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 Application
This communication is a Final Office Action in response to the Amendment, Remarks, and Arguments filed on the 12/18/2025. Currently Claim 1-26 are pending. Claims 19-26 are new. No claims are allowed.
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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under MPEP 2106, when considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A prong 1), and if so, it must additionally be determined whether the claim is integrated into a practical application (step 2A prong 2). If an abstract idea is present in the claim without integration into a practical application, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself (step 2B).
In the instant case, claims 1-26 are directed to a system and method. Thus, each of the claims falls within one of the four statutory categories (step 1). However, the claims also fall within the judicial exception of an abstract idea (step 2A). While claims 1, 3, 8, and 10, are directed to different categories, the language and scope are substantially the same and have been addressed together below.
Under Step 2A Prong 1, the test is to identify whether the claims are “directed to” a judicial exception. Examiner notes that the claimed invention is directed to an abstract idea in that the instant application is directed to certain methods of organizing human activity specifically legal or commercial interactions (innovation analysis) (see MPEP 2106.04(a)(2)(II)), mental processes (see MPEP 2106.04(a)(2)(III), and mathematical calculations (see MPEP 2106.04(a)(2)(I)).
Examiner notes that:
The phrase "methods of organizing human activity" is used to describe concepts relating to: fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In addition, the Court in Alice described the concept of risk hedging identified as an abstract idea in Bilski as ‘‘a method of organizing human activity’’. Id. Previously, in Bilski, the Court concluded that hedging is a ‘‘fundamental economic practice’’ and therefore an abstract idea. 561 U.S. at 611–612, 95 USPQ2d at 1010.
The courts have used the phrases "fundamental economic practices" or "fundamental economic principles" to describe concepts relating to the economy and commerce. Fundamental economic principles or practices include hedging, insurance, and mitigating risks.
The term "fundamental" is not used in the sense of necessarily being "old" or "well-known." See, e.g., OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015) (a new method of price optimization was found to be a fundamental economic concept); In re Smith, 815 F.3d 816, 818-19, 118 USPQ2d 1245, 1247 (Fed. Cir. 2016) (describing a new set of rules for conducting a wagering game as a "fundamental economic practice"); In re Greenstein, 774 Fed. Appx. 661, 664, 2019 USPQ2d 212400 (Fed Cir. 2019) (non-precedential) (claims to a new method of allocating returns to different investors in an investment fund was a fundamental economic concept). However, being old or well-known may indicate that the practice is fundamental. See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 219-20, 110 USPQ2d 1981-82 (2014) (describing the concept of intermediated settlement, like the risk hedging in Bilski, to be a "‘fundamental economic practice long prevalent in our system of commerce’" and also as "a building block of the modern economy") (citation omitted); Bilski v. Kappos, 561 U.S. 593, 611, 95 USPQ2d 1001, 1010 (2010) (claims to the concept of hedging are a "fundamental economic practice long prevalent in our system of commerce and taught in any introductory finance class.") (citation omitted); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1313, 120 USPQ2d 1353, 1356 (Fed. Cir. 2016) ("The category of abstract ideas embraces ‘fundamental economic practice[s] long prevalent in our system of commerce,’ … including ‘longstanding commercial practice[s]’").
Another example of a case identifying a claim as reciting a fundamental economic practice is Bancorp Services., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 103 USPQ2d 1425 (Fed. Cir. 2012). The fundamental economic practice at issue in Bancorp pertained to insurance. The patentee in Bancorp claimed methods and systems for managing a life insurance policy on behalf of a policy holder, which comprised steps including generating a life insurance policy including a stable value protected investment with an initial value based on a value of underlying securities, calculating surrender value protected investment credits for the life insurance policy; determining an investment value and a value of the underlying securities for the current day; and calculating a policy value and a policy unit value for the current day. 687 F.3d at 1270-71, 103 USPQ2d at 1427. The court described the claims as an "attempt to patent the use of the abstract idea of [managing a stable value protected life insurance policy] and then instruct the use of well-known [calculations] to help establish some of the inputs into the equation." 687 F.3d at 1278, 103 USPQ2d at 1433 (alterations in original) (citing Bilski).
"Commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations.
Other examples of subject matter where the commercial or legal interaction is an agreement in the form of contracts include: i. managing a stable value protected life insurance policy via performing calculations, Bancorp Servs., LLC v. Sun Life Assur. Co. of Canada (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012). Other examples of subject matter where the commercial or legal interaction is advertising, marketing or sales activities or behaviors include: ii. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 485, 203 USPQ 812, 816 (CCPA 1979);
Claim 1: An information retrieval system comprising:a terminal configured to receive a designated application;anda server configured to perform processing using a storage,[[;]] [[and]] wherein the terminal is display information on the basis of a processing result of the server,wherein the storage comprises at least data of specifications, drawings, and value information of a plurality of applications, wherein the drawings comprise support drawings corresponding to scopes of claims, wherein the server calculates a first degree of similarity between a specification of the designated application and each of the specifications of the plurality of applications, wherein the first degree of similarity is obtained by digitizing each text of specification,wherein the server extracts a plurality of first similar applications from the plurality of applications on the basis of the first degree of similarity, wherein the server calculates a second degree of similarity between the support drawings of at least one of the plurality of first similar applications and at least one of drawings of the designated application, respectively, wherein the second degree of similarity is processed by at least one of image processing,wherein the server extracts at least one similar drawing from the drawings of the designated application on the basis of the second degree of similarity, and wherein for at least one of the plurality of first similar applications, the terminal displays the value information, the support drawing, and the similar drawing
Claim 3: An information retrieval system comprising:a terminal configured to receive a designated application; anda server configured to perform processing using a storage,wherein the terminal is configured to output information on the basis of a processing result of the server,wherein the storage comprises at least data of specifications, drawings, and value information of a plurality of applications, wherein the drawings comprise support drawings corresponding to scopes of claims, wherein the server calculates a first degree of similarity between a specification of the designated application and each of the specifications of the plurality of applications, wherein the first degree of similarity is obtained by digitizing each text of specification,wherein the server extracts a plurality of first similar applications from the plurality of applications on the basis of the first degree of similarity, wherein the server calculates a second degree of similarity between the support drawings of each of the plurality of first similar applications and at least one of drawings of the designated application, respectively, wherein the second degree of similarity is processed by at least one of image processing,wherein the server extracts at least one second similar application by performing one or both of narrowing down and rearrangement of the plurality of first similar applications on the basis of the second degree of similarity, wherein the server extracts at least one similar drawing from the drawings of the designated application on the basis of the second degree of similarity, and wherein for the second similar application, the terminal displays the value information, the support drawing, and the similar drawing.
Claim 8: An information retrieval method comprising: receiving a designated application; calculating a first degree of similarity between a specification of the designated application and each of specifications of a plurality of applications; extracting a plurality of first similar applications from the plurality of applications on the basis of the first degree of similarity; calculating a second degree of similarity between a support drawing corresponding to a scope of claims of at least one of the plurality of first similar applications and at least one of drawings of the designated application, respectively; extracting at least one similar drawing from the drawings of the designated application on the basis of the second degree of similarity; and displaying value information, the support drawing, and the similar drawing for at least one of the plurality of first similar applications[[.]],wherein the first degree of similarity is obtained by digitizing each text of specification, and wherein the second degree of similarity is processed by at least one of image processing.
Claim 10: An information retrieval method comprising:receiving a designated application; calculating a first degree of similarity between a specification of the designated application and each of specifications of a plurality of applications; extracting a plurality of first similar applications from the plurality of applications on the basis of the first degree of similarity; calculating a second degree of similarity between a support drawing corresponding to a scope of claims of each of the plurality of first similar applications and at least one of drawings of the designated application, respectively; extracting at least one second similar application by performing one or both of narrowing down and rearrangement of the plurality of first similar applications on the basis of the second degree of similarity; extracting at least one similar drawing from the drawings of the designated application on the basis of the second degree of similarity; and displaying value information, the support drawing, and the similar drawing for the second similar application,[[.]] wherein the first degree of similarity is obtained by digitizing each text of specification, and wherein the second degree of similarity is processed by at least one of image processing.
Claim 1, 3, 8, and 10 is directed to a method and systems for receiving application information related to patent applications, processing the application compared to stored patent applications to generate multiple metrics in the form of similarity scores, and outputting the results of the analysis which is similar to the abstract idea identified in the MPEP 2106.04(a)(2)(II) in that the claims are directed to certain methods of organizing human activity such as commercial or legal interactions. The claim limitations, substantially comprising the body of the claim, recite a process analyzing innovation with computer elements being used in their intended purposes. Examiner notes that the court has been clear that inventions reciting methods of organizing human activity in the form of commercial or business interactions between people are directed to the judicial exception found in grouping “II”. The limitations above closely follow the steps of the claims involving organizing human activity set forth in the group “II” of the MPEP 2106.04(a)(2). Therefore, the claims recite a method of organizing human activity.
Examiner notes that the claimed invention is more similar to the identified abstract ideas within Bancorp and In re Maucorps.
Alternatively, Furthermore, Examiner notes that the system is directed to a mental process. The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
Claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); and a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011);
Examiner notes that Claims 1, 3, 8, and 10 are directed to obtaining patent information, performing an analysis on the data, and outputting the result which is directed to concepts that are performed mentally and a product of human mental work. Examiner notes that quantitative and qualitative analysis have been applied to patent and intellectual property information long before the invention of computers or computer models. Value and quality assessments have been determined by the mind of inventors, experts, and customers long before the standard computer functions and models were established. Examiner notes that the claimed invention is directed to receiving information related to patents and applying a quantitative and qualitative analysis on the data, and outputting the result which is similar to the abstract ideas identified in MPEP 2106.04(a)(2)(III) and the steps involved human judgments, observations, and evaluations that can be practically or reasonably performed in the human mind consistent with the “mental process” grouping set forth in MPEP 2106.04(a)(2)(III).
Examiner notes that the claimed invention amounts to a mental process in that the system is collecting command information, analyzing or processing the information for display which is similar to the abstract ideas identified in Electric Power Group and Classen.
wherein the first degree of similarity is obtained by vectorizing each text of specification
wherein the second degree of similarity is calculated using a convolutional neural network
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").
In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are "human cognitive actions" that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were "the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries." 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of "anonymous loan shopping", which was a concept that could be "performed by humans without a computer." 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
Both product claims (e.g., computer system, computer-readable medium, etc.) and process claims may recite mental processes. For example, in Mortgage Grader, the patentee claimed a computer-implemented system and a method for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The Federal Circuit determined that both the computer-implemented system and method claims were directed to "anonymous loan shopping", which was an abstract idea because it could be "performed by humans without a computer." 811 F.3d. at 1318, 1324-25, 117 USPQ2d at 1695, 1699-1700. See also FairWarning IP, 839 F.3d at 1092, 120 USPQ2d at 1294 (identifying both system and process claims for detecting improper access of a patient's protected health information in a health-care system computer environment as directed to abstract idea of detecting fraud); Content Extraction & Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 1345, 113 USPQ2d 1354, 1356 (Fed. Cir. 2014) (system and method claims of inputting information from a hard copy document into a computer program). Accordingly, the phrase "mental processes" should be understood as referring to the type of abstract idea, and not to the statutory category of the claim.
Examples of product claims reciting mental processes include: An application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356; and A computer readable medium containing program instructions for detecting fraud – CyberSource, 654 F.3d at 1368 n. 1, 99 USPQ2d at 1692 n.1.
Examiner notes that the claimed in invention is similar to the Voter Verified, Inc., FairWarning, Mortgage Grader, Berkheimer, Content Extraction and CyberSource applications wherein the court identified computer system or “computing device”, “framework”, “computer user interface”, or “display” is merely serving as a tool to perform the mental process.
The conclusion that the claim recites an abstract idea within the groupings of the MPEP 2106.04(a)(2) remains grounded in the broadest reasonable interpretation consistent with the description of the invention in the specification. For example, (App. Spec. ¶ 4), the “a system that is capable of retrieving information related to input intellectual property information”. Accordingly, the Examiner submits claims 1, 3, 8, and 10 recite an abstract idea based on the language identified, and the abstract ideas previously identified based on that language that remains consistent with the groupings of Step 2A Prong 1 of the MPEP 2106.04(a)(1).
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application.
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to a method instructing the reader to implement the identified method of organizing human activity of commercial or legal interactions such as transacting intellectual property. Examiner notes that the system provided merely receives information from a user related to an invention and analyzes the data. Nothing is presented as to how the server and device systems improved, how the network is improved, how the computer system used is improved by the claimed invention. The specific limitations of the claimed invention which are directed to a server, device and network amount to generic computer components. The invention merely directs the users to implement the method via generic computer structure. For instance, the additional elements or combination of elements other than the abstract idea itself include the elements such as a “system” and/or “processing unit” recited at a high level of generality. Accordingly, the claimed computer structure read in light of the specification can be any device and includes any wide range of possible devices comprising a number of components that are “well-known” and include an indiscriminate “device” (e.g., processor, memory, server, network, etc.). Thus, the claimed structure amounts to appending generic computer elements to abstract idea comprising the body of the claim. The computing element is only involved at a general, high level, and do not have the particular role within any of the functions but to be a generically claimed “The tenninal 230 includes a communication unit 161 b, a transmission path 164, an input unit 115, a storage unit 125, a processing unit 135, and a display unit 145. Examples of the terminal 230 include a tablet personal computer, a laptop personal computer, and various portable information terminals. The terminal 230 may be a desktop personal computer without the display unit 145 and may be connected to a monitor functioning as the display unit 145, or the like”. (App. Spec. ¶ 163).
Similarly, reciting the abstract idea as software functions used to program a generic computer is not significant or meaningful: generic computers are programmed with software to perform various functions every day. A programmed generic computer is not a particular machine and by itself does not amount to an inventive concept because, as discussed in MPEP 2106.05(a), adding the words “apply it” (or an equivalent) with the judicial exception, or more instructions to implement an abstract idea on a computer, as discussed in Alice, 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)), is not enough to integrate the exception into a practical application. Further, it is not relevant that a human may perform a task differently from a computer. It is necessarily true that a human might apply an abstract idea in a different manner from a computer. What matters is the application, “stating an abstract idea while adding the words ‘apply it with a computer’” will not render an abstract idea non-abstract. Tranxition v. Lenovo, Nos. 2015-1907, -1941, -1958 (Fed. Cir. Nov. 16, 2016), slip op. at 7-8.
Here, the instructions entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the role of the generic computing element recited in claims 21, 36, and 37 are the same as the role of the computer in the claims considered by the Supreme Court in Alice, and the claim as whole amounts merely to an instruction to apply the abstract idea on the generic device. Therefore, the claims have failed to integrate a practical application (2106.04(d)). Under the MPEP 2106.05, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that the claims 1-18, do not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea.
Claim 2, 4-7, 9, and 11-18 are further embellishments of the abstract idea and does not amount to significantly more.
Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter. See MPEP 2106.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) 1-26 are rejected under 35 U.S.C. 103 as being unpatentable over U.S Patent Application Publication No. 20090259506 to Barney in view of U.S. Patent Application Publication No. 20160350886 to Jessen et al. (hereinafter Jessen).
Referring to Claim 1 and 8 (substantially similar in scope and language), Barney discloses an information retrieval system (see at least Barney: Abstract), comprising:
a terminal configured to receive a designated application (see at least Barney: see at least Barney: ¶ 34-35 “identifying and comparing various characteristics of each individual patent to a statistically determined distribution of the same characteristics within a given patent population”; see also Barney: ¶ 40-41 “method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated”; see also Barney: ¶ 44 “individual selected patent in accordance with its overall patent quality ranking”; see also Barney: ¶ 47-50, 68, 72-74, and 82);
a server configured to perform processing using a storage (see at least Barney: ¶ 40-41 “The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest. A second database (or identified subset of the first database) of selected patent information is also provided identifying and/or quantifying certain selected characteristics of individual patents from a second population of patents generally lacking or having reduced incidence of the selected patent quality of interest”; see also Barney: ¶ 45, 72, 76-79, 88, 164-165, and 179); and
wherein the terminal configured to display information on the basis of a processing result of the server (see at least Barney: ¶ 41-43 “output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations”; see also Barney: ¶ 91 and 164),
wherein the storage comprises at least data of specifications, drawings, and value information of a plurality of applications (see at least Barney: ¶ 69 “patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent”; see also Barney: ¶ 71, 85, 93, and 137-138),
wherein the drawings comprise support drawings corresponding to scopes of claims (see at least Barney: ¶ 37 and 71 “The total point score for each claim could then be taken as an indication of its relative breadth or narrowness based on the total number and statistical prevalence of each of the words contained in the claim”; see also Barney: ¶ 135 and 137),
wherein the server calculates a first degree of similarity between a specification of the designated application and each of the specifications of the plurality of applications (see at least Barney: ¶ 38 “For example, fruitful comparisons may also be made between litigated patents (presumably the most valuable patents) and non-litigated patents; or between high-royalty-bearing patents and low-royalty-bearing patents; or between high-cost-basis patents and low-cost-basis patents; or between published patent applications and issued patents”; see also Barney: ¶ 161 “The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 180),
wherein the first degree of similarity is obtained by digitizing each text of specification
Examiner notes that the Barney reference does not designate the metric and scores calculated as similarity scores yet define them as a direct comparison to patent application and patent data populations of similar patents in the same general field and wherein the first degree of similarity is obtained by digitizing each text of specification (see at least Barney: ¶ 71).
However, Jessen, which talks about an analysis of intellectual property data relation to products and services, teaches it is known to determine a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored digital representations of product descriptions, images, and textual descriptions (see at least Jessen: ¶ 69 “a textual description of the invention, or even a digital image of the invention may be input into the concept search engine 120 so that the textual description and/or image may be searched through the various databases 118, the protocols, and the Internet”; see also Jessen: ¶ 76 “image search engine may recognize and/or match the provided image using conventional image recognition and/or matching algorithms to determine a design, shape or pattern and/or concept in the image. Based on the determined design, shape or pattern and/or concept, the image search engine 110 may identify one or more design patents and/or design patent applications (or registered trademarks, or pending trademark applications) that include this determined design, shape or pattern and/or concept that is the same as or similar to the pictorial or graphical image”; see also Jessen: ¶ 78, 80-82, 84-85, and 198-201).
Jessen further teaches determining a first and second similarity scores (see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233).
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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored digital representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description. One of ordinary skill in the art would have been motivated to apply the known technique of labeling comparison scores as similarity scores because it would identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description (see Jessen ¶ 201).
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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored digital representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to analyze intellectual-property data and generate useful information from an organization's intellectual-property data, 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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored digital representations of product descriptions, images, and textual descriptions to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description). See also MPEP § 2143(I)(D).
The combination of Barney and Jessen teach:
wherein the server extracts a plurality of first similar applications from the plurality of applications on the basis of the first degree of similarity (see at least Barney: ¶ 39 “the invention provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree”; see also Barney: ¶ 41-42, 44-45, 47-48, 69-70, and 72; see also Barney: ¶ 138 “A relatedness index metric could also be developed and used to compare the relatedness or apparent relatedness of one or more patent specifications.” and 164-165; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233),
wherein the server calculates a second degree of similarity between the support drawings of at least one of the plurality of first similar applications and at least one of drawings of the designated application, respectively (see at least Barney: ¶ 39 “the invention provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree”; see also Barney: ¶ 41-42, 44-45, 47-48, 69-70, and 72; see also Barney: ¶ 138 “A relatedness index metric could also be developed and used to compare the relatedness or apparent relatedness of one or more patent specifications.” and 164-165; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233),
wherein the second degree of similarity is processed by at least one of image processing (see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233);
wherein the server extracts at least one similar drawing from the drawings of the designated application on the basis of the second degree of similarity (see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233), and
wherein for at least one of the plurality of first similar applications, the terminal displays the value information, the support drawing, and the similar drawing (see at least Barney: ¶ 39 “the invention provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree”; see also Barney: ¶ 41-42, 44-45, 47-48, 69-70, and 72; see also Barney: ¶ 138 “A relatedness index metric could also be developed and used to compare the relatedness or apparent relatedness of one or more patent specifications.” and 164-165; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233).
Referring to Claim 2, the combination of Barney and Jessen teaches the information retrieval system according to claim 1, including wherein the terminal displays the second degree of similarity between the support drawing of at least one of the plurality of first similar applications and the similar drawing (see at least Barney: ¶ 39 “the invention provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree”; see also Barney: ¶ 41-42, 44-45, 47-48, 69-70, and 72; see also Barney: ¶ 138 “A relatedness index metric could also be developed and used to compare the relatedness or apparent relatedness of one or more patent specifications.” and 164-165; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233).
Referring to Claim 3 and 10 (substantially similar in scope and language), Barney discloses an information retrieval system and method (see at least Barney: Abstract) comprising:
a terminal configured to receive a designated application; a server configured to perform processing using a storage (see at least Barney: ¶ 34-35 “identifying and comparing various characteristics of each individual patent to a statistically determined distribution of the same characteristics within a given patent population”; see also Barney: ¶ 40-41 “method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated”; see also Barney: ¶ 44 “individual selected patent in accordance with its overall patent quality ranking”; see also Barney: ¶ 47-50, 68, 72-74, and 82); and
wherein the terminal is configured to output information on the basis of a processing result of the server (see at least Barney: ¶ 41-43 “output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations”; see also Barney: ¶ 91 and 164),
wherein the server comprises at least data of specifications, drawings, and value information of a plurality of applications (see at least Barney: ¶ 69 “patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent”; see also Barney: ¶ 71, 85, 93, and 137-138),
wherein the drawings comprise support drawings corresponding to scopes of claims (see at least Barney: ¶ 37 and 71 “The total point score for each claim could then be taken as an indication of its relative breadth or narrowness based on the total number and statistical prevalence of each of the words contained in the claim”; see also Barney: ¶ 135 and 137),
wherein the server calculates a first degree of similarity between a specification of the designated application and each of the specifications of the plurality of applications (see at least Barney: ¶ 38 “For example, fruitful comparisons may also be made between litigated patents (presumably the most valuable patents) and non-litigated patents; or between high-royalty-bearing patents and low-royalty-bearing patents; or between high-cost-basis patents and low-cost-basis patents; or between published patent applications and issued patents”; see also Barney: ¶ 161 “The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 180).
wherein the first degree of similarity is obtained by digitizing each text of specification
Examiner notes that the Barney reference does not designate the metric and scores calculated as similarity scores yet define them as a direct comparison to patent application and patent data populations of similar patents in the same general field and wherein the first degree of similarity is obtained by digitizing each text of specification (see at least Barney: ¶ 71).
However, Jessen, which talks about an analysis of intellectual property data relation to products and services, teaches it is known to determine a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored digital representations of product descriptions, images, and textual descriptions (see at least Jessen: ¶ 69 “a textual description of the invention, or even a digital image of the invention may be input into the concept search engine 120 so that the textual description and/or image may be searched through the various databases 118, the protocols, and the Internet”; see also Jessen: ¶ 76 “image search engine may recognize and/or match the provided image using conventional image recognition and/or matching algorithms to determine a design, shape or pattern and/or concept in the image. Based on the determined design, shape or pattern and/or concept, the image search engine 110 may identify one or more design patents and/or design patent applications (or registered trademarks, or pending trademark applications) that include this determined design, shape or pattern and/or concept that is the same as or similar to the pictorial or graphical image”; see also Jessen: ¶ 78, 80-82, 84-85, and 198-201).
Jessen further teaches determining a first and second similarity scores (see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233).
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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored digital representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description. One of ordinary skill in the art would have been motivated to apply the known technique of labeling comparison scores as similarity scores because it would identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description (see Jessen ¶ 201).
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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored digital representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to analyze intellectual-property data and generate useful information from an organization's intellectual-property data, 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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored digital representations of product descriptions, images, and textual descriptions to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description). See also MPEP § 2143(I)(D).
The combination of Barney and Jessen teach:
wherein the server extracts a plurality of first similar applications from the plurality of applications on the basis of the first degree of similarity (see at least Barney: ¶ 39 “the invention provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree”; see also Barney: ¶ 41-42, 44-45, 47-48, 69-70, and 72; see also Barney: ¶ 138 “A relatedness index metric could also be developed and used to compare the relatedness or apparent relatedness of one or more patent specifications.” and 164-165; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233),
wherein the server calculates a second degree of similarity between the support drawings of each of the plurality of first similar applications and at least one of drawings of the designated application, respectively (see at least Barney: ¶ 39 “the invention provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree”; see also Barney: ¶ 41-42, 44-45, 47-48, 69-70, and 72; see also Barney: ¶ 138 “A relatedness index metric could also be developed and used to compare the relatedness or apparent relatedness of one or more patent specifications.” and 164-165; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233),
wherein the second degree of similarity is processed by at least one of image processing (see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233);
wherein the server extracts at least one second similar application by performing one or both of narrowing down and rearrangement of the plurality of first similar applications on the basis of the second degree of similarity (see at least Barney: ¶ 35 “During and/or following each such test the algorithm is refined by adjusting the scorings and/or weightings until the predictive accuracy of the algorithm is optimized”; see also Barney: ¶ 39, 41, 42, 48, 74, and 118-121; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233),
wherein the server extracts at least one similar drawing from the drawings of the designated application on the basis of the second degree of similarity (see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233), and
wherein for the second similar application, the terminal displays the value information, the support drawing, and the similar drawing (see at least Barney: ¶ 39 “the invention provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree”; see also Barney: ¶ 41-42, 44-45, 47-48, 69-70, and 72; see also Barney: ¶ 138 “A relatedness index metric could also be developed and used to compare the relatedness or apparent relatedness of one or more patent specifications.” and 164-165; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233).
Referring to Claim 4, the combination of Barney and Jessen teaches the information retrieval system according to claim 1, including wherein the server displays the second degree of similarity between the support drawing of the second similar application and the similar drawing (see at least Barney: ¶ 34-35 “identifying and comparing various characteristics of each individual patent to a statistically determined distribution of the same characteristics within a given patent population”; see also Barney: ¶ 40-41 “method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated”; see also Barney: ¶ 44 “individual selected patent in accordance with its overall patent quality ranking”; see also Barney: ¶ 47-50, 68, 72-74, and 82; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233).
Referring to Claim 5, the combination of Barney and Jessen teaches the information retrieval system according to claim 1, including wherein the value information comprises related product information (see at least Barney: ¶ 44; (see at least Barney: ¶ 34-35 “identifying and comparing various characteristics of each individual patent to a statistically determined distribution of the same characteristics within a given patent population”; see also Barney: ¶ 40-41 “method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated”; see also Barney: ¶ 44 “individual selected patent in accordance with its overall patent quality ranking”; see also Barney: ¶ 47-50, 68, 72-74, and 82; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233).
Referring to Claim 6, the combination of Barney and Jessen teaches the information retrieval system according to claim 1, including wherein the storage is configured to store the processing result (see at least Barney: ¶ 69 “patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent”; see also Barney: ¶ 71, 85, 93, and 137-138).
Referring to Claim 7, the combination of Barney and Jessen teaches the information retrieval system according to claim 1, including wherein the designated application is an application pending in the Patent Office (see at least Barney: ¶ 45).
Referring to Claim 9, the combination of Barney and Jessen teaches the information retrieval method according to claim 8, including wherein the second degree of similarity between the support drawing of at least one of the plurality of first similar applications and the similar drawing is displayed (see at least Barney: ¶ 44; (see at least Barney: ¶ 34-35 “identifying and comparing various characteristics of each individual patent to a statistically determined distribution of the same characteristics within a given patent population”; see also Barney: ¶ 40-41 “method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated”; see also Barney: ¶ 44 “individual selected patent in accordance with its overall patent quality ranking”; see also Barney: ¶ 47-50, 68, 72-74, and 82; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233).
Referring to Claim 11, the combination of Barney and Jessen teaches the information retrieval method according to claim 10, including wherein the second degree of similarity between the support drawing of the second similar application and the similar drawing is output (see at least Barney: ¶ 44; (see at least Barney: ¶ 34-35 “identifying and comparing various characteristics of each individual patent to a statistically determined distribution of the same characteristics within a given patent population”; see also Barney: ¶ 40-41 “method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated”; see also Barney: ¶ 44 “individual selected patent in accordance with its overall patent quality ranking”; see also Barney: ¶ 47-50, 68, 72-74, and 82; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233).
Referring to Claim 12, the combination of Barney and Jessen teaches the information retrieval method according to claim 8, including wherein the value information comprises related product information (see at least Barney: ¶ 44; (see at least Barney: ¶ 34-35 “identifying and comparing various characteristics of each individual patent to a statistically determined distribution of the same characteristics within a given patent population”; see also Barney: ¶ 40-41 “method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated”; see also Barney: ¶ 44 “individual selected patent in accordance with its overall patent quality ranking”; see also Barney: ¶ 47-50, 68, 72-74, and 82; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233).
Referring to Claim 13, the combination of Barney and Jessen teaches the information retrieval method according to claim 8, including wherein the designated application is an application pending in the Patent Office (see at least Barney: ¶ 45).
Referring to Claim 14, the combination of Barney and Jessen teaches the information retrieval system according to claim 3, including wherein the value information comprises related product information (see at least Barney: ¶ 44; (see at least Barney: ¶ 34-35 “identifying and comparing various characteristics of each individual patent to a statistically determined distribution of the same characteristics within a given patent population”; see also Barney: ¶ 40-41 “method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated”; see also Barney: ¶ 44 “individual selected patent in accordance with its overall patent quality ranking”; see also Barney: ¶ 47-50, 68, 72-74, and 82; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233).
Referring to Claim 15, the combination of Barney and Jessen teaches the information retrieval system according to claim 3, including wherein the storage configured to store the processing result (see at least Barney: ¶ 69 “patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent”; see also Barney: ¶ 71, 85, 93, and 137-138).
Referring to Claim 16, the combination of Barney and Jessen teaches the information retrieval system according to claim 3, including wherein the designated application is an application pending in the Patent Office (see at least Barney: ¶ 45).
Referring to Claim 17, the combination of Barney and Jessen teaches the information retrieval method according to claim 10, including wherein the value information comprises related product information (see at least Barney: ¶ 44; (see at least Barney: ¶ 34-35 “identifying and comparing various characteristics of each individual patent to a statistically determined distribution of the same characteristics within a given patent population”; see also Barney: ¶ 40-41 “method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated”; see also Barney: ¶ 44 “individual selected patent in accordance with its overall patent quality ranking”; see also Barney: ¶ 47-50, 68, 72-74, and 82; see at least Jessen: ¶ 62-65, and 67 “The Search Engine 110 may store a predetermined similarity score threshold against which it may compare the candidate image's similarity score. The Search Engine 110 may provide a user 106 notice/alert if the predetermined similarity score threshold is matched or surpassed. Further, the Search Engine 110 may store more than one predetermined threshold score. In some instances, a first action may be taken if a candidate image's score surpasses a first similarity score threshold and, additionally, take a second action if a candidate image's score surpasses a second similarity score threshold”; see also Jessen: ¶ 70, 86-87, 110, 231, and 233).
Referring to Claim 18, the combination of Barney and Jessen teaches the information retrieval method according to claim 10, including wherein the designated application is an application pending in the Patent Office (see at least Barney: ¶ 45).
19. (New) The information retrieval system according to claim 1; Barney fails to explicitly state wherein the first degree of similarity is obtained by vectorizing each text of specification.
However, Jessen teaches wherein the first degree of similarity is obtained by vectorizing each text of specification (see at least Jessen: ¶ 100 and 167).
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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description. One of ordinary skill in the art would have been motivated to apply the known technique of determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions because it would identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description (see Jessen ¶ 201).
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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to analyze intellectual-property data and generate useful information from an organization's intellectual-property data, 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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description). See also MPEP § 2143(I)(D).
20. (New) The information retrieval system according to claim 1; Barney fails to explicitly state wherein the second degree of similarity is calculated using a convolutional neural network.
However, Jessen teaches wherein the second degree of similarity is calculated using a convolutional neural network (see at least Jessen: ¶ 52).
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 convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description. One of ordinary skill in the art would have been motivated to apply the known technique of using a convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions because it would identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description (see Jessen ¶ 201).
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 convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to analyze intellectual-property data and generate useful information from an organization's intellectual-property data, 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 convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description). See also MPEP § 2143(I)(D).
21. (New) The information retrieval system according to claim 3; Barney fails to explicitly state wherein the first degree of similarity is obtained by vectorizing each text of specification.
However, Jessen teaches wherein the first degree of similarity is obtained by vectorizing each text of specification (see at least Jessen: ¶ 100 and 167).
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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description. One of ordinary skill in the art would have been motivated to apply the known technique of determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions because it would identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description (see Jessen ¶ 201).
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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to analyze intellectual-property data and generate useful information from an organization's intellectual-property data, 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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description). See also MPEP § 2143(I)(D).
22. (New) The information retrieval system according to claim 3; Barney fails to explicitly state wherein the second degree of similarity is calculated using a convolutional neural network.
However, Jessen teaches wherein the second degree of similarity is calculated using a convolutional neural network (see at least Jessen: ¶ 52).
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 convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description. One of ordinary skill in the art would have been motivated to apply the known technique of using a convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions because it would identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description (see Jessen ¶ 201).
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 convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to analyze intellectual-property data and generate useful information from an organization's intellectual-property data, 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 convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description). See also MPEP § 2143(I)(D).
23. (New) The information retrieval system according to claim 8; Barney fails to explicitly state wherein the first degree of similarity is obtained by vectorizing each text of specification.
However, Jessen teaches wherein the first degree of similarity is obtained by vectorizing each text of specification (see at least Jessen: ¶ 100 and 167).
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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description. One of ordinary skill in the art would have been motivated to apply the known technique of determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions because it would identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description (see Jessen ¶ 201).
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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to analyze intellectual-property data and generate useful information from an organization's intellectual-property data, 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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description). See also MPEP § 2143(I)(D).
24. (New) The information retrieval system according to claim 8; Barney fails to explicitly state wherein the second degree of similarity is calculated using a convolutional neural network.
However, Jessen teaches wherein the second degree of similarity is calculated using a convolutional neural network (see at least Jessen: ¶ 52).
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 convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description. One of ordinary skill in the art would have been motivated to apply the known technique of using a convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions because it would identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description (see Jessen ¶ 201).
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 convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to analyze intellectual-property data and generate useful information from an organization's intellectual-property data, 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 convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description). See also MPEP § 2143(I)(D).
25. (New) The information retrieval system according to claim 10; Barney fails to explicitly state wherein the first degree of similarity is obtained by vectorizing each text of specification.
However, Jessen teaches wherein the first degree of similarity is obtained by vectorizing each text of specification (see at least Jessen: ¶ 100 and 167).
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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description. One of ordinary skill in the art would have been motivated to apply the known technique of determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions because it would identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description (see Jessen ¶ 201).
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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to analyze intellectual-property data and generate useful information from an organization's intellectual-property data, 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 determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description). See also MPEP § 2143(I)(D).
26. (New) The information retrieval system according to claim 10; Barney fails to explicitly state wherein the second degree of similarity is calculated using a convolutional neural network.
However, Jessen teaches wherein the second degree of similarity is calculated using a convolutional neural network (see at least Jessen: ¶ 52).
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 convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description. One of ordinary skill in the art would have been motivated to apply the known technique of using a convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions because it would identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description (see Jessen ¶ 201).
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 convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions (as disclosed by Jessen) to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information (as disclosed by Barney) to analyze intellectual-property data and generate useful information from an organization's intellectual-property data, 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 convolutional neural network for determining a plurality of metrics related to patent applications and intellectual property assets wherein the metric is defined as a plurality of similarity metrics based on processing stored vector representations of product descriptions, images, and textual descriptions to the known method and system for processing intellectual property information to determine metrics of patent data compared to stored patent information to identify one or more patent documents (e.g., issued patents and/or published patent applications) that include the same or substantially similar concepts as determined in the textual description or are relevant to the one or more determined concepts of the textual description). See also MPEP § 2143(I)(D).
Response to Arguments
101 Rejections
Applicant's arguments filed with respect to the rejection of the claims have been considered but they are not persuasive. The arguments merely state that the claims are not directed to an abstract idea and the rejection should be withdrawn. Examiner respectfully disagrees for the reasons and rationales explained above. The claims stand rejected.
103 Rejection
Applicant’s arguments with respect to claim(s) being rejected under 35 USC 103 have been considered in combination with the submitted amendments has required further search and consideration and the rejection has been updated to reflect the submitted amendments.
The claims stand rejected.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Michael Young/Examiner, Art Unit 3626