DETAILED ACTION
This is in response to the applicant’s communication filed on 4/2/26, wherein:
Claims 1-20 are currently pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/2/26 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
Withdrawn, in light of the amendments filed in this application and in co-pending Applications No. 18/142,531, 18/142,549, and 18/142,749 (reference applications).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 recites a method and therefore, falls into a statutory category. Similar independent claims 9 and 17 recite a system and a method, and therefore, also fall into a statutory category. Claim 1 is used as the exemplary claim.
Step 2A – Prong 1 (Is a Judicial Exception Recited?):
The following underlined limitations identify the abstract limitations which are considered certain methods of organizing human activity
identifying a first entity having first intellectual-property (IP) assets;
generating a graphical user interface (GUI) configured to display on a computing device, the GUI configured to:
display one or more second entities having second IP assets that are similar to one or more of the first IP assets; and
receive an input from the computing device;
receiving, via the GUI, input data representing the input, the input data indicating selection of at least a second entity of the one or more second entities as selected entities;
generating a machine learning model configured to generate embeddings of reference IP assets and to generate clusters of the embeddings;
receiving historical data representing prior clusters of embeddings;
transforming the historical data into a training dataset configured to be utilized by the machine learning model for training purposes;
training the machine learning model utilizing the training dataset such that a specifically trained machine learning model is generated;
generating, utilizing the specifically trained machine learning model and based at least in part on the second IP assets associated with the selected entities, data representing one or more result sets, wherein individual ones of the one or more result sets include clusters of the second IP assets, wherein the embeddings are generated based at least in part on textual content of the second IP assets and the clusters are generated based at least in part on distances between the embeddings;
determining a first minimum filing date associated with a first IP asset within a first cluster of the clusters by comparing filing dates of IP assets within the first cluster;
determining a second minimum filing date associated with a second IP asset within a second cluster of the clusters by comparing filing dates of IP assets within the second cluster; and
causing the GUI to display, on an interactive graphical element comprising a spatial representation of the clusters:
a first visualization of the first minimum filing date associated with the first IP asset within the first cluster of the clusters; and
a second visualization of the second minimum filing date associated with the second IP asset within the second cluster of the clusters, wherein the first visualization and the second visualization are displayed concurrently with the spatial representation of the clusters such that temporal characteristics of each cluster are visually identifiable relative to other clusters.
These limitations constitute analyzing intellectual property (IP) assets by targeting competitor entities to determine overall saturation and/or identify gaps in coverage associated with the IP assets included in competitor entities portfolios (see Specification ¶22), which are processes that, under their broadest reasonable interpretation, are considered certain methods of organizing human activity – commercial or legal interactions (including agreements in the form of contracts and marketing or sales activities or behaviors) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Accordingly, the claim recites an abstract idea.
The following underlined limitations identify the abstract limitations which are considered mathematical concepts:
generating a machine learning model configured to generate embeddings of reference IP assets and to generate clusters of the embeddings;
transforming the historical data into a training dataset configured to be utilized by the machine learning model for training purposes;
training the machine learning model utilizing the training dataset such that a specifically trained machine learning model is generated.
These limitations constitute using math to generate embeddings and clusters, transform the data into a training set, and training the machine learning model using the generic algorithms identified in the Specification such as artificial neural networks, deep learning, etc. (see Specification ¶143), which are processes that, under their broadest reasonable interpretation, are considered mathematical concepts, in the form of a mathematical relationship, mathematical formulas or equations, and/or mathematical calculations. As such, the training of the machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. It is important to note that a mathematical concept need not be expressed in mathematical symbols. See MPEP 2106.04(a). Accordingly, the claim recites an abstract idea. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Step 2A-Prong 2 (Is the Exception Integrated into a Practical Application?): This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of
Claim 1 and claim 17: a graphical user interface (GUI), a computing device, and an interactive graphical element; and
Claim 9: one or more processors; one or more non-transitory computer-readable media comprising instructions; a graphical user interface (GUI), a computing device, and an interactive graphical element.
The computer components are recited at a high-level of generality (i.e., as a generic processing device performing generic computer functions), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Additionally, the receiving and displaying limitations may be considered insignificant extra-solution activity (see MPEP 2106.05(g)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea when considered both individually and as a whole. The claim is directed to an abstract idea.
The limitation reciting “generating, utilizing the specifically trained machine learning model and based at least in part on the second IP assets associated with the selected entities, data representing one or more result sets, wherein individual ones of the one or more result sets include clusters of the second IP assets, wherein the embeddings are generated based at least in part on textual content of the second IP assets and the clusters are generated based at least in part on distances between the embeddings” provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Here, there are no details about how the machine learning model operates to generate the data representing one or more result sets, other than that it is being used to determine clusters and embeddings. The machine learning model is used to generally apply the abstract idea (i.e., perform the mathematical calculations) without placing any limits on how the machine learning model operates. In addition, the claim omits any details as to how the machine learning model solves a technical problem and instead, recites only the idea of a solution or outcome. Also, the claim invokes a generic machine learning model merely as a tool for making the recited mathematical calculation rather than purporting to improve the technology or a computer. See MPEP 2106.05(f). Therefore, the limitation represents no more than mere instructions to apply the judicial exception on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
Step 2B (Does the claim recite additional elements that amount to Significantly More than the Judicial Exception?): The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the steps of the abstract idea amount to no more than mere instructions to apply the exception using a generic computer component. Further, the claims simply append well-understood, routine, and conventional (WURC) activities previously known to the industry, specified at a high level of generality, to the judicial exception, in the form of the extra-solution activity. The courts have recognized that the computer functions claimed (the receiving and displaying limitations) as WURC (see 2106.05(d), identifying receiving or transmitting data over a network as WURC, as recognized by Symantec, and identifying presenting offers as WURC, as recognized by OIP Techs). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible, as when viewed individually, and as a whole, nothing in the claim adds significantly more to the abstract idea.
Dependent claims 2-8, 10-16, and 18-20 merely recite further embellishments of the abstract idea of independent claims 1, 9, and 17 as discussed above with respect to integration of the abstract idea into a practical application, and these features only serve to further limit the abstract idea of independent claims 1, 9, and 17; however, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limits.
In light of the detailed explanation and evidence provided above, the Examiner asserts that the claimed invention, when the limitations are considered individually and as whole, is directed towards an abstract idea.
Notice
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 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.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Burns et al. (US 11232383), in view of Yim et al. (US 20190347556), in view of Lee et al. (US 20100250340), and further in view of Tobias et al. (US 20220100358).
Referring to claims 1, 9, & 17:
(Claims 1, 9, and 17 are substantially similar in scope and language)
Burns discloses a method, system comprising: one or more processors; and one or more non-transitory computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, and method comprising:
identifying a first entity having first intellectual-property (IP) assets (see at least Burns: Col. 41 Line 33-67 and Col. 42 Line 5-35; see at least Burns Cols. 52-54: discussing the plethora of information collected, analyzed, quantified, and presented to users including market information; see also Burns: Col. 67-68: discussing the system quantifying a plurality of technologies and assets into a score and aggregating a plurality of scores to identify the potential success; see also Burns: Col. 50 Line 17-28; see also Burns: Col. 27 Line 20-55; see also Burns: Col. 56 Line 27-59 and Col. 78 Line 1-3);
generating a graphical user interface (GUI) configured to display on a computing device, the GUI configured to: display one or more second entities having second IP assets that are similar to one or more of the first IP assets (see at least Burns: Col. 41 Line 33-67 and Col. 42 Line 5-35; see at least Burns Cols. 52-54: discussing the plethora of information collected, analyzed, quantified, and presented to users including market information; see also Burns: Col. 67-68: discussing the system quantifying a plurality of technologies and assets into a score and aggregating a plurality of scores to identify the potential success; see also Burns: Col. 50 Line 17-28; see also Burns: Col. 27 Line 20-55; see also Burns: Col. 56 Line 27-59 and Col. 78 Line 1-3; see also Burns: Cols. 61-62: discussing scoring ideas, patens, products, and services to known patents and publications; see also Burns: Col. 48 Line 1-40, Col. 49 Line 10-58, Col. 51 Line 25-55, Col. 52 Line 29-52, Col. : discussing phase 0 through 2 discussing the formulation, adjustment, and tailoring of solutions to technical challenges; see also Burns Col. 54, Col. 55 Line 15-67, Col. 63-64: discussing and identifying disruptive innovation determination and development);
receive an input from the computing device; and receiving, via the GUI, input data representing the input, the input data indicating selection of at least a second entity of the one or more second entities as selected entities (see at least Burns: Col. 41 Line 33-67 and Col. 42 Line 5-35; see at least Burns Cols. 52-54: discussing the plethora of information collected, analyzed, quantified, and presented to users including market information; see also Burns: Col. 67-68: discussing the system quantifying a plurality of technologies and assets into a score and aggregating a plurality of scores to identify the potential success; see also Burns: Col. 50 Line 17-28; see also Burns: Col. 27 Line 20-55; see also Burns: Col. 56 Line 27-59 and Col. 78 Line 1-3; see also Burns: Cols. 61-62: discussing scoring ideas, patens, products, and services to known patents and publications; see also Burns: Col. 48 Line 1-40, Col. 49 Line 10-58, Col. 51 Line 25-55, Col. 52 Line 29-52, Col. : discussing phase 0 through 2 discussing the formulation, adjustment, and tailoring of solutions to technical challenges; see also Burns Col. 54, Col. 55 Line 15-67, Col. 63-64: discussing and identifying disruptive innovation determination and development);
generating a machine learning model configured to [generate embeddings of reference IP assets and to generate clusters of the embeddings]; Burns discloses the system generating and utilizing a machine learning model to generate predictive analytics including “machine learning (ML), artificial intelligence (AI), neural networks (NNs) (e.g., long short term memory (LSTM) neural networks), deep learning, historical data, and/or data mining to make future predictions and/or models.” (see at least Burns: Col. 81 Line 25-45). The functional language directed to generate embeddings of reference IP assets and to generate clusters of the embeddings is taught by Yim below.
Burns discloses the system generating and utilizing a machine learning model to generate predictive analytics including “machine learning (ML), artificial intelligence (AI), neural networks (NNs) (e.g., long short term memory (LSTM) neural networks), deep learning, historical data, and/or data mining to make future predictions and/or models.” (see at least Burns: Col. 81 Line 25-45). Burns fails to explicitly disclose receiving historical data representing prior clusters of embeddings; transforming the historical data into a training dataset configured to be utilized by the machine learning model for training purposes; and training the machine learning model utilizing the training dataset such that a specifically trained machine learning model is generated.
However, Yim, discloses a system for an AI based discovery search engine employing an embedding neural network training algorithm. Yim discloses receiving historical data representing prior clusters of embeddings; transforming the historical data into a training dataset configured to be utilized by the machine learning model for training purposes; training the machine learning model utilizing the training dataset such that a specifically trained machine learning model is generated (see at least Yim: ¶ 54 “determination may be made at 255 whether to continue training of the embedding NN. In one embodiment, the embedding NN may be trained using TensorFlow machine learning framework”; see at least Yim: ¶ 33 “embedding neural network training server may send a training data request 125 to a repository 110 to obtain the specified dataset for the training period”; see at least Yim: ¶ 38 “embedding NN training server may send an embedding NN training response 145 to the administrator. The embedding NN training response may be used to inform the administrator that training was completed successfully”; see at least Yim: ¶ 44-47 “training period for training the embedding NN may be determined at 205. For example, the training period (e.g., one day) may determine which training data is used to train the embedding NN. In one implementation, the embedding NN training request may be parsed (e.g., using PHP commands) to determine the training period. In another implementation, a setting associated with the embedding NN may specify the training period”), using previous clusters and embeddings (historical information) as training datasets to train the model and create a specific machine learning model utilizing the training set (see at least Yim: ¶ 97 “The example shows Cosine Distances and Euclidean Distances calculated for embeddings using historical data”; see also Yim: ¶ 30 “the ANDSE may utilize unsupervised learning to generate a neural network that calculates embeddings for contexts, and may find neighboring contexts using distances (e.g., Cosine distance and/or Euclidean distance) between embeddings”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of utilizing unsupervised learning to generate a neural network that calculates embeddings (as disclosed by Yim) to the known system and method for automated analysis, evaluation, and assessment of technology, intellectual property, innovation, corporate management resources and structure and commercialization opportunity utilizing machine learning and historical information (as disclosed by Burns) to generate a neural network that calculates embeddings for contexts, and may find neighboring contexts using distances (e.g., Cosine distance and/or Euclidean distance) between embeddings. One of ordinary skill in the art would have been motivated to apply the known technique of utilizing unsupervised learning to generate a neural network that calculates embeddings because it would generate a neural network that calculates embeddings for contexts, and may find neighboring contexts using distances (e.g., Cosine distance and/or Euclidean distance) between embeddings (see Yim ¶ 30). Furthermore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of utilizing unsupervised learning to generate a neural network that calculates embeddings (as disclosed by Yim) to the known system and method for automated analysis, evaluation, and assessment of technology, intellectual property, innovation, corporate management resources and structure and commercialization opportunity utilizing machine learning and historical information (as disclosed by Burns) to generate a neural network that calculates embeddings for contexts, and may find neighboring contexts using distances (e.g., Cosine distance and/or Euclidean distance) between embeddings, because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique of utilizing unsupervised learning to generate a neural network that calculates embeddings to the known system and method for automated analysis, evaluation, and assessment of technology, intellectual property, innovation, corporate management resources and structure and commercialization opportunity utilizing machine learning and historical information to generate a neural network that calculates embeddings for contexts, and may find neighboring contexts using distances (e.g., Cosine distance and/or Euclidean distance) between embeddings). See also MPEP § 2143(I)(D).
The combination of Burns and Yim teaches generating, utilizing the specifically trained machine learning model and based at least in part on the second IP assets associated with the selected entities, data representing one or more result sets, wherein individual ones of the one or more result sets include clusters of the second IP assets (see at least Burns: Col. 41 Line 33-67 and Col. 42 Line 5-35; see at least Burns Cols. 52-54: discussing the plethora of information collected, analyzed, quantified, and presented to users including market information; see also Burns: Col. 67-68: discussing the system quantifying a plurality of technologies and assets into a score and aggregating a plurality of scores to identify the potential success; see also Burns: Col. 50 Line 17-28; see also Burns: Col. 27 Line 20-55; see also Burns: Col. 56 Line 27-59 and Col. 78 Line 1-3; see also Burns: Cols. 61-62: discussing scoring ideas, patens, products, and services to known patents and publications; see also Burns: Col. 48 Line 1-40, Col. 49 Line 10-58, Col. 51 Line 25-55, Col. 52 Line 29-52, Col. : discussing phase 0 through 2 discussing the formulation, adjustment, and tailoring of solutions to technical challenges; see also Burns Col. 54, Col. 55 Line 15-67, Col. 63-64: discussing and identifying disruptive innovation determination and development; see at least Burns: Col. 81 Line 27-40: discussing the system use and employment of machine learning, AI, deep learning, neural networks, artificial neural networks and support vector machines);
Examiner notes that Burns discusses determining and displaying clustered information quantifying a plurality of technologies and assets into a score and aggregating a plurality of scores to identify the potential success, and Yim teaches the system finds relationships using a clustering approach (see at least Yim: ¶ 53 and 95) but fails to explicitly state: determining a first minimum filing date associated with a first IP asset within a first cluster of the clusters; determining a second minimum filing date associated with a second IP asset within a second cluster of the clusters; and causing the GUI to display: a first visualization of the first minimum filing date associated with the first IP asset within the first cluster of the clusters; and a second visualization of the second minimum filing date associated with the second IP asset within the second cluster of the clusters.
However, Lee, is directed to a system for processing and displaying intellectual property information. Lee discloses determining a first minimum filing date associated with a first IP asset within a first cluster of the clusters; determining a second minimum filing date associated with a second IP asset within a second cluster of the clusters; and causing the GUI to display: a first visualization of the first minimum filing date associated with the first IP asset within the first cluster of the clusters; and a second visualization of the second minimum filing date associated with the second IP asset within the second cluster of the clusters (see at least Lee: ¶ 96-97 “The global map 2440 includes a time control or time slider control that a user may manipulate. In response to user manipulation, an algorithm may access information germane to a query with respect to time and update the global map graphic accordingly”; see also Lee: ¶ 113-114 “FIG. 25 shows a representation of a figure from one of the related applications, which are incorporated by reference herein. FIG. 25 shows an exemplary system 2500 and various associated exemplary methods 2510, 2520, and 2530 for selecting a time, a time frame, an event or events and graphically displaying information associated with the selected time, the selected time frame, the selected event or the selected events”; see also Lee: ¶ 116-117 “The user interface 2600 allows a user to select a point in time via the time control 2650. A user may manipulate the slider or elect to automate the slider to move backward or forward in time”; see also Lee: ¶ 128 “FIG. 30 is a diagram of an actual time slider 3010 with three rows or columns where the length of the column changes upon placement of a cursor over the timeline 3010”; see also Lee: Claims 16-18 and ¶ 123 and 131-132).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of using a slider mechanism to control the display of specific patent information to various timeframes (as disclosed by Lee) to the known method and system for managing, analyzing, processing, and presenting intellectual property information wherein the system is capable of displaying a plethora of information based on the inputs of users and generating the information in a single display of a plurality of layered information (as disclosed by the combination of Burns and Yim) to provide understandable graphics, bookmarks, documents associated with codes and optionally other documents and/or graphics. One of ordinary skill in the art would have been motivated to apply the known technique of using a slider mechanism to control the display of specific patent information to various timeframes because it would provide understandable graphics, bookmarks, documents associated with codes and optionally other documents and/or graphics (see Lee ¶ 95). Furthermore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of using a slider mechanism to control the display of specific patent information to various timeframes (as disclosed by Lee) to the known method and system for managing, analyzing, processing, and presenting intellectual property information wherein the system is capable of displaying a plethora of information based on the inputs of users and generating the information in a single display of a plurality of layered information (as disclosed by the combination of Burns and Yim) to provide understandable graphics, bookmarks, documents associated with codes and optionally other documents and/or graphics, because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique of using a slider mechanism to control the display of specific patent information to various timeframes to the known method and system for managing, analyzing, processing, and presenting intellectual property information wherein the system is capable of displaying a plethora of information based on the inputs of users and generating the information in a single display of a plurality of layered information to provide understandable graphics, bookmarks, documents associated with codes and optionally other documents and/or graphics). See also MPEP § 2143(I)(D).
Burns, as modified by Yim and Lee, discloses determining and displaying clustered information quantifying a plurality of technologies and assets into a score and aggregating a plurality of scores to identify the potential success. Burns, as modified by Yim and Lee, does not disclose wherein the embeddings are generated based at least in part on textual content of the second IP assets and the clusters are generated based at least in part on distances between the embeddings; wherein the determining a first minimum filing date associated with a first IP asset within a first cluster of the clusters is performed by comparing filing dates of IP assets within the first cluster; and wherein the determining a second minimum filing date associated with a second IP asset within a second cluster of the clusters is performed by comparing filing dates of IP assets within the second cluster; and causing the GUI to display on an interactive graphical element comprising a spatial representation of the clusters; wherein the first visualization and the second visualization are displayed concurrently with the spatial representation of the clusters such that temporal characteristics of each cluster are visually identifiable relative to other clusters.
However, Tobias discloses a similar system for an intellectual property landscaping platform (abstract). Tobias discloses wherein the embeddings are generated based at least in part on textual content of the second IP assets and the clusters are generated based at least in part on distances between the embeddings {Tobias [0052]-[0056]; For example, the vector component may be configured to generate a vector representation of a publication and use the vector representation to identify IP assets having similar vector representations. Techniques to generate vectors representing IP assets may include vectorization techniques such as Doc2Vec, or other similar techniques. Additionally, or alternatively, techniques to generate vectors representing IP assets may include a method that takes a document, such as an IP asset, and turns it into a vector form as a list of floating-point numbers based at least in part on the document's text contents. This vector form may be called an embedding. This embedding may be used to calculate distance, and therefore similarity, between documents [0053] and As mentioned above, the embedding may be used to calculate distance, and therefore similarity, between documents. The embeddings may also be utilized to create thematic groups of documents. The thematic groups may be determined utilizing a set of keywords determined following analysis of a text portion of the IP assets, and the result may be a visual display of document groups (e.g., the clusters) that share similar themes [0056]};
wherein the determining a first minimum filing date associated with a first IP asset within a first cluster of the clusters is performed by comparing filing dates of IP assets within the first cluster; and wherein the determining a second minimum filing date associated with a second IP asset within a second cluster of the clusters is performed by comparing filing dates of IP assets within the second cluster {Tobias [0047] and Fig. 10; the slider filter control may be configured to receive user input representing a lower bound and/or an upper bound associated with a priority date and/or proprietary score associated with the IP assets included in the clusters of the selected result set [0047] where the assets with a filing date which are later than the lower bound and/or earlier than the upper bound are displayed};
causing the GUI to display on an interactive graphical element comprising a spatial representation of the clusters; wherein the first visualization and the second visualization are displayed concurrently with the spatial representation of the clusters such that temporal characteristics of each cluster are visually identifiable relative to other clusters {Tobias [0146][0147] Fig. 10; FIG. 10 illustrates an example user interface 1000 for displaying data associated with a user account representing a spatial representation of cluster(s) that may be included in a selected result set and/or included in a user-defined result set [0146] and the graphical indicators 1012 may be color coded, such that IP assets that are included in a cluster of the selected result set may be represented by a graphical indicator 1012 having a color associated with the cluster [0147]}.
It would have been obvious for a person of ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to modify the system disclosed in Burns, Yim, and Lee to incorporate embeddings, clusters, comparing filing dates, an interactive graphical element, and displaying the visualizations as taught by Tobias because this would provide a manner for identifying IP assets that may be determined to be similar to the IP portfolio of one or more target entities (Tobias [0025]), thus aiding the user by accurately and efficiently visualizing a landscape of the clusters of IP assets.
Referring to claims 2 & 10:
(substantially similar in scope and language)
The combination of Burns, Yim, Lee, and Tobias disclose the method of claim 1, system of claim 9, including wherein individual ones of the clusters comprise a respective IP asset with a minimum filing date and the method further comprises causing the GUI to display visualizations of respective IP assets associated with the individual ones of the clusters (see at least Lee: ¶ 96-97 “The global map 2440 includes a time control or time slider control that a user may manipulate. In response to user manipulation, an algorithm may access information germane to a query with respect to time and update the global map graphic accordingly”; see also Lee: ¶ 113-114 “FIG. 25 shows a representation of a figure from one of the related applications, which are incorporated by reference herein. FIG. 25 shows an exemplary system 2500 and various associated exemplary methods 2510, 2520, and 2530 for selecting a time, a time frame, an event or events and graphically displaying information associated with the selected time, the selected time frame, the selected event or the selected events”; see also Lee: ¶ 116-117 “The user interface 2600 allows a user to select a point in time via the time control 2650. A user may manipulate the slider or elect to automate the slider to move backward or forward in time”; see also Lee: ¶ 128 “FIG. 30 is a diagram of an actual time slider 3010 with three rows or columns where the length of the column changes upon placement of a cursor over the timeline 3010”; see also Lee: Claims 16-18 and ¶ 123 and 131-132).
Referring to claims 3, 11, & 18:
(substantially similar in scope and language)
The combination of Burns, Yim, Lee, and Tobias disclose the method of claim 2, system of claim 10, and method of claim 17 including wherein the visualizations of respective IP assets associated with individual ones of the clusters comprises a vertical list overlayed on a third visualization of the clusters (see at least Lee: ¶ 73-74).
Referring to claims 4 & 12:
(substantially similar in scope and language)
The combination of Burns, Yim, Lee, and Tobias disclose the method of claim 2, and system of claim 10, including wherein the visualizations of respective IP assets associated with individual ones of the clusters are selectable and the method further comprises receiving a selection of at least one of the visualizations (see at least Lee: ¶ 73-74).
Referring to claims 5, 13, & 20:
(substantially similar in scope and language)
The combination of Burns, Yim, Lee, and Tobias disclose the method of claim 1, system of claim 9, and method of claim 17 including further comprising receiving a selection of at least one of the first visualization or the second visualization and displaying, in response to the selection, at least one of: a cluster name; the first minimum filing date; the second minimum filing date; a total number of IP assets within a cluster; an entity associated with assignment to a threshold number of IP assets within a cluster; or a metric associated with IP assets within a cluster (see at least Lee: ¶ 96-97 “The global map 2440 includes a time control or time slider control that a user may manipulate. In response to user manipulation, an algorithm may access information germane to a query with respect to time and update the global map graphic accordingly”; see also Lee: ¶ 113-114 “FIG. 25 shows a representation of a figure from one of the related applications, which are incorporated by reference herein. FIG. 25 shows an exemplary system 2500 and various associated exemplary methods 2510, 2520, and 2530 for selecting a time, a time frame, an event or events and graphically displaying information associated with the selected time, the selected time frame, the selected event or the selected events”; see also Lee: ¶ 116-117 “The user interface 2600 allows a user to select a point in time via the time control 2650. A user may manipulate the slider or elect to automate the slider to move backward or forward in time”; see also Lee: ¶ 128 “FIG. 30 is a diagram of an actual time slider 3010 with three rows or columns where the length of the column changes upon placement of a cursor over the timeline 3010”; see also Lee: Claims 16-18 and ¶ 123 and 131-132).
Referring to claims 6, 14, & 19:
(substantially similar in scope and language)
The combination of Burns, Yim, Lee, and Tobias disclose the method of claim 1, system of claim 9, and method of claim 17 including wherein at least one of the first visualization or the second visualization comprises a timeline-graph (see at least Lee: ¶ 73-74).
Referring to claims 7 & 15:
(substantially similar in scope and language)
The combination of Burns, Yim, Lee, and Tobias disclose the method of claim 1, and system of claim 9, including identifying, from the second IP assets, foreign IP assets and design IP assets as third IP assets; and removing the third IP assets from the second IP assets prior to generating the data representing the one or more result sets (see at least Lee: ¶ 102-103).
Referring to claims 8 & 16:
(substantially similar in scope and language)
The combination of Burns, Yim, Lee, and Tobias disclose the method of claim 1 and system of claim 9including determining multiple entities associated with a respective number of IP assets within the clusters; and causing the GUI to display the multiple entities ranked in order of individual ones of the multiple entities associated respective number of IP assets (see at least Lee: ¶ 103).
Response to Arguments
Provisional Obviousness-Type Double Patenting Rejection
Withdrawn, for the reasons stated above.
101 Rejections
Applicant merely refers to the amendments and does not provide substantive arguments to which Examiner can respond. The rejection has been updated to address the amendments submitted.
103 Rejections
Applicant argues that the prior art does not disclose the amended limitations. Examiner has provided new art to address the amendments.
Conclusion
Applicant requests an Interview. Examiner notes Applicant’s request; however, compact prosecution is best served by conducting the Interview after Applicant’s receipt of this office action, and encourages Applicant to call Examiner at the number below or use AIR to schedule an Interview if Applicant has any questions after receipt of the office action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARRIE S GILKEY whose telephone number is (571)270-7119. The examiner can normally be reached Monday-Thursday 7:30-4:30 CT and Friday 7:30-12 CT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached on 571-270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CARRIE S GILKEY/Primary Examiner, Art Unit 3626
Prosecution Insights