Ex Parte Turner et alDownload PDFPatent Trial and Appeal BoardNov 25, 201411522177 (P.T.A.B. Nov. 25, 2014) Copy Citation UNITED STATES PATENT AND TRADEMARK OFFICE UNITED STATES DEPARTMENT OF COMMERCE United States Patent and Trademark Office Address: COMMISSIONER FOR PATENTS P.O. Box 1450 Alexandria, Virginia 22313-1450 www.uspto.gov APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO. 11/522,177 09/15/2006 Alan E. Turner 15268-E (BA4-304) 3929 21567 7590 11/25/2014 Wells St. John P.S. 601 West First Avenue Suite 1300 Spokane, WA 99201-3828 EXAMINER AMIN, MUSTAFA A ART UNIT PAPER NUMBER 2176 MAIL DATE DELIVERY MODE 11/25/2014 PAPER Please find below and/or attached an Office communication concerning this application or proceeding. The time period for reply, if any, is set in the attached communication. PTOL-90A (Rev. 04/07) UNITED STATES PATENT AND TRADEMARK OFFICE ____________ BEFORE THE PATENT TRIAL AND APPEAL BOARD ____________ Ex parte ALAN E. TURNER, ELIZABETH G. HETZLER, and GRANT C. NAKAMURA ____________ Appeal 2012-002280 Application1 11/522,177 Technology Center 2100 ____________ Before ERIC B. GRIMES, LORA M. GREEN, and ULRIKE W. JENKS, Administrative Patent Judges. JENKS, Administrative Patent Judge. DECISION ON APPEAL This is an appeal under 35 U.S.C. § 134 involving claims directed to a device and method for text analysis. The Examiner has rejected the claims as anticipated and obvious. We have jurisdiction under 35 U.S.C. § 6(b). We affirm-in-part. STATEMENT OF THE CASE Claims 11–14, 16–23, 25, 29, 30, 32, 34–36, and 41–45 are on appeal,2 and can be found in the Claims Appendix of the Appeal Brief (see 1 Appellants identify Battelle Memorial Institute as the Real Party in Interest. (App. Br. 1.) Appeal 2012-002280 Application 11/522,177 2 also App. Br. 1). Claims 11, 18, and 32 are representative of the claims on appeal, and read as follows: 11. An article of manufacture comprising: a computer readable storage medium configured to store programming configured to cause processing circuitry to perform processing comprising: accessing a first representation of an initial text item; accessing a second representation of the initial text item, wherein the second representation is different than the first representation; combining the first and second representations of the initial text item to form a third representation of the initial text item which is different than the first and second representations; using the third representation, processing the initial text item with respect to another text item; and wherein the combining comprises initial combining, and wherein the programming is configured to cause the processing circuitry to perform processing comprising subsequent combining of the first and second representations of the initial text item to form a fourth representation of the initial text item which is different than the first, second and third representations. 18. A text analysis method comprising: using processing circuitry, generating a first representation of a text item using a first measurement basis; using the processing circuitry, generating a second representation of the text item using a second measurement basis different than the first measurement basis, wherein the second representation is different than the first representation; using the processing circuitry, analyzing the text item using the first representation and the second representation; and 2 The Examiner has withdrawn the rejection of claims 1–10 and 38–40 under 35 U.S.C § 103(a) over Calistri-Yeh in view of Ukrainczyk, as well as the rejection of claims 38 and 40–45 under 35 U.S.C. § 112, first paragraph, and the rejection of claim 11 under 35 U.S.C. § 112, second paragraph (Ans. 4). Appeal 2012-002280 Application 11/522,177 3 using the processing circuitry, steering the generating of at least one of the first and second representations according to a perspective of interest of a user. 32. A text analysis method comprising: using processing circuitry, accessing a first representation of a text item; using the processing circuitry, accessing a second representation of the text item; using the processing circuitry, accessing a blend constant; using the processing circuitry, weighting at least one of the first and second representations using the accessed blend constant; after the weighting and using the processing circuitry, providing a third representation of the text item using the first and second representations which were weighted using the accessed blend constant; using the processing circuitry, changing the blend constant; using the processing circuitry, reweighting the at least one of the first and second representations using the blend constant after the providing the third representation and after the changing; and using the processing circuitry, providing a fourth representation of the text item using the first and second representations after the reweighting. The following grounds of rejection are before us for review: I. claims 11–14, 16, 17, 32, 35, and 36 under 35 U.S.C § 102(b) as anticipated by Calistri-Yeh3 (Ans. 5); II. claims 18–23, 25, 29, 30, and 41–45 under 35 U.S.C § 103(a) as obvious over Calistri-Yeh in view of Ukrainczyk4 (Ans. 17); and 3 Calistri-Yeh et al., US 2004/0199505 A1, published Oct. 7, 2004. Appeal 2012-002280 Application 11/522,177 4 III. claim 34 under 35 U.S.C. § 103(a) as obvious over Calistri-Yeh in view of Fox5 (Ans. 36). I. The Issue: Anticipation by Calistri-Yeh The Examiner finds that Calistri-Yeh meets all claim limitations (see Ans. 5–9, 38–39). Appellants contend that the trainable semantic vectors (TSV) as disclosed in Calistri-Yeh are not determined multiple times using the same data point as claimed. Specifically, “[t]here are no teachings that a first TSV is calculated using the formula of paragraph 0055 a plurality of times for a given data point, and to the contrary, paragraph 0057 discloses that the TSV formula is utilized to calculate a single TSV value for each data point.” (Reply Br. 3.) Does the preponderance of the evidence of record support the Examiner’s finding that Calistri-Yeh anticipates the claims? Findings of Fact 1. Calistri-Yeh disclosed “producing a semantic representation of information in a semantic space” (Calistri-Yeh Abstract). The construction of trainable semantic vectors (TSV) allows a visual representation of this information. Fig. 2 is reproduced below: 4 Ukrainczyk et al., US 2006/0143175 A1, published June 29, 2006. 5 Fox et al., US 6,574,632 B2, issued June 3, 2003. App App Fig 2 step is ind categ deter form entri acco eal 2012-0 lication 11 shows th S210, a da icative of ories.” (C 2. C mine the s ula: TSV= 3. “A es (i.e. the mplished b 02280 /522,177 e steps inv ta table is a relation alistri-Ye alistri-Yeh ignificanc α(v)+(l-α) t step S21 data point y first cal olved in th constructe ship betwe h 4: ¶ 46.) disclosed e of data p (u)” (Cal 2 [shown s) in the d culating th 5 e construc d. The da en data po applying w oints, acc istri-Yeh 5 above Fig ata table is e percenta tion of a s ta table sto ints and p eighting ording to t : ¶¶ 55–56 . 2], the si determin ge of data emantic d res inform redetermin factor α “t he followi ; Ans. 14. gnificance ed. . . . Th points occ ataset. “A ation that ed o ng ) of the is can be urring in t App App each 51; A of a Ans. asso cons each categ poin word “FIG stren 110; TSV eal 2012-0 lication 11 category” ns. 14.) 4. “V data point 14.) “Bot ciated with 5. “A tructed. T entry, or d ories). . . t in the dat 6. C in a given . 9 illustr gth of eac Ans. 40.) 7. Fi is “calcul 02280 /522,177 and is rep ” in the T ’s occurren h u and v a particul t step S21 he first TS ata point, . Accordin a table.” ( alistri-Yeh category ates a table h word wi g. 10 of C ated based resented a SV formu ce across represent t ar categor 4 [shown V corresp across the gly, a firs Calistri-Y disclosed . Fig. 9 is 230 that th respect alistri-Yeh on the act 6 s “u” in th la represen all categor he strengt y.” (Calis above in F onds to a s semantic t TSV mu eh 5: ¶ 57 a table of reproduce stores the v to the cate , reproduc ual values e formula. ts “the pro ies.” (Cal h with wh tri-Yeh 5: ig. 2], a fi emantic re space (i.e. st be const ; Ans. 14.) stored val d below: alues that gories.” ( ed below, stored in (Calistri- bability d istri-Yeh 5 ich an entr ¶ 53; Ans rst TSV is presentati , the prede ructed for ues associ indicate t Calistri-Ye shows tha table 230. Yeh 5: ¶ istribution : ¶ 52; y is . 14.) on for termined each data ated with a he relative h 10: ¶ t each App App Acco repre “FIG the w the s Mor α(v)+ of a Fig. docu 11: ¶ recei eal 2012-0 lication 11 rdingly, th sentation . 10, a tab ords . . . . emantic re e particula (l-α)(u).” 8. Fi plurality o 11 shows ments wou 114.) 9. C ved they “ 02280 /522,177 e TSVs sh of the exem le 250 is s Table 25 presentati rly, the fol (Calistri- g. 11 of C f words de “a two-dim ld be clus alistri-Yeh must be cl own in ta plary wo hown for 0 is a com on of each lowing for Yeh 10: ¶ alistri-Yeh fined by o ensional c tered in th disclosed assified w 7 ble 250 co rds” (Cali illustrating bination of word acro mula is us 112.) , reproduc nly two ca oordinate e same ma that when ithin the p rrespond t stri-Yeh 10 the sema five TSV ss the sem ed to calc ed below, tegories. for each w nner as w additiona reviously o the actu : ¶ 113). ntic repres s that corr antic spac ulate the v shows the ord. Not ords.” (Ca l items (w defined ca al entation o espond to e. . . . alues. clustering e that listri-Yeh ords) are tegories.” f Appeal 2012-002280 Application 11/522,177 8 (Calistri-Yeh 8: ¶ 86.) After newly received data items are placed into categories this eventually leads to “a process wherein the nature of each category is reevaluated. This iterative approach enables the classification algorithm to adapt to changes in data and definition over time.” (Calistri- Yeh 8: ¶ 87.) “TSVs are optionally reconstructed. More particularly, the reconstructed category TSVs are recalculated according to the method described earlier to represent the semantic dimension across the space of the original sample items within that category as well as the newly added items within that category.” (Calistri-Yeh 8: ¶ 88.) Principle of Law “A single prior art reference that discloses, either expressly or inherently, each limitation of a claim invalidates that claim by anticipation.” Perricone v. Medicis Pharm. Corp., 432 F.3d 1368, 1375 (Fed. Cir. 2005). Analysis Claim 11 Calistri-Yeh disclosed the use of trainable semantic vectors to display relational information about data sets (FF 1–9). The process involves the production of data tables for storing relational information about data points in a particular category (FF 1, 6–8). The number of data points in a given category provides information about the importance of each data point within that particular category (FF 3–4). A data point can also fall into more than one category and the distribution of a data point across the various categories is also given a value (FF 4). “[A] first TSV must be constructed for each data point in the data table” (FF 5, 7). “Through empirical evidence Appeal 2012-002280 Application 11/522,177 9 gathered from experimentation, . . . [it is] determined that the weighted average of the u and v vectors can produce superior results than achievable without the use of a weighting factor.” (Calistri-Yeh 5: ¶ 56.) Calistri-Yeh disclosed the TSV is calculated with the formula TSV=α(v)+(l-α)(u), where α is a weighting factor (FF 2). Construction of the first TSV requires the input of values “u” and “v” (FF 1–8). We find that claim 11 recites a first, second, third, and fourth representation of an initial text item. The claim also requires that these representations are different from each other. The claim, however, is silent with respect to showing these representations simultaneously on a display, or even that the information about the four representations are simultaneously accessible in a table. Appellants contend that “allowing the user to select the value of the weighting factor which is used in the TSV formula fails to disclose that the TSV formula is calculated a plurality of times using different values of the weighting factor to provide different TSV values for the same data point.” (App. Br. 21; see also Reply Br. 4.) We are not persuaded by Appellants’ contentions. The Examiner identifies “u” as being a first representation of an initial text item, and “v” as being “a second representation of the initial text item” (Ans. 18). The Examiner identifies the combination of “u and v without a[ ] weighting factor” to form the third representation of an initial text item (Ans. 38 (emphasis added)). “Calistri-Yeh explicitly discloses that from experimentation, the weighted average of u and v vectors provide superior results than without the use of the weighting factor (i.e. for a certain data point, a first TSV was calculated without weighting a factor and for the Appeal 2012-002280 Application 11/522,177 10 same data point, a second TSV was calculated with the weighting factor)” (Ans. 38). Finally, according to the Examiner, the combination of “u and v with a[ ] weighting factor” is identified as the fourth representation (Ans. 38–39 (emphasis added)). The Examiner’s position for establishing that the initial values “u” and “v” are used multiple times to construct a TSV comes from Calistri-Yeh’s explanation that the superiority of the weighting factor was empirically determined. “Through empirical evidence gathered from experimentation, the inventors [Calistri-Yeh] have determined that the weighted average of the u and v vectors can produce superior results than achievable without the use of a weighting factor.” (Calistri-Yeh 5: ¶ 56.) We agree with the Examiner’s position that in order to compare the results with and without the use of a weighting factor, necessarily requires the use the same initial data points “u” and “v.” Thus, we agree with the Examiner’s finding that Calistri-Yeh disclosed a first and second representation as well as a third and fourth representation that are formed by combining the same first and second representations either with or without a weighting factor. Accordingly, we find that the Examiner has adequately explained how Calistri-Yeh, within the meaning of 35 U.S.C. § 102(b), describes a third and fourth representation derived from the same initial text item as required by claim 11, we affirm the Examiner’s anticipation rejection of claim 11. Appellants do not separately argue dependent claims 12–14, 16, and 17. (App. Br. 19–22.) Therefore, these claims fall with claim 11. 37 C.F.R. § 41.37(c)(1). Appeal 2012-002280 Application 11/522,177 11 Claim 32 Appellants contend that claim 32 recites “changing the blend constant” as well as “reweighting” of the first and second representations, while Calistri-Yeh merely discloses at paragraph 0056 that a user may select the alpha value which is used during the single execution of the TSV formula to provide the single TSV for the data item per paragraph 0057. Appellants contend that Calistri-Yeh fails to provide any teachings that multiple TSVs are calculated for a given text item using multiple alpha values and to the contrary merely discloses that a single TSV is calculated for an individual data item. (App. Br. 27–28.) We are not persuaded by Appellants’ contention. Calistri-Yeh disclosed that there are instances in which additional data is added to categories in a table requiring a recalculation of the TSV items (FF 9). The TSV is calculated based on a formula requiring a variable weighting factor, and as recognized by Calistri-Yeh the use of the weighting factor produced superior results (FF 2, 9; Calistri-Yeh 5: ¶ 56). “The variable α is a weighting factor that can be determined based on the information being represented and analyzed. . . . Other values [for the weighting factor] can be selected depending on various factors such as the type and quantity of information, or the level of detail necessary to represent the information.” (Calistri-Yeh 5: ¶ 56; see also FF 1, 6–9). Most importantly, Calistri-Yeh provides that the addition of additional data to a table requires the recalculation of the TSV (FF 9). We agree with the Examiner that “by simply changing ‘alpha’ value, one can create infinitely many vectors (e.g. TSVs) that are different from each other depending on the level detail Appeal 2012-002280 Application 11/522,177 12 desired at a first point in time and a different level of detail at a different time.” (Ans. 42.) Accordingly, we find that the Examiner has adequately explained how Calistri-Yeh, within the meaning of 35 U.S.C. § 102(b), discloses a third and fourth representation using different weighting factors as required by claim 32, and we affirm the Examiner’s anticipation rejection of claim 32. Appellants do not separately argue dependent claims 35 and 36. (App. Br. 26–29.) Therefore, these claims fall with claim 32. 37 C.F.R. § 41.37(c)(1). II. The Issue: Obviousness over Calistri-Yeh and Ukrainczyk The Examiner finds that Calistri-Yeh teaches most of the recited claim limitations of claim 18 (see Ans. 17–19). The Examiner, however, acknowledges that Calistri-Yeh “fails to expressly disclose - and using the processing circuitry, steering the generating of at least of the first and second representations according to a perspective of interest of a user.” (Id. at 19) (emphasis omitted.) The Examiner relies on Ukrainczyk for this teaching, and concludes that Ukrainczyk “‘provide[s] an effective method for classifying text in which user knowledge may be used’, in other words Ukrainczyk improves upon the teaching of Calistri-Yeh by incorporating user input (i.e. word list and concepts) in order to improve classification of texts or documents.” (Ans. 40–41.) Appellants contend that the combination of references fails to suggest “weighting of values from plural vectors of the same text item which are generated according to different perspectives of interest of a user and combining the weighted values of the first and second vectors as positively- claimed.” (App. Br. 15.) Appellants contend that the combination fails to Appeal 2012-002280 Application 11/522,177 13 suggest “the weighted values of one of these vectors with the weighted values of the other of the vectors to produce a third vector as explicitly claimed.” (App. Br. 16.) Does the preponderance of evidence of record support the Examiner’s conclusion that the combination of Calistri-Yeh and Ukrainczyk teaches “generating of at least one of the first and second representations according to a perspective of interest of a user”? Findings of Fact 10. Ukrainczyk disclosed a method: “for automatically classifying text into categories. . . . A weight is then coupled to each feature, wherein the weight indicates a degree of association between the feature and the category.” (Ukrainczyk Abstract.) “The REE table 50 consists of user- inputted, concept evidence vector edits. Concept evidence vector edits are user modified feature vectors 100 with associated flags and weights. The information in REE table 50 is used to modify feature vectors 100 in topic spotter matrix 90. (Ukrainczyk 3: ¶ 29; Ans. 19.) 11. Ukrainczyk disclosed: Weight—the strength of the association between a term (human and learned) and a concept node (manually or automatically specified). In the case of human evidence, the weight can be assigned by the system or it can be assigned by a person. . . . . A user may assign the weights such that a feature contributes to the decision to assign a document to a particular concept node. . . . In the event a user does not specify the weight, it will be assigned based on the feature’s distribution in the training data (possibly modulated by its prior probability of occurrence in the language). (Ukrainczyk 6: ¶ 52; Ans. 19.) Appeal 2012-002280 Application 11/522,177 14 Principle of Law “The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.” KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007). As expressly recognized in KSR, any art recognized need or problem can provide a reason for combining claim elements. Id. Analysis Claim 18 Appellants contend that Calistri-Yeh fails to disclose “a measurement basis let alone plural acts of generating first and second representations of the same text item using first and second measurement bases as claimed.” (App. Br. 22; see also Reply Br. 5.) We are not persuaded. Claim 18 requires “a first representation of a text item using a first measurement basis” as well as “a second representation of the text item using a second measurement basis.” The Examiner interprets the “measurement basis” to be the formula that produces values “u” and “v” in table 230 of Calistri-Yeh (Ans. 40; FF 3, 4, 6). [T]he formula for vector “u” can fairly be equated to “first measurement basis” that measures relative strength of each word with respect to categories. The results as shown in figure 9 (e.g. (u(w1), cat2) = 0.74) and can fairly be interpreted as “first representation” thus clearly Calistri-Yeh discloses “generating a first representative of a text item using a first measurement basis”. Furthermore the formula for vector “v” can fairly be equated to “second measurement basis” that measure the probability distribution of a data points occurrence across all categories and the results as shown in figure 9 (e.g. Appeal 2012-002280 Application 11/522,177 15 (v(w1), cat2) = .071) can fairly be interpreted as “second representation.” (Ans. 39–40.) “[D]uring examination proceedings, claims are given their broadest reasonable interpretation consistent with the specification.” In re Hyatt, 211 F.3d 1367, 1372 (Fed. Cir. 2000). The Specification provides that “measurement basis 58 may indicate a plurality of associations or relationships of individual ones of the measurement features with respect to the dimension anchors . . . . Rows of the array may be the measurement features and columns of the array may be the dimension anchors.” (Spec. 16: ¶ 49 (emphasis added).) The Specification, therefore, reasonably provides that the measurement basis can indicate a relationship based on multiple measurement features or based on a single measurement feature. We interpret that based on the Specification a “measurement basis” can reasonably be calculated from an individual (single) “measurement feature,” such as a word. We recognize, but are not persuaded by, Appellants’ contention that “u” and “v” of Calistri-Yeh are “both void of array teachings let alone teachings of arrays including measurement features, dimension anchors and association values which associate the two.” (Reply Br. 7.) As discussed above “measurement features,” in light of the Specification, can reasonably be interpreted to encompass a single feature. Thus, we find that the Examiner has reasonably interpreted word (w1) found in table 230 of Calistri-Yeh to be the measurement feature and that formulas resulting in the vectors “u” and “v” are reasonably interpreted to meet the “measurement basis” requirement of the claim (Ans. 40). Appeal 2012-002280 Application 11/522,177 16 We are also not persuaded by Appellants’ contention that the Examiner has failed to articulate a reason for making the combination, and that Calistri-Yeh teaches away from the combination (App. Br. 8–9). The Examiner explains that “Calistri-Yeh and Ukrainczyk both deal with clustering and/or organizing document by categories/similarities” (Ans. 40). “The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.” KSR, 550 U.S. at 416. Calistri-Yeh disclosed an automated method for organizing text (FF 1–9), while Ukrainczyk also disclosed a method for organizing text (FF 10–11). Ukrainczyk’s method provides that the organization can be either made automatically, supplemented manually by the user, or may utilize a combination to achieve the desired level of organization (FF 10– 11). Ukrainczyk acknowledges problems with the “prior art systems that utilize training data, most do not have the capability to interactively take advantage of human knowledge” (Ukrainczyk 1: ¶ 10). Thus, the inability to interact with users when organizing data and utilize their knowledge to improve data organization is an art recognized problem. As expressly recognized in KSR, any art recognized need or problem can provide a reason for combining claim elements. KSR, 550 U.S. at 416. Here, Ukrainczyk “provide[s] an effective method for classifying text in which user knowledge may be utilized very early in the construction of the statistical model” (Ukrainczyk 2: ¶ 11; FF 10–11). Accordingly, we find that the Examiner has met the burden of articulating a reason for making the combination based on Calistri-Yeh and Ukrainczyk. A reference is said to “teach away” from a claimed invention when it “suggests that the line of development flowing from the reference’s Appeal 2012-002280 Application 11/522,177 17 disclosure is unlikely to be productive of the result sought by the applicant.” In re Gurley, 27 F.3d 551, 553 (Fed. Cir. 1994). That is not the case here. If anything, these references merely illustrate the trade-offs that those of ordinary skill in this art recognize and routinely consider in designing computer based organizational applications. We find that the Examiner has adequately articulated how the combination of Calistri-Yeh and Ukrainczyk meets the limitation of the text analysis method as set out in claim 18. For the reasons set out above and those articulated by the Examiner in the Answer, we affirm the Examiner’s obviousness rejection of claim 18. Appellants do not separately argue claims 19–23, 25, 27–30, and 41. (App. Br. 22–26.) Therefore, these claims fall with claim 18. 37 C.F.R. § 41.37(c)(1). Claims 42 Appellants contend that the combination of Calistri-Yeh and Ukrainczyk does not disclose the limitation of “accessing at least one text pattern of interest to the user” as recited in claim 42 (App. Br. 33–34). We are not persuaded. Ukrainczyk describes that “[t]he method comprises the steps of manually or automatically associating a plurality of features with at least one category, wherein the plurality of features contribute to a decision to classify a document into said at least one category.” (Ukrainczyk 2: ¶ 13 (emphasis added.) As explained by the Examiner the recorded evidence edits (REE) table of Ukrainczyk contains user input and is used to modify the evidence vectors (Ans. 43–44; FF 10). We agree with the Examiner’s conclusion that the user input as disclosed in Appeal 2012-002280 Application 11/522,177 18 Ukrainczyk meets the limitation of providing a search based on the interest of the user. Claim 43 Appellants contend that the combination of Calistri-Yeh and Ukrainczyk does not meet the limitation of “associations of a plurality of measurement features” as recited in claim 43 (App. Br. 34–36). With respect to claim 43, we find that Appellants have the better position. Although we agree with the Examiner that Calistri-Yeh discloses a first and second measurement basis, we do not agree with the position that the first and second measurement basis as disclosed in Calistri-Yeh is based on “a plurality of measurement features.” In sum, we are not persuaded that the Examiner has adequately explained how Calistri-Yeh’s use of a single measurement feature in combination with Ukrainczyk teaches the limitation of a measurement basis based on a plurality of measurement features. Accordingly, we reverse the rejection of claim 43. Claim 44 Appellants contend that the combination of Calistri-Yeh and Ukrainczyk does not meet the limitation “generating the one of the first measurement basis and the second measurement basis wherein the at least one text pattern . . . comprises one of the measurement features and one of the dimension anchors” (App. Br. 36–37). We are not persuaded. As explained above (Claim 18) we agree with the Examiner that the combination of Calistri-Yeh and Ukrainczyk teaches utilizing at least one text pattern of interest to the user in order to organize Appeal 2012-002280 Application 11/522,177 19 texts (Ans. 47; FF 10–11). We also agree with the Examiner that Calistri- Yeh teaches generating a first and second measurement basis from a text (e.g., word) (FF 6–8). “As shown in figure 9, ‘measurement basis’ clearly include ‘measurement feature’ (e.g. word) and ‘dimension anchors’ (e.g. categories)” (Ans. 48). We conclude that the evidence cited by the Examiner supports a prima facie case of obviousness with respect to claim 44. III. The Issue: Obviousness over Calistri-Yeh and Fox The Examiner has rejected claim 34 based on the combination of Calistri-Yeh and Fox (Ans. 36–38). Appellants do not address the merits of this rejection in their Appeal Brief or Reply Brief. “When the appellant fails to contest a ground of rejection to the Board, . . . the Board may treat any argument with respect to that ground of rejection as waived.” Hyatt v. Dudas, 551 F.3d 1307, 1314 (Fed. Cir. 2008). SUMMARY We affirm the rejection of claims 11–14, 16, 17, 32, 35, and 36 under 35 U.S.C § 102(b) over by Calistri-Yeh. We affirm the rejection of claims 18–23, 25, 29, 30, 41, 42, 44, and 45 under 35 U.S.C § 103(a) over Calistri-Yeh in view of Ukrainczyk. We reverse the rejection of claim 43 under 35 U.S.C § 103(a) over Calistri-Yeh in view of Ukrainczyk. We affirm the rejection of 34 under 35 U.S.C. § 103(a) over Calistri- Yeh in view of Fox. Appeal 2012-002280 Application 11/522,177 20 Only those arguments actually made by Appellants have been considered in this decision. Arguments which Appellants could have made but chose not to make in the Brief have not been considered and are deemed to be waived. See 37 C.F.R. § 41.37(c)(1). TIME PERIOD FOR RESPONSE No time period for taking any subsequent action in connection with this appeal may be extended under 37 C.F.R. § 1.136(a). AFFIRMED-IN-PART cdc Copy with citationCopy as parenthetical citation