namely [3,5,7,3]. The only situation where the IPC assignments is uniquely defined, given the [3,5,7,3] fingerprint, is when the portfolio of company X contains 18 patents. In this case every patent contains only one IPC code. In this example, analysis shows that fingerprint equality does not mean patent portfolio equality. For example if both companies X and Y share the same fingerprint, namely [3,5,7,3], and have both a portfolio size of 11 patents, then the probability for these portfolios to match perfectly, despite that they have the same fingerprint, is as low as 0.1%=1/1000. While company similarity is not dependent upon absolute equality of company patent portfolios, this example illustrates the limitations of the aggregate approach based on cosine similarity as a measure of similarity. Although the cosine of the angle between the two identical vectors [3,5,7,3] is O degrees and, therefore, the similarity measure is 1, the two portfolios are not identical and therefore have an unmeasured, in this instance, degree of similarity/dissimilarity. The above clearly illustrates that aggregated “fingerprint” portfolio views are unreliable measures to draw detailed conclusions about similarity of underlying patent sets... The extracted data may include a hierarchical set of features and the portfolio comparison module may be adapted to determine the set of similarity scores based on a set of weights assigned, respectively, to the hierarchical set of features and to generate a set of feature vectors).
Regarding Claim 4, Nefedov/Malik/Keren/Tan/Keyngnaert teaches the limitations of Claim 4 which state
wherein the first feature vector comprises features related to at least one of: at least one of a trademark class or a trademark sub-class of each trademark in the first trademark portfolio, a tally of trademarks owned by the first entity in the at least one of the trademark class and or trademark sub-class, or a percentage of trademarks owned by the first entity in each of the at least one of the trademark classes or the trademark sub-classes out of a total number of trademarks in first trademark portfolio (Nefedov: Para 0011 via The invention may use the structural properties of an ontology (e.g., hierarchical classifications of patents, trademarks, legal documents, scientific papers, citations etc) to identify object peers (e.g., company peers). In particular, in one manner of implementation the invention uses taxonomy as a tree and defines a similarity measure based on a distance on a graph. For example, a patent portfolio may be presented as a vector containing weighted patent classifications codes (IPC). Each IPC in turn is formed by groups of characters corresponding to different hierarchy levels. Then we link hierarchy levels to a function of graph distances and recursively calculate similarity between feature IPC vectors. It may be shown that the suggested similarity measure is more accurate and more scalable than other (non-graph) measures such as cosine- similarity. The invention allows comparison of portfolios with items classified similarly (measuring on a similarity scale) while current methods only allow exact comparison (discrete 0 or 1 measure)).
Regarding Claim 5, Nefedov/Malik/Keren/Tan/Keyngnaert teaches the limitations of Claim 5 which state
wherein the second feature vector comprises features related to at least one of: at least one of a trademark class or a trademark sub-class of each trademark in the second trademark portfolio, a tally of trademarks owned by the second entity in the at least one of the trademark class and or trademark sub-class, or a percentage of trademarks owned by the second entity in each of the at least one of the trademark classes or the trademark sub-classes out of a total number of trademarks in first trademark portfolio (Nefedov: Para 0011 via The invention may use the structural properties of an ontology (e.g., hierarchical classifications of patents, trademarks, legal documents, scientific papers, citations etc) to identify object peers (e.g., company peers). In particular, in one manner of implementation the invention uses taxonomy as a tree and defines a similarity measure based on a distance on a graph. For example, a patent portfolio may be presented as a vector containing weighted patent classifications codes (IPC). Each IPC in turn is formed by groups of characters corresponding to different hierarchy levels. Then we link hierarchy levels toa function of graph distances and recursively calculate similarity between feature IPC vectors. It may be shown that the suggested similarity measure is more accurate and more scalable than other (non-graph) measures such as cosine- similarity. The invention allows comparison of portfolios with items classified similarly (measuring on a similarity scale) while current methods only allow exact comparison (discrete 0 or 1 measure)).
Regarding Claim 7, Nefedov/Malik/Keren/Tan/Keyngnaert teaches the limitations of Claim 7 which state
wherein monitoring the trends in the second portfolio similarity score comprises: updating a database of the trademarks by adding newly registered trademarks and to generate an updated database of trademarks; recalculating the second portfolio similarity score between the first trademark portfolio profile and the third trademark portfolio profile; and tracking changes in the second portfolio similarity score over time (Tan: Para 0023, 0047 via a portfolio monitoring system can provide change warnings about various domain names along with historical information about the domains. For example, a user can set an alert threshold and add domain names to be monitored. The system may suggest even more domain names (e.g., based on WHOIS of an initial domain name). A WHOIS search can return a variety of useful data which can be monitored. For example, the system can monitor for changes in Creation Date, Registration Date, Expiry Date, Last Update Organization Name, Organization Address, Admin Name, Admin E-mail, Admin Phone, and other fields of information... GUI generation module 215 generates a graphical user interface allowing submission of trademarks. The trademarks may be submitted to the portfolio monitoring system for evaluation. The portfolio monitoring system can return one or more domain names (which may or may not be owned by the user) that may be of interest. The user is then able to select which domains to monitor. In some cases, the user can also associate notification criteria with each domain name. Examples of notification criteria include, maximum price the user is willing to pay for the domain name, an expiration notification buffer time (e.g., a time period, such as one month, before the expiration of the domain name), and others).
Regarding Claim 8, Nefedov/Malik/Keren/Tan/Keyngnaert teaches the limitations of Claim 8 which state
receiving filtering criteria from the user; filtering, based on the filtering criteria, a plurality of additional entities to generate a set of filtered entities; and providing the set of filtered entities to the user (Nefedov: Para 0055 via The assigned international patent code (IPC) and cited patents contain rich information about a particular patent. In one manner the peer detection system may initially employ a set of rules to determine relevance of candidate patents pcand to a target patent, e.g., a patent of company A, which may be deemed ptarget. First, if pcand's IPC matches (or has non-zero similarity taking into account hierarchical structure) with the IPC of the ptarget, and cites or is cited by ptarget, then pcand is similar to the ptarget, and is assigned a graded match or is simply placed in a first pool. Second, if pcand's IPC matches (or has non-zero similarity) with the IPC of the ptarget, but is neither cited by nor cites ptarget, then pcand is considered somewhat less similar to the ptarget, and is assigned a lower graded match or placed in a separate pool. Third, if pcand's IPC does not match (has no similarity) the IPC of the ptarget, and is neither cited by nor cites ptarget, then p is judged significantly less similar to the ptarget, and is assigned a lower grade or placed ina separate pool. As described below, peer detection by comparing patent portfolios of companies of interest and potential candidate peers may be done based on IPC codes the aggregated to a given hierarchy or using IPC codes specified in patents (direct patents comparison) which may include asymmetric measures as described below. In one manner, a company of interest may be analyzed to produce a fingerprint or DNA based on patent holdings and then compared against the fingerprint or DNA of other companies to arrive at a cluster of like or similar entities. For example, users in the financial services field may use the invention to analyze a company of interest and define industry segments as a collection or cluster of peers. The invention may be used to determine sets of IPCs within a given industry of interest by first broadly considering patent holdings of companies within an industry or market segment and then the user may drill down into results to examine in a more focused manner the entities making up the cluster. Moreover, to assist human users in this endeavor, the invention may present graphical user interface representations to allow the user to more readily visualize and experience the relatedness of companies and to selectively drill down into areas of interest for selective observation).
Regarding Claim 9, Nefedov/Malik/Keren/Tan/Keyngnaert teaches the limitations of Claim 9 which state
wherein the filtering criteria comprise at least one of: a similarity score threshold; a maximum number of filtered entities to be included in the set of filtered entities, a geographical region of the filtered entities, or a market sector of the filtered entities (Nefedov: Para 0055 via The assigned international patent code (IPC) and cited patents contain rich information about a particular patent. In one manner the peer detection system may initially employ a set of rules to determine relevance of candidate patents pcand to a target patent, e.g., a patent of company A, which may be deemed ptarget. First, if pcand's IPC matches (or has non-zero similarity taking into account hierarchical structure) with the IPC of the ptarget, and cites or is cited by ptarget, then pcand is similar to the ptarget, and is assigned a graded match or is simply placed in a first pool. Second, if pcand's IPC matches (or has non-zero similarity) with the IPC of the ptarget, but is neither cited by nor cites ptarget, then pcand is considered somewhat less similar to the ptarget, and is assigned a lower graded match or placed in a separate pool. Third, if pcand's IPC does not match (has no similarity) the IPC of the ptarget, and is neither cited by nor cites ptarget, then p is judged significantly less similar to the ptarget, and is assigned a lower grade or placed in a separate pool. As described below, peer detection by comparing patent portfolios of companies of interest and potential candidate peers may be done based on IPC codes the aggregated to a given hierarchy or using IPC codes specified in patents (direct patents comparison) which may include asymmetric measures as described below. In one manner, a company of interest may be analyzed to produce a fingerprint or DNA based on patent holdings and then compared against the fingerprint or DNA of other companies to arrive at a cluster of like or similar entities. For example, users in the financial services field may use the invention to analyze a company of interest and define industry segments as a collection or cluster of peers. The invention may be used to determine sets of IPCs within a given industry of interest by first broadly considering patent holdings of companies within an industry or market segment and then the user may drill down into results to examine in a more focused manner the entities making up the cluster. Moreover, to assist human users in this endeavor, the invention may present graphical user interface representations to allow the user to more readily visualize and experience the relatedness of companies and to selectively drill down into areas of interest for selective observation).
Regarding Claim 10, Nefedov/Malik/Keren/Tan/Keyngnaert teaches the limitations of Claim 10 which state
wherein the first trademark portfolio further comprises trademarks owned by subsidiaries of the first entity (Nefedov: Para 0062-0063 via For example, a user may input a company name and a set of text terms with an indicia, such as title, of which the user is aware relates to the company of interest, e.g., the query may be “Microsoft & ttl(software).” Using this exemplary query, the user is interested in finding a set of peers having patent portfolios similar to Microsoft in the area of “software.” In this example Microsoft is the target company and its portfolio of patents having “software” in the title is the target patent portfolio. The search is broadly structured and involves searching for Microsoft patents having “software” in the title. Initially the PDSE may identify a set of patents owned by Microsoft that include the term “software” in the title of the invention. Based on this information, the PDSE may, in one exemplary manner, next identify a set of IPC codes extracted from or associated with the resulting Microsoft “software” patents. Next, at step 210, the PDSE 104 may use one or more IPC code(s) associated with the initial search results to identify potential peer candidates, i.e., companies having patents that match to some degree the IPC code of interest... the PDSE is used to compare the patent portfolios of the identified peer candidates against the portfolio of “software” related patents held by Microsoft. In addition, the PDSE may broaden the set of Microsoft patents by using the IPC codes identified in the initial search process and using that to find additional Microsoft patents that do not have “software” in the title but that do match the IPC code at some level. The PDSE may then at step 214, determine a set of similarity scores to determine the degree of similarity between a set of peers and Microsoft).
Regarding Claims 11-15 and 17-19 they are similar to Claims 1-5 and 7-9 respectively and are rejected for the same reasons (Nefedov: Para 0069).
Regarding Claims 20-24 and 26-28, they are analogous to Claims 1-5 and 7- 9 respectively and are rejected for the same reasons (Nefedov: Para 0071).
Claim(s) 29-31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nefedov et al. (US 2016/0350294 A1) in view of Malik et al. (US 20190278777 A1) in view of Keren et al. (US 2021/0248624 A1) in view of Tan (US 2013/0318177 A1) in view of Keyngnaert et al. (US 2015/0324469 A1) further in view of Jessen et al. (US 2016/0350886 A1).
Regarding Claim 29 while the combination of Nefedov/Malik/Keren/Tan/Keyngnaert teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 29 which states wherein monitoring trends in a second portfolio similarity score comprises recalculating the second portfolio similarity score.
Jessen though, with the teachings of Nefedov/Malik/Keren/Tan/Keyngnaert, teaches of
wherein monitoring trends in a second portfolio similarity score comprises recalculating the second portfolio similarity score (Jessen: Para 0194 via the IP- based business intelligence service 102 allows the user 106 to select other scoring algorithms from the group of scoring algorithms. In response to receiving a user selection of a new set of scoring algorithms, the IP-based business intelligence service 102 will perform the evaluation of the quality of the patent or the patent application using the new set of scoring algorithms, and update or display the evaluation results (and/or the combined evaluation score) of the patent or the patent application via the user interface).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nefedov/Malik/Keren/Tan/Keyngnaert with the teachings of Jessen in order to have wherein monitoring trends in a second portfolio similarity score comprises recalculating the second portfolio similarity score. The motivations behind this being to incorporate the teachings of evaluating intellectual property. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claims 30 and 31, it is analogous to Claim 29 and is rejected for the same reasons.
Conclusion
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